#MLOps

[kamalkafir-blog]

CAMHS Practitioner - Unscheduled Care

Job title: CAMHS Practitioner – Unscheduled Care Company: NHS Job description: Reference number 050-NMR129-0225-A Job locations Royal Alexandra Hospital Marine Drive Rhyl LL18 3AS… Act. Knowledge of relevant national strategies. Knowledge of Information and Technology Communication Knowledge of the… Expected salary: £37898 – 45637 per year Location: Rhyl, Denbighshire Job date: Fri, 27 Jun 2025…

[the-ml-engineer]

Hey people,

I am here to share my everyday learnings. I plan to put atleast an hour a day starting from 29th November 2024 and document my learning on here. All of your support will be appreciated.. Highly motivated 🙏🏻🙏🏻

[innoappstech]

Why Your First AI Hire Could Make or Break Your Startup in 2025

The AI revolution is no longer a prediction—it’s the present. Every startup wants to integrate artificial intelligence, automate smarter, and deliver personalized user experiences. But while many founders are racing to plug in AI features, few realize that the success of those features depends on one critical thing: your first AI hire.

In 2025, almost every developer’s resume mentions experience with ChatGPT, LLMs, or ML pipelines. But the gap between “I built a demo using GPT-4” and “I deployed a production-ready AI system that scaled with thousands of users” is massive.

If you're building an AI-driven product, you don’t need someone who just “understands AI”—you need someone who understands your business problem and can translate that into scalable solutions.

Here’s what you’ll learn in our in-depth blog:

What sets real AI developers apart: From ML fundamentals to data engineering, LLM fine-tuning, and model deployment—what skills matter in production environments.

Red flags to watch for when hiring: Why resumes filled with buzzwords or GitHub projects aren't always proof of real-world ability.

Hiring strategy in phases: Why generalists often work better at MVP stage and when it’s worth bringing in specialists like MLOps engineers or data scientists.

Team-building vs. outsourcing: Whether to build your team in-house or collaborate with an experienced AI partner to accelerate delivery.

This is the kind of clarity most founders miss—until it’s too late.

If you’re planning to build with AI this year, read the full breakdown here: https://innoapps.com/blog/hiring-ai-developers-in-2025

Don’t just hire talent. Hire strategically.

#AI #ArtificialIntelligence #MachineLearning #Founders #TechStartups #StartupTips #InnoApps #MLOps #AIRecruitment #BuildBetter

[meraakidesigns]

𝐘𝐨𝐮𝐫 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐈𝐬𝐧’𝐭 𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐀𝐧𝐲𝐦𝐨𝐫𝐞—𝐈𝐭’𝐬 𝐭𝐡𝐞 𝐁𝐫𝐚𝐢𝐧. Once upon a time, backends served code. Today, they serve context. Gone are the days when AI was bolted on like a plugin. Now it’s infused—woven through the very infrastructure that powers your product. 𝐖𝐞'𝐫𝐞 𝐰𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧 𝐭𝐞𝐚𝐦𝐬 𝐟𝐥𝐢𝐩 𝐭𝐡𝐞 𝐬𝐜𝐫𝐢𝐩𝐭: • Moving from monoliths to modular—so AI runs where it works best • Deploying models with FastAPI & TensorFlow to cut latency by 40%+ • Using event-driven backends to make real-time decisions feel native • Automating pipelines with MLOps—turning weeks into minutes • Building with governance-first thinking, not governance-later panic And what’s the payoff? AI that adapts. Infra that scales. Experiences that feel like magic—but run on precision. 𝐀 𝐛𝐞𝐭𝐭𝐞𝐫 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐥𝐨𝐚𝐝 𝐟𝐚𝐬𝐭—𝐢𝐭 𝐭𝐡𝐢𝐧𝐤𝐬 𝐟𝐚𝐬𝐭. Building with AI in mind? 𝐋𝐞𝐭’𝐬 𝐭𝐚𝐥𝐤 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐚𝐭 𝐥𝐞𝐚𝐫𝐧𝐬 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮. 🔗https://meraakidesigns.com/

[itcourseslearninglearninghub]

📊 Future-Proof Your Career with Full Stack Data Science & AI! 🤖

The demand for skilled Data Scientists and AI professionals is skyrocketing. Get ahead with the Full Stack Data Science & AI program by Mr. Prakash Senapathi, starting July 22 at 7:00 PM IST — a comprehensive course designed to turn you into an industry-ready professional.

