#managing Kubernetes clusters

[virtualizationhowto]

Best Kubernetes Management Tools in 2023

Best Kubernetes Management Tools in 2023 #homelab #vmwarecommunities #Kubernetesmanagementtools2023 #bestKubernetescommandlinetools #managingKubernetesclusters #Kubernetesdashboardinterfaces #kubernetesmanagementtools #Kubernetesdashboard

Kubernetes is everywhere these days. It is used in the enterprise and even in many home labs. It’s a skill that’s sought after, especially with today’s push for app modernization. Many tools help you manage things in Kubernetes, like clusters, pods, services, and apps. Here’s my list of the best Kubernetes management tools in 2023. Table of contentsWhat is Kubernetes?Understanding Kubernetes and…

[agapi-kalyptei]

How to be a senior developer, pt. 1

Since I'm making a presentation for work, i figured I might as well write it out.

In this part I'll explain my viewpoint, and point out to Shuhari, vertical slices, kata, and the Cynefin framework as helpful tools for figuring out where you are.

In next three parts I'll explain what I think it means to be a good junior, experienced, and senior developer.

About me and the purpose of this talk/article

I don't especially care to impress you and establish my credibility in detail. I'm not the wisest coolest fastest developer you've ever seen, but I've been programming for ~35 years and spent most of my adult life as a professional software developer and architect. I never sought leadership or management positions, but I've been involved in hiring, onboarding, documentation, etc.

The purpose of this is to give you something to think about, to gain some clarity about how to progress. This is not a technical tutorial or life hack or your therapy session.

Classic warning labels

I’m not your dad, it’s your life, I won't tell you what to do with your career.

This is not a criticism of any of you, and please don’t come at me with “this doesn’t apply to me actually”. I will likely say something like "senior dev should know this" and you might be a senior and not know it, it's fine. This is not an appraisal, I'm not your boss, your happiness doesn't depend on me.

And even when I use the labels "junior", "experienced" and "senior" developer, I see zero benefit in assigning you three rigid categories. We're all dumb in our own ways, we're all clever and wise in our own ways.

Let's begin.

Shuhari

https://en.wikipedia.org/wiki/Shuhari

Shu-ha-ri (守破離) is a way of viewing mastery of any skill as three stages. Instead of using the more typical western idea of having "experts" who are people who just Know a lot, it instead focuses on how you interact with the skill.

In very simplified terms, it's obeying the rules and respecting the tradition (Shu), then evolving the existing rules by breaking them bit by bit (Ha), and eventually detaching yourself from the usual wisdom and rules and just vibing (Ri).

A simple way to remember the Shuhari stages - follow the rules, break the rules, transcend the rules.

Another way to look at it is mimicking others (Shu), taking a step back and understanding context (Ha) and having a global perspective (Ri).

For example, I've made 1500-2000 pancakes over the past 13 years. I started by following the existing recipe and measures (Shu). I started trying different variations and ingredients from different recommendations (still Shu).

Eventually I started breaking the traditional recipes by adding ingredients that didn't seem expected (Ha) and improvising more.

I'm not confident I'd say I reached the Ri stage, because I still use the same basic ingredients since I have a relatively limited, desired outcome. I'd argue to really be in Ri level of mastery I'd have to have a MacGyver-like flexibility when it comes to ingredients.

At that's fine. Not everyone needs to be a guru.

The important thing is - someone at Ri level of making pancakes isn't just making Shu level pancakes very very fast.

A "Shu" developer repeats what they learned in school, copy pastes from Stack Overflow, follows advice of senior developers, makes simple CRUD REST endpoints.

A "Ha" developer can improve on existing tooling or workflow, remove more complex technical debt and knows when to have exceptions to common rules.

A "Ri" developer is someone who invents workflows, architecture, enterprise patterns, combines tech stack in creative ways, and doesn't necessarily follow hype.

It should be noted that in real world, developers don't have infinite freedom because of practical considerations - audits, legal requirements, ISO certifications, Jira, limitations in your employees' know-how, etc. I can't just develop something in COBOL and then deploy it outside of a Kubernetes cluster just cause it would be a cool way to solve a problem, it needs to fit into the company goals and needs and policies.

This, sadly, also means that a company can restrict your growth in some ways. It doesn't mean you can't grow, but you can't grow in any possible way imaginable. Choose your battles, etc.

Why is this useful?

It might give you a better framework for analyzing your skill set than "junior" "intermediate" "expert". Shuhari isn't about the amount of your knowledge, it's about how you practice your skill and what is your current approach to learning.

And again - being on Shu level doesn't mean your bad / evil / stupid / incompetent / slow / dumb / etc.

Kata

This is not a new or difficult concept. Kata are the unit tests of your skills. The best way to learn is in small pieces. Sometimes all you need to do is write a few lines of code in REPL.

ADHD and others

This is not a medical advice, but keep in mind that you might prefer different learning style than others. Some people like to RTFM. Some want to dive in and try it on their own. You'll have to balance finding and using the style you prefer, but also remembering the limitations of each method. Watching youtube doesn't give you actual experience. Reading the manual doesn't help you remember everything. Trial and error programming won't alert you to potential pitfalls the code will have in edge cases.

The most effective method is, always was, and always will be having a mentor.

Remember to take breaks. Fresh air, clean water, healthy, varied diet, regular movement and exercise. With both diet and exercise, adopt an additive mindset - sure you might be eating a greasy frozen pizza, but if you add some spinach, rucola, tomatoes, peppers on top of it, you're eating _some_ vegetables. If you do only 1 push-up per day, it's infinitely more than 0 pushups.

If blaming or hating yourself for not doing enough would work, it would have worked by now.

Medication might help some. To get diagnosed with ADHD as an adult in Estonia, you must document that it's affecting your life, fulfill the diagnostic criteria, and fork out 250~350 euro for a cognitive assessment. Don't bother with state psychiatrists.

Some over the counter supplements that might or might not help: Vitamin D, Omega-3, Lecithin, Magnesium L-Threonate, Ginkgo Biloba. Caffeine stimulates your brain indiscriminately and might make it harder to concentrate, and also builds up tolerance.

Cynefin

See more at https://en.wikipedia.org/wiki/Cynefin_framework 

Cynefin (Welsh for 'habitat', pronounced like if you take the name Kevin and make it keh-nev-in... i think) is a framework usually used for crisis management and decision making. However, you can use it to aid your learning, to help make sense of situations like production incidents, or when refining tasks during planning meetings.

One use is to look at the 5 domains and figuring out which of them are you comfortable with, and where is your current task located. The names might not be what they seem at first though. They don't represent how long will a task take.

Let's start from bottom right and then move counter-clockwise.

(1) The bottom-right domain is called Clear or Obvious or Simple or Known - it's easy to think of it as tasks like CRUD, BO page with pagination. Generally something that can be easily unit tested.

However, even more complex tasks like placing an order - where there's a lot to keep in mind, many branched pathways, legal requirements, asynchronous calls, etc, something you’d cover with a bunch of integration tests - is still considered “clear” in this framework. If there are defined rules leading to defined results, it's "Clear".

(2) Top right corner is Complicated or Knowable - e.g. an incident in production - a bug that we haven’t found, or an unidentified performance issue. The approach for these is “Sense - analyze - respond” or maybe for tasks that are not burning, “have a meetings and discuss and split the tasks". If you're feeling overwhelmed by a task, it's maybe because it's in the Complicated domain, and you need to find a way to move it to the Clear domain.

(3) Complex domain - investigating an incident where you don’t know what’s wrong and what causes it (untestable, impossible to replicate). Most likely, this is a production incident when you don't even know what's going on. Instead of looking at a dashboard and seeing "oh this endpoint is slow", it's something like "something is slow sometimes but we don't know what caused it and what is a side effect". In this domain, you would probably add more logging, create new Grafana graphs, dive deep into Kibana logs, etc.

Definitely not a domain that should be a part of feature development, unless you're way out of your depth and completely misunderstood how a given technology works.

