#MLflow

[damilola-doodles]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[dammyanimation]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-ai-automation]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-warrior-mindset]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-moyo]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[nschool]

The Best Open-Source Tools for Data Science in 2025

Data science in 2025 is thriving, driven by a robust ecosystem of open-source tools that empower professionals to extract insights, build predictive models, and deploy data-driven solutions at scale. This year, the landscape is more dynamic than ever, with established favorites and emerging contenders shaping how data scientists work. Here’s an in-depth look at the best open-source tools that are defining data science in 2025.

1. Python: The Universal Language of Data Science

Python remains the cornerstone of data science. Its intuitive syntax, extensive libraries, and active community make it the go-to language for everything from data wrangling to deep learning. Libraries such as NumPy and Pandas streamline numerical computations and data manipulation, while scikit-learn is the gold standard for classical machine learning tasks.

NumPy: Efficient array operations and mathematical functions.

Pandas: Powerful data structures (DataFrames) for cleaning, transforming, and analyzing structured data.

scikit-learn: Comprehensive suite for classification, regression, clustering, and model evaluation.

Python’s popularity is reflected in the 2025 Stack Overflow Developer Survey, with 53% of developers using it for data projects.

2. R and RStudio: Statistical Powerhouses

R continues to shine in academia and industries where statistical rigor is paramount. The RStudio IDE enhances productivity with features for scripting, debugging, and visualization. R’s package ecosystem—especially tidyverse for data manipulation and ggplot2 for visualization—remains unmatched for statistical analysis and custom plotting.

Shiny: Build interactive web applications directly from R.

CRAN: Over 18,000 packages for every conceivable statistical need.

R is favored by 36% of users, especially for advanced analytics and research.

3. Jupyter Notebooks and JupyterLab: Interactive Exploration

Jupyter Notebooks are indispensable for prototyping, sharing, and documenting data science workflows. They support live code (Python, R, Julia, and more), visualizations, and narrative text in a single document. JupyterLab, the next-generation interface, offers enhanced collaboration and modularity.

Over 15 million notebooks hosted as of 2025, with 80% of data analysts using them regularly.

4. Apache Spark: Big Data at Lightning Speed

As data volumes grow, Apache Spark stands out for its ability to process massive datasets rapidly, both in batch and real-time. Spark’s distributed architecture, support for SQL, machine learning (MLlib), and compatibility with Python, R, Scala, and Java make it a staple for big data analytics.

65% increase in Spark adoption since 2023, reflecting its scalability and performance.

5. TensorFlow and PyTorch: Deep Learning Titans

For machine learning and AI, TensorFlow and PyTorch dominate. Both offer flexible APIs for building and training neural networks, with strong community support and integration with cloud platforms.

TensorFlow: Preferred for production-grade models and scalability; used by over 33% of ML professionals.

PyTorch: Valued for its dynamic computation graph and ease of experimentation, especially in research settings.

6. Data Visualization: Plotly, D3.js, and Apache Superset

Effective data storytelling relies on compelling visualizations:

Plotly: Python-based, supports interactive and publication-quality charts; easy for both static and dynamic visualizations.

D3.js: JavaScript library for highly customizable, web-based visualizations; ideal for specialists seeking full control.

Apache Superset: Open-source dashboarding platform for interactive, scalable visual analytics; increasingly adopted for enterprise BI.

Tableau Public, though not fully open-source, is also popular for sharing interactive visualizations with a broad audience.

7. Pandas: The Data Wrangling Workhorse

Pandas remains the backbone of data manipulation in Python, powering up to 90% of data wrangling tasks. Its DataFrame structure simplifies complex operations, making it essential for cleaning, transforming, and analyzing large datasets.

8. Scikit-learn: Machine Learning Made Simple

scikit-learn is the default choice for classical machine learning. Its consistent API, extensive documentation, and wide range of algorithms make it ideal for tasks such as classification, regression, clustering, and model validation.

9. Apache Airflow: Workflow Orchestration

As data pipelines become more complex, Apache Airflow has emerged as the go-to tool for workflow automation and orchestration. Its user-friendly interface and scalability have driven a 35% surge in adoption among data engineers in the past year.

10. MLflow: Model Management and Experiment Tracking

MLflow streamlines the machine learning lifecycle, offering tools for experiment tracking, model packaging, and deployment. Over 60% of ML engineers use MLflow for its integration capabilities and ease of use in production environments.

11. Docker and Kubernetes: Reproducibility and Scalability

Containerization with Docker and orchestration via Kubernetes ensure that data science applications run consistently across environments. These tools are now standard for deploying models and scaling data-driven services in production.

12. Emerging Contenders: Streamlit and More

Streamlit: Rapidly build and deploy interactive data apps with minimal code, gaining popularity for internal dashboards and quick prototypes.

Redash: SQL-based visualization and dashboarding tool, ideal for teams needing quick insights from databases.

Kibana: Real-time data exploration and monitoring, especially for log analytics and anomaly detection.

Conclusion: The Open-Source Advantage in 2025

Open-source tools continue to drive innovation in data science, making advanced analytics accessible, scalable, and collaborative. Mastery of these tools is not just a technical advantage—it’s essential for staying competitive in a rapidly evolving field. Whether you’re a beginner or a seasoned professional, leveraging this ecosystem will unlock new possibilities and accelerate your journey from raw data to actionable insight.