👨‍🏫Learn directly from an expert with real-world industry experience. This program blends foundational concepts with modern tools and real-time projects to ensure hands-on learning.

🚀 Course Highlights:

Python, NumPy, Pandas, Matplotlib, Seaborn

Advanced Machine Learning Algorithms

Deep Learning using Keras & TensorFlow

AI Concepts: NLP, Computer Vision, Chatbots, and Speech Recognition

SQL, Data Warehousing, Power BI & Tableau Visualizations

Cloud Deployment using AWS, Azure, and GCP basics

Streamlit Dashboards & Flask App APIs

Exploratory Data Analysis (EDA) & Feature Engineering

Model Optimization, Cross-Validation & Hyperparameter Tuning

GitHub Workflow, Version Control & MLOps with CI/CD

Capstone Projects with Resume and LinkedIn Portfolio Review

Time Series Forecasting & Recommender Systems

Data Ethics, Bias Mitigation & Explainable AI (XAI)

🎯 Suitable for:

Graduates, Working Professionals, and Tech Enthusiasts

Anyone aiming to pivot into AI, ML, or Data Analytics

Business Analysts transitioning into tech roles

Developers seeking a transition to ML/AI-focused jobs

Entrepreneurs aiming to use AI in real-world products

💡 Extras:

Real-world datasets from finance, healthcare & e-commerce

Weekly mini-projects and quizzes

Lifetime access to recorded sessions + Certification

Mock technical interviews, resume guidance & job referrals

Supportive learner community for networking & Q&A

Tools walkthrough: Jupyter, Colab, GitHub, Docker Basics

Special module on AI Trends: GenAI, ChatGPT APIs, and AutoML

🔗 Register Now: https://tr.ee/4DF2gi 🎓 All Free Demos: https://linktr.ee/ITcoursesFreeDemos

[sapientsapiens]

MLOps Zoomcamp Capstone Project

Starting the capstone project for the #mlopszoomcamp @DataTalksClub putting together everything taught in the course. If time permits (only 9 days left) would opt for a Maturity model of 4; if not, would have to be content with building the project for a maturity model of 3.

[pythonjobsupport]

MLOps: Principles, Pipelines, and Practices

The provided sources collectively explain MLOps (Machine Learning Operations), detailing its core principles, benefits, and … source

[kamalkafir-blog]

BMW New Car Sales Executive

Job title: BMW New Car Sales Executive Company: BMW Group Retail Job description: on, one weekend off. IT literate – knowledge of MS Office & CRM systems preferred. Rewards Working Pattern: 5-day schedule… a premium BMW at a reduced cost. Specialist training with BMW UK + BMW Accreditation. Genuine career progression… Expected salary: Location: Warrington, Cheshire Job date: Sun, 29 Jun 2025…

[aditisingh01]

Uncovering the Real ROI: How Data Science Services Are Driving Competitive Advantage in 2025

Introduction

What if you could predict your customer’s next move, optimize every dollar spent, and uncover hidden growth levers—all from data you already own? In 2025, the real edge for businesses doesn’t come from owning the most data, but from how effectively you use it. That’s where data science services come in.

Too often, companies pour resources into data collection and storage without truly unlocking its value. The result? Data-rich, insight-poor environments that frustrate leadership and slow innovation. This post is for decision-makers and analytics leads who already know the fundamentals of data science but need help navigating the growing complexity and sophistication of data science services.

Whether you’re scaling a data team, outsourcing to a provider, or rethinking your analytics strategy, this blog will help you make smart, future-ready choices. Let’s break down the trends, traps, and tangible strategies for getting maximum impact from data science services.

Section 1: The Expanding Scope of Data Science Services in 2025

Gone are the days when data science was just about modeling customer churn or segmenting audiences. Today, data science services encompass everything from real-time anomaly detection to predictive maintenance, AI-driven personalization, and prescriptive analytics for operational decisions.

Predictive & Prescriptive Modeling: Beyond simply forecasting, top-tier data science service providers now help businesses simulate outcomes and optimize strategies with scenario analysis.

AI-Driven Automation: From smart inventory management to autonomous marketing, data science is fueling a new level of automation.

Real-Time Analytics: With the rise of edge computing and faster data streams, businesses expect insights in seconds, not days.

Embedded Analytics: Service providers are helping companies build intelligence directly into products, not just dashboards.

These services now touch nearly every business function—HR, operations, marketing, finance—with increasingly sophisticated tools and technologies.