(4) Chaos domain is not a good place to be. The cause and effect are unclear, e.g. fighting off a hacking attack. It's never happened before, there are no best practices, no playbook, best action is any action. "Have you tried turning it off and on again" style approach, but it might work on some occasions - it's better than nothing. Generally you want to move out of this domain asap.

Example 1: Improving a performance by adding an SQL index can be Simple/Clear/Obvious, but adding redis caching with invalidation to endpoints can be Complicated, if you don't know until you try, and it can be Complex, if you have cache that isn't invalidated immediately, and the impact of having an outdated cache and inconsistent data might be difficult to understand.

If you mess it up and wrong data starts showing to wrong customers, you might feel like it's chaotic because it's stressful, but you're really in Simple or Complicated situation, because you either you know you messed up the caching rules, or you don't know exactly, but have a way to measure it and find out.

(5) Confusion in the middle of the illustration - when you don’t know which one you have, best to split the problem and try to assign parts into different 4 domains.

Remember that for any situation, the domains are individual - a non-programmer can see BO acting weird (Chaotic domain or Confusion), junior dev can see slowness without an obvious cause (Complicated domain) DBA can see a missing index (Simple).

Possibly the most important thing to remember is that you can keep moving the problem between the domains.

Example 2:

implementing an existing compression algorithm is Simple.

developing a new disassembly tool, DRM, or compression is Complicated (trial and error to work around more and more tricks)

developing an algorithm that does open heart surgeries is impossible Complex

Trying to crack a brand new cipher is Chaotic because you don't know what's the content, what's the cipher, what information is there in what format, how many layers of compression, encryption and encoding are there

Example 3:

developing an illegal, unlicensed Tetris™️ prototype is simple, and there are plenty of tutorials available

developing a PvP multiplayer game is Complicated, because you'll have to measure many different unpredictable situations, strategies, and combinations to balance it

developing an MMORPG like EVE Online is Complex because there's no easy, orderly way to have 5'000 players shoot lasers at each other for 12 hours.

developing any game is Chaotic if you're an overconfident noob

Example 4:

making a fake sportsbook website without any real money is Simple

making a real sportsbook website with real money and wallet and 3rd party odds is Simple, even if it will take months

managing odds is both Complicated and Complex

making good UI for both FO and BO is Complex

making a sportsbook website that performs well under a very high load with very fast resolving is Complex because there is never any realistic load testing tool

Example 5:

fixing a bug in logic in a feature that's otherwise behaving correctly and has clean code is usually Simple

fixing a bug in a horrible spaghetti code is Complicated

fixing a bug in an OS kernel on some specific hardware that exhibits undocumented behavior is Complex

trying to fix a software bug when you actually have physical memory corruption is Chaotic

Figuring out how to use Cynefin is up to you. If nothing else, remember to try to take a step back, have a fresh look at a task that's stumping you, and figuring out why isn't the task "Simple". Usually it's one of the three - either you're lacking some technical knowledge (read the manual; Complicated -> Simple), or you're not sure how exactly it is used in our company (ask questions; Complex -> Complicated -> Simple), or you're overwhelmed by a task that's otherwise in your capacity (split the task; Complicated -> Simple).

[govindhtech]

What is Argo CD? And When Was Argo CD Established?

What Is Argo CD?

Argo CD is declarative Kubernetes GitOps continuous delivery.

In DevOps, ArgoCD is a Continuous Delivery (CD) technology that has become well-liked for delivering applications to Kubernetes. It is based on the GitOps deployment methodology.

When was Argo CD Established?

Argo CD was created at Intuit and made publicly available following Applatix’s 2018 acquisition by Intuit. The founding developers of Applatix, Hong Wang, Jesse Suen, and Alexander Matyushentsev, made the Argo project open-source in 2017.

Why Argo CD?

Declarative and version-controlled application definitions, configurations, and environments are ideal. Automated, auditable, and easily comprehensible application deployment and lifecycle management are essential.

Getting Started

Quick Start

kubectl create namespace argocd kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml

For some features, more user-friendly documentation is offered. Refer to the upgrade guide if you want to upgrade your Argo CD. Those interested in creating third-party connectors can access developer-oriented resources.

How it works

Argo CD defines the intended application state by employing Git repositories as the source of truth, in accordance with the GitOps pattern. There are various approaches to specify Kubernetes manifests:

Applications for Customization

Helm charts

JSONNET files

Simple YAML/JSON manifest directory

Any custom configuration management tool that is set up as a plugin

The deployment of the intended application states in the designated target settings is automated by Argo CD. Deployments of applications can monitor changes to branches, tags, or pinned to a particular manifest version at a Git commit.

Architecture

The implementation of Argo CD is a Kubernetes controller that continually observes active apps and contrasts their present, live state with the target state (as defined in the Git repository). Out Of Sync is the term used to describe a deployed application whose live state differs from the target state. In addition to reporting and visualizing the differences, Argo CD offers the ability to manually or automatically sync the current state back to the intended goal state. The designated target environments can automatically apply and reflect any changes made to the intended target state in the Git repository.

Components

API Server

The Web UI, CLI, and CI/CD systems use the API, which is exposed by the gRPC/REST server. Its duties include the following:

Status reporting and application management

Launching application functions (such as rollback, sync, and user-defined actions)

Cluster credential management and repository (k8s secrets)

RBAC enforcement

Authentication, and auth delegation to outside identity providers

Git webhook event listener/forwarder

Repository Server

An internal service called the repository server keeps a local cache of the Git repository containing the application manifests. When given the following inputs, it is in charge of creating and returning the Kubernetes manifests:

URL of the repository

Revision (tag, branch, commit)

Path of the application

Template-specific configurations: helm values.yaml, parameters

A Kubernetes controller known as the application controller keeps an eye on all active apps and contrasts their actual, live state with the intended target state as defined in the repository. When it identifies an Out Of Sync application state, it may take remedial action. It is in charge of calling any user-specified hooks for lifecycle events (Sync, PostSync, and PreSync).

Features

Applications are automatically deployed to designated target environments.

Multiple configuration management/templating tools (Kustomize, Helm, Jsonnet, and plain-YAML) are supported.

Capacity to oversee and implement across several clusters

Integration of SSO (OIDC, OAuth2, LDAP, SAML 2.0, Microsoft, LinkedIn, GitHub, GitLab)

RBAC and multi-tenancy authorization policies

Rollback/Roll-anywhere to any Git repository-committed application configuration

Analysis of the application resources’ health state

Automated visualization and detection of configuration drift

Applications can be synced manually or automatically to their desired state.

Web user interface that shows program activity in real time

CLI for CI integration and automation

Integration of webhooks (GitHub, BitBucket, GitLab)

Tokens of access for automation

Hooks for PreSync, Sync, and PostSync to facilitate intricate application rollouts (such as canary and blue/green upgrades)

Application event and API call audit trails

Prometheus measurements

To override helm parameters in Git, use parameter overrides.

Read more on Govindhtech.com

[greenoperator]

Microsoft Azure Fundamentals AI-900 (Part 5)

Microsoft Azure AI Fundamentals: Explore visual studio tools for machine learning

What is machine learning? A technique that uses math and statistics to create models that predict unknown values

Types of Machine learning

Regression - predict a continuous value, like a price, a sales total, a measure, etc

Classification - determine a class label.

Clustering - determine labels by grouping similar information into label groups

x = features

y = label

Azure Machine Learning Studio

You can use the workspace to develop solutions with the Azure ML service on the web portal or with developer tools

Web portal for ML solutions in Sure

Capabilities for preparing data, training models, publishing and monitoring a service.

First step assign a workspace to a studio.