Section 2: Choosing the Right Data Science Services Partner

Selecting the right partner is arguably more critical than the tools themselves. A good fit ensures strategic alignment, faster time to value, and fewer rework cycles.

Domain Expertise: Don’t just look for technical brilliance. Look for providers who understand your industry’s unique metrics, workflows, and regulations.

Tech Stack Compatibility: Ensure your provider is fluent in your existing environment—whether it’s Snowflake, Databricks, Azure, or open-source tools.

Customization vs. Standardization: The best data science services strike a balance between reusable IP and tailored solutions.

Transparency & Collaboration: Look for vendors who co-build with your internal teams, not just ship over-the-wall solutions.

Real-World Example: A retail chain working with a generic vendor struggled with irrelevant models. Switching to a vertical-focused data science services provider with retail-specific datasets improved demand forecasting accuracy by 22%.

Section 3: Common Pitfalls That Derail Data Science Projects

Despite strong intent, many data science initiatives still fail to deliver ROI. Here are common traps and how to avoid them:

Lack of a Clear Business Goal: Many teams jump into modeling without aligning on the problem statement or success metrics.

Dirty or Incomplete Data: If your foundational data layers are unstable, no algorithm can fix the problem.

Overemphasis on Accuracy: A highly accurate model that no one uses is worthless. Focus on adoption, interpretability, and stakeholder buy-in.

Skills Gap: Without a strong bridge between data scientists and business users, insights never make it into workflows.

Solution: The best data science services include data engineers, business translators, and UI/UX designers to ensure end-to-end delivery.

Section 4: Unlocking Hidden Opportunities with Advanced Analytics

In 2025, forward-thinking firms are using data science services not just for problem-solving, but for uncovering growth levers:

Customer Lifetime Value Optimization: Predictive models that help decide how much to spend and where to focus retention.

Dynamic Pricing: Real-time adjustment based on demand, inventory, and competitor moves.

Fraud Detection & Risk Management: ML models can now flag anomalies within seconds, preventing millions in losses.

ESG & Sustainability Metrics: Data science is enabling companies to report and optimize environmental and social impact.

Real-World Use Case: A logistics firm used data science services to optimize delivery routes using real-time weather, traffic, and vehicle condition data, reducing fuel costs by 19%.

Section 5: How to Future-Proof Your Data Science Strategy

Data science isn’t a one-time investment—it’s a moving target. To remain competitive, your strategy must evolve.

Invest in Data Infrastructure: Cloud-native platforms, version control for data, and real-time pipelines are now baseline requirements.

Prioritize Model Monitoring: Drift happens. Build feedback loops to track model accuracy and retrain when needed.

Embrace Responsible AI: Ensure fairness, explainability, and data privacy compliance in all your models.

Build a Culture of Experimentation: Foster a test-learn-scale mindset across teams to embrace insights-driven decision-making.

Checklist for Evaluating Data Science Service Providers:

Do they offer multi-disciplinary teams (data scientists, engineers, analysts)?

Can they show proven case studies in your industry?

Do they prioritize ethics, security, and compliance?

Will they help upskill your internal teams?

Conclusion

In 2025, businesses can’t afford to treat data science as an experimental playground. It’s a strategic function that drives measurable value. But to realize that value, you need more than just data scientists—you need the right data science services partner, infrastructure, and mindset.

When chosen wisely, these services do more than optimize KPIs—they uncover opportunities you didn’t know existed. Whether you’re trying to grow smarter, serve customers better, or stay ahead of risk, the right partner can be your unfair advantage.

If you’re ready to take your analytics game from reactive to proactive, it may be time to evaluate your current data science service stack.

[nareshitechnologiesofficial]

📊 Future-Proof Your Career with Full Stack Data Science & AI! 🤖

The demand for skilled Data Scientists and AI professionals is skyrocketing. Get ahead with the Full Stack Data Science & AI program by Mr. Prakash Senapathi, starting July 22 at 7:00 PM IST — a comprehensive course designed to turn you into an industry-ready professional.

👨‍🏫 Learn directly from an expert with real-world industry experience. This program blends foundational concepts with modern tools and real-time projects to ensure hands-on learning.