Compute targets are cloud-based resources which can run model training and data exploration processes

Compute Instances - Development workstations that data scientists can use to work with data and models

Compute Clusters - Scalable clusters of VMs for on demand processing of experiment code

Inference Clusters - Deployment targets for predictive services that use your trained models

Attached Compute - Links to existing Azure compute resources like VMs or Azure data brick clusters

What is Azure Automated Machine Learning

Jobs have multiple settings

Provide information needed to specify your training scripts, compute target and Azure ML environment and run a training job

Understand the AutoML Process

ML model must be trained with existing data

Data scientists spend lots of time pre-processing and selecting data

This is time consuming and often makes inefficient use of expensive compute hardware

In Azure ML data for model training and other operations are encapsulated in a data set.

You create your own dataset.

Classification (predicting categories or classes)

Regression (predicting numeric values)

Time series forecasting (predicting numeric values at a future point in time)

After part of the data is used to train a model, then the rest of the data is used to iteratively test or cross validate the model

The metric is calculated by comparing the actual known label or value with the predicted one

Difference between the actual known and predicted is known as residuals; they indicate amount of error in the model.

Root Mean Squared Error (RMSE) is a performance metric. The smaller the value, the more accurate the model’s prediction is

Normalized root mean squared error (NRMSE) standardizes the metric to be used between models which have different scales.

Shows the frequency of residual value ranges.

Residuals represents variance between predicted and true values that can’t be explained by the model, errors

Most frequently occurring residual values (errors) should be clustered around zero.

You want small errors with fewer errors at the extreme ends of the sale

Should show a diagonal trend where the predicted value correlates closely with the true value

Dotted line shows a perfect model’s performance

The closer to the line of your model’s average predicted value to the dotted, the better.

Services can be deployed as an Azure Container Instance (ACI) or to a Azure Kubernetes Service (AKS) cluster

For production AKS is recommended.

Identify regression machine learning scenarios

Regression is a form of ML

Understands the relationships between variables to predict a desired outcome

Predicts a numeric label or outcome base on variables (features)

Regression is an example of supervised ML

What is Azure Machine Learning designer

Allow you to organize, manage, and reuse complex ML workflows across projects and users

Pipelines start with the dataset you want to use to train the model

Each time you run a pipelines, the context(history) is stored as a pipeline job

Encapsulates one step in a machine learning pipeline.

Like a function in programming

In a pipeline project, you access data assets and components from the Asset Library tab

You can create data assets on the data tab from local files, web files, open at a sets, and a datastore

Data assets appear in the Asset Library

Azure ML job executes a task against a specified compute  target.

Jobs allow systematic tracking of your ML experiments and workflows.

Understand steps for regression

To train a regression model, your data set needs to include historic features and known label values.

Use the designer’s Score Model component to generate the predicted class label value

Connect all the components that will run in the experiment

Average difference between predicted and true values

It is based on the same unit as the label

The lower the value is the better the model is predicting

The square root of the mean squared difference between predicted and true values

Metric based on the same unit as the label.

A larger difference indicates greater variance in the individual  label errors

Relative metric between 0 and 1 on the square based on the square of the differences between predicted and true values

Closer to 0 means the better the model is performing.

Since the value is relative, it can compare different models with different label units

Relative metric between 0 and 1 on the square based on the absolute of the differences between predicted and true values

Closer to 0 means the better the model is performing.

Can be used to compare models where the labels are in different units

Also known as R-squared

Summarizes how much variance exists between predicted and true values

Closer to 1 means the model is performing better

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

Create a classification model with Azure ML designer

Classification is a form of ML used to predict which category an item belongs to

Like regression this is a supervised ML technique.

Understand steps for classification

True Positive - Model predicts the label and the label is correct

False Positive - Model predicts wrong label and the data has the label

False Negative - Model predicts the wrong label, and the data does have the label

True Negative - Model predicts the label correctly and the data has the label

For multi-class classification, same approach is used. A model with 3 possible results would have a 3x3 matrix.

Diagonal lien of cells were the predicted and actual labels match

Number of cases classified as positive that are actually positive

True positives divided by (true positives + false positives)

Fraction of positive cases correctly identified

Number of true positives divided by (true positives + false negatives)

Overall metric that essentially combines precision and recall

Classification models predict probability for each possible class

For binary classification models, the probability is between 0 and 1

Setting the threshold can define when a value is interpreted as 0 or 1.  If its set to 0.5 then 0.5-1.0 is 1 and 0.0-0.4 is 0

Recall also known as True Positive Rate

Has a corresponding False Positive Rate

Plotting these two metrics on a graph for all values between 0 and 1 provides information.

Receiver Operating Characteristic (ROC) is the curve.

In a perfect model, this curve would be high to the top left

Area under the curve (AUC).

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

Create a Clustering model with Azure ML designer

Clustering is used to group similar objects together based on features.

Clustering is an example of unsupervised learning, you train a model to just separate items based on their features.

Understanding steps for clustering

Prebuilt components exist that allow you to clean the data, normalize it, join tables and more

Requires a dataset that includes multiple observations of the items you want to cluster

Requires numeric features that can be used to determine similarities between individual cases

Initializing K coordinates as randomly selected points called centroids in an n-dimensional space (n is the number of dimensions in the feature vectors)

Plotting feature vectors as points in the same space and assigns a value how close they are to the closes centroid

Moving the centroids to the middle points allocated to it (mean distance)

Reassigning to the closes centroids after the move

Repeating the last two steps until tone.

Maximum distances between each point and the centroid of that point’s cluster.

If the value is high it can mean that cluster is widely dispersed.

With the Average Distance to Closer Center, we can determine how spread out the cluster is

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

[hawkstack]

Creating and Configuring Production ROSA Clusters (CS220) – A Practical Guide

Introduction

Red Hat OpenShift Service on AWS (ROSA) is a powerful managed Kubernetes solution that blends the scalability of AWS with the developer-centric features of OpenShift. Whether you're modernizing applications or building cloud-native architectures, ROSA provides a production-grade container platform with integrated support from Red Hat and AWS. In this blog post, we’ll walk through the essential steps covered in CS220: Creating and Configuring Production ROSA Clusters, an instructor-led course designed for DevOps professionals and cloud architects.

What is CS220?

CS220 is a hands-on, lab-driven course developed by Red Hat that teaches IT teams how to deploy, configure, and manage ROSA clusters in a production environment. It is tailored for organizations that are serious about leveraging OpenShift at scale with the operational convenience of a fully managed service.

Why ROSA for Production?

Deploying OpenShift through ROSA offers multiple benefits:

Streamlined Deployment: Fully managed clusters provisioned in minutes.

Integrated Security: AWS IAM, STS, and OpenShift RBAC policies combined.

Scalability: Elastic and cost-efficient scaling with built-in monitoring and logging.

Support: Joint support model between AWS and Red Hat.

Key Concepts Covered in CS220

Here’s a breakdown of the main learning outcomes from the CS220 course:

1. Provisioning ROSA Clusters

Participants learn how to:

Set up required AWS permissions and networking pre-requisites.

Deploy clusters using Red Hat OpenShift Cluster Manager (OCM) or CLI tools like rosa and oc.

Use STS (Short-Term Credentials) for secure cluster access.

2. Configuring Identity Providers

Learn how to integrate Identity Providers (IdPs) such as:

GitHub, Google, LDAP, or corporate IdPs using OpenID Connect.

Configure secure, role-based access control (RBAC) for teams.

3. Networking and Security Best Practices

Implement private clusters with public or private load balancers.

Enable end-to-end encryption for APIs and services.

Use Security Context Constraints (SCCs) and network policies for workload isolation.

4. Storage and Data Management

Configure dynamic storage provisioning with AWS EBS, EFS, or external CSI drivers.

Learn persistent volume (PV) and persistent volume claim (PVC) lifecycle management.

5. Cluster Monitoring and Logging

Integrate OpenShift Monitoring Stack for health and performance insights.

Forward logs to Amazon CloudWatch, ElasticSearch, or third-party SIEM tools.

6. Cluster Scaling and Updates

Set up autoscaling for compute nodes.

Perform controlled updates and understand ROSA’s maintenance policies.

Use Cases for ROSA in Production

Modernizing Monoliths to Microservices

CI/CD Platform for Agile Development

Data Science and ML Workflows with OpenShift AI

Edge Computing with OpenShift on AWS Outposts

Getting Started with CS220

The CS220 course is ideal for:

DevOps Engineers

Cloud Architects

Platform Engineers

Prerequisites: Basic knowledge of OpenShift administration (recommended: DO280 or equivalent experience) and a working AWS account.

Course Format: Instructor-led (virtual or on-site), hands-on labs, and guided projects.

Final Thoughts

As more enterprises adopt hybrid and multi-cloud strategies, ROSA emerges as a strategic choice for running OpenShift on AWS with minimal operational overhead. CS220 equips your team with the right skills to confidently deploy, configure, and manage production-grade ROSA clusters — unlocking agility, security, and innovation in your cloud-native journey.

Want to Learn More or Book the CS220 Course? At HawkStack Technologies, we offer certified Red Hat training, including CS220, tailored for teams and enterprises. Contact us today to schedule a session or explore our Red Hat Learning Subscription packages. www.hawkstack.com

[seodigital7]

Machine Learning Infrastructure: The Foundation of Scalable AI Solutions

Introduction: Why Machine Learning Infrastructure Matters

In today's digital-first world, the adoption of artificial intelligence (AI) and machine learning (ML) is revolutionizing every industry—from healthcare and finance to e-commerce and entertainment. However, while many organizations aim to leverage ML for automation and insights, few realize that success depends not just on algorithms, but also on a well-structured machine learning infrastructure.

Machine learning infrastructure provides the backbone needed to deploy, monitor, scale, and maintain ML models effectively. Without it, even the most promising ML solutions fail to meet their potential.

In this comprehensive guide from diglip7.com, we’ll explore what machine learning infrastructure is, why it’s crucial, and how businesses can build and manage it effectively.

What is Machine Learning Infrastructure?

Machine learning infrastructure refers to the full stack of tools, platforms, and systems that support the development, training, deployment, and monitoring of ML models. This includes:

Data storage systems

Compute resources (CPU, GPU, TPU)

Model training and validation environments

Monitoring and orchestration tools

Version control for code and models

Together, these components form the ecosystem where machine learning workflows operate efficiently and reliably.

Key Components of Machine Learning Infrastructure

To build robust ML pipelines, several foundational elements must be in place:

1. Data Infrastructure

Data is the fuel of machine learning. Key tools and technologies include:

Data Lakes & Warehouses: Store structured and unstructured data (e.g., AWS S3, Google BigQuery).

ETL Pipelines: Extract, transform, and load raw data for modeling (e.g., Apache Airflow, dbt).

Data Labeling Tools: For supervised learning (e.g., Labelbox, Amazon SageMaker Ground Truth).

2. Compute Resources

Training ML models requires high-performance computing. Options include:

On-Premise Clusters: Cost-effective for large enterprises.

Cloud Compute: Scalable resources like AWS EC2, Google Cloud AI Platform, or Azure ML.

GPUs/TPUs: Essential for deep learning and neural networks.

3. Model Training Platforms

These platforms simplify experimentation and hyperparameter tuning:

TensorFlow, PyTorch, Scikit-learn: Popular ML libraries.

MLflow: Experiment tracking and model lifecycle management.

KubeFlow: ML workflow orchestration on Kubernetes.

4. Deployment Infrastructure

Once trained, models must be deployed in real-world environments:

Containers & Microservices: Docker, Kubernetes, and serverless functions.

Model Serving Platforms: TensorFlow Serving, TorchServe, or custom REST APIs.

CI/CD Pipelines: Automate testing, integration, and deployment of ML models.

5. Monitoring & Observability

Key to ensure ongoing model performance:

Drift Detection: Spot when model predictions diverge from expected outputs.

Performance Monitoring: Track latency, accuracy, and throughput.

Logging & Alerts: Tools like Prometheus, Grafana, or Seldon Core.

Benefits of Investing in Machine Learning Infrastructure

Here’s why having a strong machine learning infrastructure matters:

Scalability: Run models on large datasets and serve thousands of requests per second.

Reproducibility: Re-run experiments with the same configuration.

Speed: Accelerate development cycles with automation and reusable pipelines.

Collaboration: Enable data scientists, ML engineers, and DevOps to work in sync.

Compliance: Keep data and models auditable and secure for regulations like GDPR or HIPAA.

Real-World Applications of Machine Learning Infrastructure

Let’s look at how industry leaders use ML infrastructure to power their services:

Netflix: Uses a robust ML pipeline to personalize content and optimize streaming.

Amazon: Trains recommendation models using massive data pipelines and custom ML platforms.

Tesla: Collects real-time driving data from vehicles and retrains autonomous driving models.

Spotify: Relies on cloud-based infrastructure for playlist generation and music discovery.

Challenges in Building ML Infrastructure

Despite its importance, developing ML infrastructure has its hurdles:

High Costs: GPU servers and cloud compute aren't cheap.

Complex Tooling: Choosing the right combination of tools can be overwhelming.

Maintenance Overhead: Regular updates, monitoring, and security patching are required.

Talent Shortage: Skilled ML engineers and MLOps professionals are in short supply.

How to Build Machine Learning Infrastructure: A Step-by-Step Guide

Here’s a simplified roadmap for setting up scalable ML infrastructure:

Step 1: Define Use Cases

Know what problem you're solving. Fraud detection? Product recommendations? Forecasting?

Step 2: Collect & Store Data

Use data lakes, warehouses, or relational databases. Ensure it’s clean, labeled, and secure.

Step 3: Choose ML Tools

Select frameworks (e.g., TensorFlow, PyTorch), orchestration tools, and compute environments.

Step 4: Set Up Compute Environment

Use cloud-based Jupyter notebooks, Colab, or on-premise GPUs for training.

Step 5: Build CI/CD Pipelines

Automate model testing and deployment with Git, Jenkins, or MLflow.

Step 6: Monitor Performance

Track accuracy, latency, and data drift. Set alerts for anomalies.

Step 7: Iterate & Improve

Collect feedback, retrain models, and scale solutions based on business needs.

Machine Learning Infrastructure Providers & Tools

Below are some popular platforms that help streamline ML infrastructure: Tool/PlatformPurposeExampleAmazon SageMakerFull ML development environmentEnd-to-end ML pipelineGoogle Vertex AICloud ML serviceTraining, deploying, managing ML modelsDatabricksBig data + MLCollaborative notebooksKubeFlowKubernetes-based ML workflowsModel orchestrationMLflowModel lifecycle trackingExperiments, models, metricsWeights & BiasesExperiment trackingVisualization and monitoring

Expert Review

Reviewed by: Rajeev Kapoor, Senior ML Engineer at DataStack AI

"Machine learning infrastructure is no longer a luxury; it's a necessity for scalable AI deployments. Companies that invest early in robust, cloud-native ML infrastructure are far more likely to deliver consistent, accurate, and responsible AI solutions."

Frequently Asked Questions (FAQs)

Q1: What is the difference between ML infrastructure and traditional IT infrastructure?

Answer: Traditional IT supports business applications, while ML infrastructure is designed for data processing, model training, and deployment at scale. It often includes specialized hardware (e.g., GPUs) and tools for data science workflows.

Q2: Can small businesses benefit from ML infrastructure?

Answer: Yes, with the rise of cloud platforms like AWS SageMaker and Google Vertex AI, even startups can leverage scalable machine learning infrastructure without heavy upfront investment.

Q3: Is Kubernetes necessary for ML infrastructure?

Answer: While not mandatory, Kubernetes helps orchestrate containerized workloads and is widely adopted for scalable ML infrastructure, especially in production environments.

Q4: What skills are needed to manage ML infrastructure?

Answer: Familiarity with Python, cloud computing, Docker/Kubernetes, CI/CD, and ML frameworks like TensorFlow or PyTorch is essential.

Q5: How often should ML models be retrained?

Answer: It depends on data volatility. In dynamic environments (e.g., fraud detection), retraining may occur weekly or daily. In stable domains, monthly or quarterly retraining suffices.