🚀 Course Highlights:

Python, NumPy, Pandas, Matplotlib, Seaborn

Advanced Machine Learning Algorithms

Deep Learning using Keras & TensorFlow

AI Concepts: NLP, Computer Vision, and more

SQL, Data Warehousing, and Power BI Dashboards

Deployment on AWS, Flask, and Streamlit

End-to-End Capstone Projects with Resume Guidance

Exploratory Data Analysis (EDA) & Feature Engineering

Model Optimization, Cross-Validation & Hyperparameter Tuning

Data Versioning, GitHub Workflow, and MLOps Basics

🎯 Suitable for:

Graduates, Working Professionals, and Tech Enthusiasts

Anyone aiming to pivot into AI, ML, or Data Analytics

Analysts seeking a deeper tech foundation for career growth

💡 Extras:

Hands-on projects with real datasets

Cloud deployment walkthroughs

Lifetime session access + Certification

Mock interviews & portfolio reviews

🔗 Register Now: https://tr.ee/4DF2gi 🎓 All Free Demos: https://linktr.ee/ITcoursesFreeDemos

[macro-pulse]

7. Builder’s Playbook: Never Repeat Grok 4.0

Seven safeguards to bolt on today—before the trolls do.

Write immutable extremist blocks at the infra level—no prompt can override.

Separate staging from live posting. Human eye before social feed.

Dual-key toggles for safety flags; no lone engineer flips.

Realtime anomaly alarms (burst of hate keywords > threshold).

Transparent but delayed prompt publishing—audit logs without zero-day leaks.

Shadow mode releases for major prompt shifts.

Incident drills with comms team—speed matters.

Ship these, sleep better.

Sources: TechCrunch, Jul 9 2025; Guardian, Jul 9 2025; Business Insider, Feb 2025. :contentReference[oaicite:12]{index=12}

Follow, reblog, stay safe—more teardown threads coming.

[labellerr-ai-tool]

Scaling Machine Learning Operations with Modern MLOps Frameworks

The rise of business-critical AI demands sophisticated operational frameworks. Modern end to end machine learning pipeline frameworks combine ML best practices with DevOps, enabling scalable, reliable, and collaborative operations.

MLOps Framework Architecture

Experiment management and artifact tracking

Model registry and approval workflows

Pipeline orchestration and workflow management

Advanced Automation Strategies

Continuous integration and testing for ML

Automated retraining and rollback capabilities

Multi-stage validation and environment consistency

Enterprise-Scale Infrastructure

Kubernetes-based and serverless ML platforms

Distributed training and inference systems

Multi-cloud and hybrid cloud orchestration

Monitoring and Observability

Multi-dimensional monitoring and predictive alerting

Root cause analysis and distributed tracing

Advanced drift and business impact analytics

Collaboration and Governance

Role-based collaboration and cross-functional workflows

Automated compliance and audit trails

Policy enforcement and risk management

Technology Stack Integration

Kubeflow, MLflow, Weights & Biases, Apache Airflow

API-first and microservices architectures

AutoML, edge computing, federated learning

Conclusion

Comprehensive end to end machine learning pipeline frameworks are the foundation for sustainable, scalable AI. Investing in MLOps capabilities ensures your organization can innovate, deploy, and scale machine learning with confidence and agility.

[impact-newswire]

Bitdeer AI Wins 2025 AI Breakthrough Award for MLOps Innovation

SINGAPORE, July 01, 2025 (GLOBE NEWSWIRE) — Bitdeer AI, part of Bitdeer Technologies Group (NASDAQ: BTDR) and a fast-growing AI neocloud platform, is proud to announce that it has been presented with the MLOps Innovation Award by AI Breakthrough. The 2025 AI Breakthrough Awards, now in their eighth year, are presented by AI Breakthrough, a leading market intelligence organization that recognizes…

[kamalkafir-blog]

Generative AI Solutions Architect

Job title: Generative AI Solutions Architect Company: EXL Service Job description: to be essential for the role: Programming & Libraries: Deep proficiency in Python and extensive experience with relevant AI/ML/NLP… experience in technology consulting or a client-facing technical specialist role within a technology provider is highly… Expected salary: Location: United Kingdom Job date: Wed, 28 May…

[sapientsapiens]

MLOps Zoomcamp Module 6

Starting Module 6 of #mlopszoomcamp @DataTalksClub regarding best practices in MLOps like:

unit & functional testing with pyetest,

integration test with docker-compose,

cloud testing with LocalStack,

code quality improve with linting & formatting,

Git pre-commit hooks,

Makefiles and make

Infrastructure-as-Code

Continuous Integration/ Continuous Deployment with Github Actions