Final Thoughts

Machine learning infrastructure isn’t just about stacking technologies—it's about creating an agile, scalable, and collaborative environment that empowers data scientists and engineers to build models with real-world impact. Whether you're a startup or an enterprise, investing in the right infrastructure will directly influence the success of your AI initiatives.

By building and maintaining a robust ML infrastructure, you ensure that your models perform optimally, adapt to new data, and generate consistent business value.

For more insights and updates on AI, ML, and digital innovation, visit diglip7.com.

[virtualizationhowto]

Lens Kubernetes: Simple Cluster Management Dashboard and Monitoring

Lens Kubernetes: Simple Cluster Management Dashboard and Monitoring #homelab #kubernetes #KubernetesManagement #LensKubernetesDesktop #KubernetesClusterManagement #MultiClusterManagement #KubernetesSecurityFeatures #KubernetesUI #kubernetesmonitoring

Kubernetes is a well-known container orchestration platform. It allows admins and organizations to operate their containers and support modern applications in the enterprise. Kubernetes management is not for the “faint of heart.” It requires the right skill set and tools. Lens Kubernetes desktop is an app that enables managing Kubernetes clusters on Windows and Linux devices. Table of…

[ludoonline]

Security and Compliance in Cloud Deployments: A Proactive DevOps Approach

As cloud computing becomes the backbone of modern digital infrastructure, organizations are increasingly migrating applications and data to the cloud for agility, scalability, and cost-efficiency. However, this shift also brings elevated risks around security and compliance. To ensure safety and regulatory alignment, companies must adopt a proactive DevOps approach that integrates security into every stage of the development lifecycle—commonly referred to as DevSecOps.

Why Security and Compliance Matter in the Cloud

Cloud environments are dynamic and complex. Without the proper controls in place, they can easily become vulnerable to data breaches, configuration errors, insider threats, and compliance violations. Unlike traditional infrastructure, cloud-native deployments are continuously evolving, which requires real-time security measures and automated compliance enforcement.

Neglecting these areas can lead to:

Financial penalties for regulatory violations (GDPR, HIPAA, SOC 2, etc.)

Data loss and reputation damage

Business continuity risks due to breaches or downtime

The Role of DevOps in Cloud Security

DevOps is built around principles of automation, collaboration, and continuous delivery. By extending these principles to include security (DevSecOps), teams can ensure that infrastructure and applications are secure from the ground up, rather than bolted on as an afterthought.

A proactive DevOps approach focuses on:

Shift-Left Security: Security checks are moved earlier in the development process to catch issues before deployment.

Continuous Compliance: Policies are codified and integrated into CI/CD pipelines to maintain adherence to industry standards automatically.

Automated Risk Detection: Real-time scanning tools identify vulnerabilities, misconfigurations, and policy violations continuously.

Infrastructure as Code (IaC) Security: IaC templates are scanned for compliance and security flaws before provisioning cloud infrastructure.

Key Components of a Proactive Cloud Security Strategy

Identity and Access Management (IAM): Ensure least-privilege access using role-based policies and multi-factor authentication.

Encryption: Enforce encryption of data both at rest and in transit using cloud-native tools and third-party integrations.

Vulnerability Scanning: Use automated scanners to check applications, containers, and VMs for known security flaws.

Compliance Monitoring: Track compliance posture continuously against frameworks such as ISO 27001, PCI-DSS, and NIST.

Logging and Monitoring: Centralized logging and anomaly detection help detect threats early and support forensic investigations.

Secrets Management: Store and manage credentials, tokens, and keys using secure vaults.

Best Practices for DevSecOps in the Cloud

Integrate Security into CI/CD Pipelines: Use tools like Snyk, Aqua, and Checkov to run security checks automatically.

Perform Regular Threat Modeling: Continuously assess evolving attack surfaces and prioritize high-impact risks.

Automate Patch Management: Ensure all components are regularly updated and unpatched vulnerabilities are minimized.

Enable Policy as Code: Define and enforce compliance rules through version-controlled code in your DevOps pipeline.

Train Developers and Engineers: Security is everyone’s responsibility—conduct regular security training and awareness sessions.

How Salzen Cloud Ensures Secure Cloud Deployments

At Salzen Cloud, we embed security and compliance at the core of our cloud solutions. Our team works with clients to develop secure-by-design architectures that incorporate DevSecOps principles from planning to production. Whether it's automating compliance reports, hardening Kubernetes clusters, or configuring IAM policies, we ensure cloud operations are secure, scalable, and audit-ready.

Conclusion

In the era of cloud-native applications, security and compliance can no longer be reactive. A proactive DevOps approach ensures that every component of your cloud environment is secure, compliant, and continuously monitored. By embedding security into CI/CD workflows and automating compliance checks, organizations can mitigate risks while maintaining development speed.

Partner with Salzen Cloud to build secure and compliant cloud infrastructures with confidence.

[bliiot]

Comparison of Ubuntu, Debian, and Yocto for IIoT and Edge Computing

In industrial IoT (IIoT) and edge computing scenarios, Ubuntu, Debian, and Yocto Project each have unique advantages. Below is a detailed comparison and recommendations for these three systems:

1. Ubuntu (ARM)

Advantages

Ready-to-use: Provides official ARM images (e.g., Ubuntu Server 22.04 LTS) supporting hardware like Raspberry Pi and NVIDIA Jetson, requiring no complex configuration.

Cloud-native support: Built-in tools like MicroK8s, Docker, and Kubernetes, ideal for edge-cloud collaboration.

Long-term support (LTS): 5 years of security updates, meeting industrial stability requirements.

Rich software ecosystem: Access to AI/ML tools (e.g., TensorFlow Lite) and databases (e.g., PostgreSQL ARM-optimized) via APT and Snap Store.

Use Cases

Rapid prototyping: Quick deployment of Python/Node.js applications on edge gateways.

AI edge inference: Running computer vision models (e.g., ROS 2 + Ubuntu) on Jetson devices.

Lightweight K8s clusters: Edge nodes managed by MicroK8s.

Limitations

Higher resource usage (minimum ~512MB RAM), unsuitable for ultra-low-power devices.

2. Debian (ARM)

Advantages

Exceptional stability: Packages undergo rigorous testing, ideal for 24/7 industrial operation.

Lightweight: Minimal installation requires only 128MB RAM; GUI-free versions available.

Long-term support: Up to 10+ years of security updates via Debian LTS (with commercial support).

Hardware compatibility: Supports older or niche ARM chips (e.g., TI Sitara series).

Use Cases

Industrial controllers: PLCs, HMIs, and other devices requiring deterministic responses.

Network edge devices: Firewalls, protocol gateways (e.g., Modbus-to-MQTT).

Critical systems (medical/transport): Compliance with IEC 62304/DO-178C certifications.

Limitations

Older software versions (e.g., default GCC version); newer features require backports.

3. Yocto Project

Advantages

Full customization: Tailor everything from kernel to user space, generating minimal images (<50MB possible).

Real-time extensions: Supports Xenomai/Preempt-RT patches for μs-level latency.

Cross-platform portability: Single recipe set adapts to multiple hardware platforms (e.g., NXP i.MX6 → i.MX8).

Security design: Built-in industrial-grade features like SELinux and dm-verity.

Use Cases

Custom industrial devices: Requires specific kernel configurations or proprietary drivers (e.g., CAN-FD bus support).

High real-time systems: Robotic motion control, CNC machines.

Resource-constrained terminals: Sensor nodes running lightweight stacks (e.g., Zephyr+FreeRTOS hybrid deployment).

Limitations

Steep learning curve (BitBake syntax required); longer development cycles.

4. Comparison Summary

5. Selection Recommendations

Choose Ubuntu ARM: For rapid deployment of edge AI applications (e.g., vision detection on Jetson) or deep integration with public clouds (e.g., AWS IoT Greengrass).

Choose Debian ARM: For mission-critical industrial equipment (e.g., substation monitoring) where stability outweighs feature novelty.

Choose Yocto Project: For custom hardware development (e.g., proprietary industrial boards) or strict real-time/safety certification (e.g., ISO 13849) requirements.

6. Hybrid Architecture Example

Smart factory edge node:

Real-time control layer: RTOS built with Yocto (controlling robotic arms)

Data processing layer: Debian running OPC UA servers

Cloud connectivity layer: Ubuntu Server managing K8s edge clusters

Combining these systems based on specific needs can maximize the efficiency of IIoT edge computing.

[sysadminxpert]

Kubernetes Objects Explained 💡 Pods, Services, Deployments & More for Admins & Devs

learn how Kubernetes keeps your apps running as expected using concepts like desired state, replication, config management, and persistent storage.

✔️ Pod – Basic unit that runs your containers ✔️ Service – Stable network access to Pods ✔️ Deployment – Rolling updates & scaling made easy ✔️ ReplicaSet – Maintains desired number of Pods ✔️ Job & CronJob – Run tasks once or on schedule ✔️ ConfigMap & Secret – Externalize configs & secure credentials ✔️ PV & PVC – Persistent storage management ✔️ Namespace – Cluster-level resource isolation ✔️ DaemonSet – Run a Pod on every node ✔️ StatefulSet – For stateful apps like databases ✔️ ReplicationController – The older way to manage Pods

[coredgeblogs]

Containerization with Docker and Kubernetes: The Dynamic Duo of Modern Tech

Let’s dive into the world of containerization. Containerization is a software deployment process that packages together software code with all its essential components, like the files, frameworks, libraries, and other dependencies it needs to run on any infrastructure. Here apps don’t just sit pretty—they’re lightweight, portable, and ready to roll out anywhere,

Containers, which are an integral constituent of the DevOps architecture are lightweight, portable, and highly beneficial to automation. For various use cases, containerization has become a foundation of development pipelines and application infrastructure. Developers often figure out containerization as a companion or substitute to virtualization. Because of its measurable benefits, as containerization develops and gains traction, it gives DevOps a lot to talk about. Implementing it securely and understanding what containerization is, can help your organization upgrade and scale its technology stacks.

 Let’s meet the icons of our show: Docker and Kubernetes.

 Whether you’re just a newbie or a veteran pro, this guide, sprinkled with real-world applications, will take you on a tour with a fun and informative walkthrough. Oh, and obviously, we’ll speak about ArgoCD too!

Introduction to Docker: Containers Made Simple:

 Think it like going on a trip requires a lot of stuff to be kept in luggage, so instead of tossing your pieces of stuff into luggage, you pack everything into a small, perfectly organized box. That’s Docker! This platform wraps up your application, libraries, and dependencies—mostly everything—into a neat little “container.” The consistency in these containers makes them run from anywhere, whether it’s your laptop or a massive cloud server.

Docker and Kubernetes are considered two of the best-admired technologies for containerized development. Docker is used to bundle applications into containers, while these containers in production are orchestrated and managed by Kubernetes.

Kubernetes has shifted the paradigm of the development and deployment of containerized applications, providing a robust orchestration platform that automates tasks such as load balancing, scaling, and self-healing. The realization of the full potential of Kubernetes orchestration can only be ascertained when your applications are well-prepared, effective, and securely developed from the beginning. That’s where Docker’s development tools come into the picture and play a vital role.

 Docker: A cool thing, why?

It’s because of the ease of portability and efficiency!

      Developers and sysadmins can finally be best buddies because the "It works on my  machine!" argument is now a thing of the past.

Pro Tip: Docker Hub is like an app store for containers—download prebuilt ones or share your own.

Basics of Kubernetes: The Master Orchestrator:

 Imagine it like, in a restaurant, if Docker is designated as a chef, Kubernetes acts as the restaurant manager, ensuring every dish reaches the table, fresh, hot, and on time. Kubernetes, abbreviated as K8s, is an open-source container orchestration system that automates deploying, scaling, and managing containerized applications.

Basics of Kubernetes:

Pods- Pods, consisting of one or more containers is the basic execution unit in Kubernetes.

Nodes: Physical or virtual machines that run pods are nodes.

Clusters: A unit of nodes that work together to run pods.

Deployments: A way to manage the rollout of new versions of an application.

Services: An abstraction that provides a network identity and load balancing for accessing applications.

 Key Features:

Load balancing: Handles traffic efficiently so that under pressure your app doesn’t crash.

Self-healing: Kubernetes restarts it immediately if something crashes. No drama, no downtime.

Scaling: Handles the spike in seasonal traffic like a pro.

How Docker and Kubernetes Work Together:

Docker and Kubernetes are like best buddies and work in proper coherence. The magic happens here! The containers are created by Docker and managed by Kubernetes. It’s like a dream team: Docker builds, Kubernetes scales.Suppose you have a fancy app related to microservices; the individual services like the login page or payment processor are handled by Docker, while Kubernetes ensures they all work together in tandem. Required updates? No Worry? Kubernetes has your back to handle all the concerns.

 ArgoCD makes an entry into the chat!

 For DevOps devotees, ArgoCD is a GitOps tool that pairs amazingly with Kubernetes. ArgoCD, specifically designed for Kubernetes environments, is a declarative GitOps continuous delivery tool. It operates as a Kubernetes controller and automates the deployment, rollouts, and rollbacks of applications across multiple environments such as production, staging, and development.

 Consistency across environments is ensured by Argo CD by applying and tracking changes to the infrastructure as code (IaC) configurations.

Benefits and Use Cases

Why are Docker and Kubernetes making a noise out there? Here’s why:

Benefits

Portability:  In any environment, containers run consistently.

Scalability: During traffic surges, Kubernetes scales your app seamlessly.

Automation: ArgoCD simplifies deployments and updates.

Cost-Efficiency: The resource is optimized only based upon your need.

Use Cases

E-commerce platforms: Flash sales are handled effectively without crashing.

Streaming services: Millions of user's streaming is managed seamlessly without any glitches.

AI/ML Workloads: For running massive AI models, pairing with Docker containers and Kubernetes’ scaling is picture-perfect.

 Wrapping it UP:

It isn’t about the competition to discuss Kubernetes vs Docker, it’s about collaboration. Docker manages the containers; Kubernetes make sure they play like rockstars on stage. Tools like ArgoCD spiced up this a little more, and things are set for a future-proof setup for modern applications.

So, are you ready to give it a shot? Let the magic of containerization transform your workflows by grabbing a Docker image and spinning up a Kubernetes cluster. And let’s not forget to bring ArgoCD into the fusion for some GitOps brilliance.

Happy containerizing!

[hawkstack]

Red Hat OpenShift Administration III: Scaling Deployments in the Enterprise

In the world of modern enterprise IT, scalability is not just a desirable trait—it's a mission-critical requirement. As organizations continue to adopt containerized applications and microservices architectures, the ability to seamlessly scale infrastructure and workloads becomes essential. That’s where Red Hat OpenShift Administration III comes into play, focusing on the advanced capabilities needed to manage and scale OpenShift clusters in large-scale production environments.

Why Scaling Matters in OpenShift

OpenShift, Red Hat’s Kubernetes-powered container platform, empowers DevOps teams to build, deploy, and manage applications at scale. But managing scalability isn’t just about increasing pod replicas or adding more nodes—it’s about making strategic, automated, and resilient decisions to meet dynamic demand, ensure availability, and optimize resource usage.

OpenShift Administration III (DO380) is the course designed to help administrators go beyond day-to-day operations and develop the skills needed to ensure enterprise-grade scalability and performance.

Key Takeaways from OpenShift Administration III

1. Advanced Cluster Management

The course teaches administrators how to manage large OpenShift clusters with hundreds or even thousands of nodes. Topics include:

Advanced node management

Infrastructure node roles

Cluster operators and custom resources

2. Automated Scaling Techniques

Learn how to configure and manage:

Horizontal Pod Autoscalers (HPA)

Vertical Pod Autoscalers (VPA)

Cluster Autoscalers These tools allow the platform to intelligently adjust resource consumption based on workload demands.

3. Optimizing Resource Utilization

One of the biggest challenges in scaling is maintaining cost-efficiency. OpenShift Administration III helps you fine-tune quotas, limits, and requests to avoid over-provisioning while ensuring optimal performance.

4. Managing Multitenancy at Scale

The course delves into managing enterprise workloads in a secure and multi-tenant environment. This includes:

Project-level isolation

Role-based access control (RBAC)

Secure networking policies

5. High Availability and Disaster Recovery

Scaling isn't just about growing—it’s about being resilient. Learn how to:

Configure etcd backup and restore

Maintain control plane and application availability

Build disaster recovery strategies

Who Should Take This Course?

This course is ideal for:

OpenShift administrators responsible for large-scale deployments

DevOps engineers managing Kubernetes-based platforms

System architects looking to standardize on Red Hat OpenShift across enterprise environments

Final Thoughts

As enterprises push towards digital transformation, the demand for scalable, resilient, and automated platforms continues to grow. Red Hat OpenShift Administration III equips IT professionals with the skills and strategies to confidently scale deployments, handle complex workloads, and maintain robust system performance across the enterprise.

Whether you're operating in a hybrid cloud, multi-cloud, or on-premises environment, mastering OpenShift scalability ensures your infrastructure can grow with your business.

Ready to take your OpenShift skills to the next level? Contact HawkStack Technologies today to learn about our Red Hat Learning Subscription (RHLS) and instructor-led training options for DO380 – Red Hat OpenShift Administration III. For more details www.hawkstack.com 

[govindhtech]

FinOps Hub 2.0 Removes Cloud Waste With Smart Analytics

FinOps Hub 2.0

As Google Cloud customers used FinOps Hub 2.0 to optimise, business feedback increased. Businesses often lack clear insights into resource consumption, creating a blind spot. DevOps users have tools and utilisation indicators to identify waste.

The latest State of FinOps 2025 Report emphasises waste reduction and workload optimisation as FinOps priorities. If customers don't understand consumption, workloads and apps are hard to optimise. Why get a committed usage discount for computing cores you may not be using fully?

Using paid resources more efficiently is generally the easiest change customers can make. The improved FinOps Hub in 2025 focusses on cleaning up optimisation possibilities to help you find, highlight, and eliminate unnecessary spending.

Discover waste: FinOps Hub 2.0 now includes utilisation data to identify optimisation opportunities.

FinOps Hub 2.0 released at Google Cloud Next 2025 to highlight resource utilisation statistics to discover waste and take immediate action. Waste can be an overprovisioned virtual machine (VM) that is barely used at 5%, an underprovisioned GKE cluster that is running hot at 110% utilisation and may fail, or managed resources like Cloud Run instances that are configured suboptimally or never used.

FinOps users may now display the most expensive waste category in a single heatmap per service or AppHub application. FinOps Hub not only identifies waste but also delivers cost savings for Cloud Run, Compute Engine, Kubernetes Engine, and Cloud SQL.

Highlight waste: FinOps Hub uses Gemini Cloud Assist for optimisation and engineering.

The fact that it utilises Gemini Cloud Assist to speed up FinOps Hub's most time-consuming tasks may make this version a 2.0. Since January 2024 to January 2025, Gemini Cloud Assist has saved clients over 100,000 FinOps hours a year by providing customised cost reports and synthesising insights.

Google Cloud offered FinOps Hub two options to simplify and automate procedures using Gemini Cloud Assist. FinOps can now get embedded optimisation insights on the hub, such cost reports, so you don't have to find the optimisation "needle in the haystack". Second, Gemini Cloud Assist can now assemble and provide the most significant waste insights to your engineering teams for speedy fixes.

Eliminate waste: Give IT solution owners a NEW IAM role authorisation to view and act on optimisation opportunities.

Tech solution owners now have access to the billing panel, FinOps' most anticipated feature. This will display Gemini Cloud Assist and FinOps data for all projects in one window. With multi-project views in the billing console, you can give a department that only uses a subset of projects for their infrastructure access to FinOps Hub or cost reports without giving them more billing data while still letting them view all of their data in one view.

The new Project Billing Costs Manager IAM role (or granular permissions) provides multi-project views. Sign up for the private preview of these new permissions. With increased access limitations, you may fully utilise FinOps solutions across your firm.

“With clouds overgrown, like winter’s old grime, spring clean your servers, save dollars and time.” Clean your cloud infrastructure with FinOps Hub 2.0 and Gemini Cloud Assist this spring. Whatever, Gemini says so.

[technocourses]

Getting Started with Google Kubernetes Engine: Your Gateway to Cloud-Native Greatness

After spending over 8 years deep in the trenches of cloud engineering and DevOps, I can tell you one thing for sure: if you're serious about scalability, flexibility, and real cloud-native application deployment, Google Kubernetes Engine (GKE) is where the magic happens.

Whether you’re new to Kubernetes or just exploring managed container platforms, getting started with Google Kubernetes Engine is one of the smartest moves you can make in your cloud journey.

"Containers are cool. Orchestrated containers? Game-changing."

🚀 What is Google Kubernetes Engine (GKE)?

Google Kubernetes Engine is a fully managed Kubernetes platform that runs on top of Google Cloud. GKE simplifies deploying, managing, and scaling containerized apps using Kubernetes—without the overhead of maintaining the control plane.

Why is this a big deal?

Because Kubernetes is notoriously powerful and notoriously complex. With GKE, Google handles all the heavy lifting—from cluster provisioning to upgrades, logging, and security.

"GKE takes the complexity out of Kubernetes so you can focus on building, not babysitting clusters."

🧭 Why Start with GKE?

If you're a developer, DevOps engineer, or cloud architect looking to:

Deploy scalable apps across hybrid/multi-cloud

Automate CI/CD workflows

Optimize infrastructure with autoscaling & spot instances

Run stateless or stateful microservices seamlessly

Then GKE is your launchpad.

Here’s what makes GKE shine:

Auto-upgrades & auto-repair for your clusters

Built-in security with Shielded GKE Nodes and Binary Authorization

Deep integration with Google Cloud IAM, VPC, and Logging

Autopilot mode for hands-off resource management

Native support for Anthos, Istio, and service meshes

"With GKE, it's not about managing containers—it's about unlocking agility at scale."

🔧 Getting Started with Google Kubernetes Engine

Ready to dive in? Here's a simple flow to kick things off:

Set up your Google Cloud project

Enable Kubernetes Engine API

Install gcloud CLI and Kubernetes command-line tool (kubectl)

Create a GKE cluster via console or command line

Deploy your app using Kubernetes manifests or Helm

Monitor, scale, and manage using GKE dashboard, Cloud Monitoring, and Cloud Logging

If you're using GKE Autopilot, Google manages your node infrastructure automatically—so you only manage your apps.

“Don’t let infrastructure slow your growth. Let GKE scale as you scale.”

🔗 Must-Read Resources to Kickstart GKE

👉 GKE Quickstart Guide – Google Cloud

👉 Best Practices for GKE – Google Cloud

👉 Anthos and GKE Integration

👉 GKE Autopilot vs Standard Clusters

👉 Google Cloud Kubernetes Learning Path – NetCom Learning

🧠 Real-World GKE Success Stories

A FinTech startup used GKE Autopilot to run microservices with zero infrastructure overhead

A global media company scaled video streaming workloads across continents in hours

A university deployed its LMS using GKE and reduced downtime by 80% during peak exam seasons

"You don’t need a huge ops team to build a global app. You just need GKE."

🎯 Final Thoughts

Getting started with Google Kubernetes Engine is like unlocking a fast track to modern app delivery. Whether you're running 10 containers or 10,000, GKE gives you the tools, automation, and scale to do it right.

With Google Cloud’s ecosystem—from Cloud Build to Artifact Registry to operations suite—GKE is more than just Kubernetes. It’s your platform for innovation.

“Containers are the future. GKE is the now.”

So fire up your first cluster. Launch your app. And let GKE do the heavy lifting while you focus on what really matters—shipping great software.

Let me know if you’d like this formatted into a visual infographic or checklist to go along with the blog!

[krnetwork]

EX280: Red Hat OpenShift Administration

Red Hat OpenShift Administration is a vital skill for IT professionals interested in managing containerized applications, simplifying Kubernetes, and leveraging enterprise cloud solutions. If you’re looking to excel in OpenShift technology, this guide covers everything from its core concepts and prerequisites to advanced certification and career benefits.

1. What is Red Hat OpenShift?

Red Hat OpenShift is a robust, enterprise-grade Kubernetes platform designed to help developers build, deploy, and scale applications across hybrid and multi-cloud environments. It offers a simplified, consistent approach to managing Kubernetes, with added security, automation, and developer tools, making it ideal for enterprise use.

Key Components of OpenShift:

OpenShift Platform: The foundation for scalable applications with simplified Kubernetes integration.

OpenShift Containers: Allows seamless container orchestration for optimized application deployment.

OpenShift Cluster: Manages workload distribution, ensuring application availability across multiple nodes.

OpenShift Networking: Provides efficient network configuration, allowing applications to communicate securely.

OpenShift Security: Integrates built-in security features to manage access, policies, and compliance seamlessly.

2. Why Choose Red Hat OpenShift?

OpenShift provides unparalleled advantages for organizations seeking a Kubernetes-based platform tailored to complex, cloud-native environments. Here’s why OpenShift stands out among container orchestration solutions:

Enterprise-Grade Security: OpenShift Security layers, such as role-based access control (RBAC) and automated security policies, secure every component of the OpenShift environment.

Enhanced Automation: OpenShift Automation enables efficient deployment, management, and scaling, allowing businesses to speed up their continuous integration and continuous delivery (CI/CD) pipelines.

Streamlined Deployment: OpenShift Deployment features enable quick, efficient, and predictable deployments that are ideal for enterprise environments.

Scalability & Flexibility: With OpenShift Scaling, administrators can adjust resources dynamically based on application requirements, maintaining optimal performance even under fluctuating loads.

Simplified Kubernetes with OpenShift: OpenShift builds upon Kubernetes, simplifying its management while adding comprehensive enterprise features for operational efficiency.

3. Who Should Pursue Red Hat OpenShift Administration?

A career in Red Hat OpenShift Administration is suitable for professionals in several IT roles. Here’s who can benefit:

System Administrators: Those managing infrastructure and seeking to expand their expertise in container orchestration and multi-cloud deployments.

DevOps Engineers: OpenShift’s integrated tools support automated workflows, CI/CD pipelines, and application scaling for DevOps operations.

Cloud Architects: OpenShift’s robust capabilities make it ideal for architects designing scalable, secure, and portable applications across cloud environments.

Software Engineers: Developers who want to build and manage containerized applications using tools optimized for development workflows.

4. Who May Not Benefit from OpenShift?

While OpenShift provides valuable enterprise features, it may not be necessary for everyone:

Small Businesses or Startups: OpenShift may be more advanced than required for smaller, less complex projects or organizations with a limited budget.

Beginner IT Professionals: For those new to IT or with minimal cloud experience, starting with foundational cloud or Linux skills may be a better path before moving to OpenShift.

5. Prerequisites for Success in OpenShift Administration

Before diving into Red Hat OpenShift Administration, ensure you have the following foundational knowledge:

Linux Proficiency: Linux forms the backbone of OpenShift, so understanding Linux commands and administration is essential.

Basic Kubernetes Knowledge: Familiarity with Kubernetes concepts helps as OpenShift is built on Kubernetes.

Networking Fundamentals: OpenShift Networking leverages container networks, so knowledge of basic networking is important.

Hands-On OpenShift Training: Comprehensive OpenShift training, such as the OpenShift Administration Training and Red Hat OpenShift Training, is crucial for hands-on learning.

Read About Ethical Hacking

6. Key Benefits of OpenShift Certification

The Red Hat OpenShift Certification validates skills in container and application management using OpenShift, enhancing career growth prospects significantly. Here are some advantages:

EX280 Certification: This prestigious certification verifies your expertise in OpenShift cluster management, automation, and security.

Job-Ready Skills: You’ll develop advanced skills in OpenShift deployment, storage, scaling, and troubleshooting, making you an asset to any IT team.

Career Mobility: Certified professionals are sought after for roles in OpenShift Administration, cloud architecture, DevOps, and systems engineering.

7. Important Features of OpenShift for Administrators

As an OpenShift administrator, mastering certain key features will enhance your ability to manage applications effectively and securely:

OpenShift Operator Framework: This framework simplifies application lifecycle management by allowing users to automate deployment and scaling.

OpenShift Storage: Offers reliable, persistent storage solutions critical for stateful applications and complex deployments.

OpenShift Automation: Automates manual tasks, making CI/CD pipelines and application scaling efficiently.

OpenShift Scaling: Allows administrators to manage resources dynamically, ensuring applications perform optimally under various load conditions.

Monitoring & Logging: Comprehensive tools that allow administrators to keep an eye on applications and container environments, ensuring system health and reliability.

8. Steps to Begin Your OpenShift Training and Certification

For those seeking to gain Red Hat OpenShift Certification and advance their expertise in OpenShift administration, here’s how to get started:

Enroll in OpenShift Administration Training: Structured OpenShift training programs provide foundational and advanced knowledge, essential for handling OpenShift environments.

Practice in Realistic Environments: Hands-on practice through lab simulators or practice clusters ensures real-world application of skills.

Prepare for the EX280 Exam: Comprehensive EX280 Exam Preparation through guided practice will help you acquire the knowledge and confidence to succeed.

9. What to Do After OpenShift DO280?

After completing the DO280 (Red Hat OpenShift Administration) certification, you can further enhance your expertise with advanced Red Hat training programs:

a) Red Hat OpenShift Virtualization Training (DO316)

Learn how to integrate and manage virtual machines (VMs) alongside containers in OpenShift.

Gain expertise in deploying, managing, and troubleshooting virtualized workloads in a Kubernetes-native environment.

b) Red Hat OpenShift AI Training (AI267)

Master the deployment and management of AI/ML workloads on OpenShift.

Learn how to use OpenShift Data Science and MLOps tools for scalable machine learning pipelines.

c) Red Hat Satellite Training (RH403)

Expand your skills in managing OpenShift and other Red Hat infrastructure on a scale.

Learn how to automate patch management, provisioning, and configuration using Red Hat Satellite.

These advanced courses will make you a well-rounded OpenShift expert, capable of handling complex enterprise deployments in virtualization, AI/ML, and infrastructure automation.

Conclusion: Is Red Hat OpenShift the Right Path for You?

Red Hat OpenShift Administration is a valuable career path for IT professionals dedicated to mastering enterprise Kubernetes and containerized application management. With skills in OpenShift Cluster management, OpenShift Automation, and secure OpenShift Networking, you will become an indispensable asset in modern, cloud-centric organizations.

KR Network Cloud is a trusted provider of comprehensive OpenShift training, preparing you with the skills required to achieve success in EX280 Certification and beyond.

Why Join KR Network Cloud?

 With expert-led training, practical labs, and career-focused guidance, KR Network Cloud empowers you to excel in Red Hat OpenShift Administration and achieve your professional goals.

https://creativeceo.mn.co/posts/the-ultimate-guide-to-red-hat-openshift-administration

https://bogonetwork.mn.co/posts/the-ultimate-guide-to-red-hat-openshift-administration

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Orchestrate HPC Systems

One of the key concepts of cloud computing is Orchestration. It refers to overseeing the deployment, running, and monitoring of all the components of an application in the cluster. Additionally, an orchestrator can perform other tasks like healing (managing errors), scaling, and logging. Orchestrators like the well-known Kubernetes or Mesos can access cloud cluster resources directly by…