The future of data science is open, and in 2025, these tools are your ticket to building smarter, faster, and more impactful solutions.

[digitalmarketing6669]

MLflow: The Essential Tool Every Data Scientist

[lifetechweb]

Rastreamento de Large Language Models (LLM) com MLflow: Um guia completo

À medida que os Large Language Models (LLMs) crescem em complexidade e escala, rastrear seu desempenho, experimentos e implantações se torna cada vez mais desafiador. É a�� que o MLflow entra – fornecendo uma plataforma abrangente para gerenciar todo o ciclo de vida dos modelos de machine learning, incluindo LLMs. Neste guia aprofundado, exploraremos como aproveitar o MLflow para rastrear, avaliar…

[govindhtech]

MLflow Tracking Servers with managed MLflow on Amazon

We are excited to inform that a fully managed MLflow capability on Amazon SageMaker is now generally available. A popular open-source tool called MLflow is essential for assisting machine learning (ML) teams in handling the full ML lifecycle. Customers can now quickly and easily set up and operate MLflow Tracking Servers with only a few clicks thanks to this new introduction, which streamlines the procedure and increases productivity.

With MLflow, data scientists and machine learning developers may monitor several iterations of model training as runs within experiments, assess models, compare runs with visualisations, and add the top models to a Model Registry. With Amazon SageMaker, ML administrators can quickly and easily create secure, scalable MLflow environments on AWS by doing away with the monotonous heavy lifting involved in MLflow setup and management.

Essential elements of SageMaker’s managed MLflow Three essential elements form the foundation of SageMaker’s fully managed MLflow capability:

MLflow Tracking Server: Using the SageMaker Studio UI, you can quickly establish an MLflow Tracking Server. You may start effectively monitoring your machine learning experiments by using this standalone HTTP server, which provides numerous REST API endpoints for tracking runs and experiments. An additional option for finer-grained security customisation is to use the AWS Command Line Interface (AWS CLI). MLflow backend metadata store: This essential component of the MLflow Tracking Server is where all experiment, run, and artifact-related metadata is stored. This ensures thorough recording and management of your machine learning experiments and contains information such as experiment titles, run IDs, parameter values, metrics, tags, and artefact locations. The MLflow artefact store component offers a place to store all of the artefacts produced by machine learning experiments, including datasets, trained models, logs, and graphs. It provides a customer-managed AWS account for safely and effectively storing these artefacts using an Amazon Simple Storage Service (Amazon S3) bucket. Amazon SageMaker’s advantages with MLflow

Your machine learning workflows can be improved and streamlined by integrating Amazon SageMaker with MLflow:

Complete Experiment Monitoring: Monitor MLflow experiments in managed IDEs in SageMaker Studio, local IDEs, training jobs, processing tasks, and pipelines. Complete MLflow Functionality: Utilise all of MLflow’s experimental features, including MLflow Model Registry, MLflow Evaluations, and Tracking, to quickly compare and assess the outcomes of training iterations. Unified Model Governance: You may deploy MLflow models to SageMaker inference without the need to create special containers thanks to the unified model governance experience provided by models registered in MLflow, which automatically appear in the SageMaker Model Registry. Effective Server Management: Use the SageMaker Studio UI or APIs to provision, delete, and upgrade MLflow Tracking Servers as needed. Customers don’t need to handle the underlying infrastructure because SageMaker takes care of your tracking servers’ scaling, patching, and continuous maintenance. Enhanced Security: AWS Identity and Access Management (IAM) provides safe access to MLflow Tracking Servers. To provide strong security for your machine learning environments, create IAM policies that allow or prohibit access to particular MLflow APIs. Sufficient Oversight and Management: Amazon EventBridge and AWS CloudTrail can be used to monitor an MLflow Tracking Server’s behaviour in order to facilitate efficient Tracking Server governance. Prerequisites for the MLflow Tracking Server (environment setup)

Make a domain for SageMaker Studio With the new SageMaker Studio experience, you may establish a SageMaker Studio domain.

Set up the role for IAM execution To register models in SageMaker and read and publish artefacts to Amazon S3, the MLflow Tracking Server requires an IAM execution role. The Tracking Server execution role can be either created as a separate role or it can be used as the Studio domain execution role. For further information on the IAM position, see the SageMaker Developer Guide if you decide to build a new role for this. For information on what IAM policy the Studio domain execution role requires, see the SageMaker Developer Guide if you decide to upgrade it.

Set up the tracking server for MLflow Throughout the guide, you create an MLflow Tracking Server using the default options, which include the Tracking Server execution role (Studio domain execution role), the Tracking Server version (2.13.2), and the Tracking Server size (Small). We advise utilising a Small Tracking Server for teams of up to 25 users, as the size of the tracking server affects how much utilisation it can accommodate.

To begin, pick MLflow under Applications in your SageMaker Studio domain that was created during the environment setup previously described, then click Create.

Next, give the Tracking Server’s Name and Artefact store location (S3 URI).

An MLflow Tracking Server can take up to twenty-five minutes to set up.

Monitor and contrast training runs You’ll need your Tracking Server ARN, which was assigned during the creation process, and a Jupyter Notebook in order to begin recording metrics, parameters, and artefacts to MLflow. Training runs can be tracked using the MLflow SDK and compared with the MLflow UI.

The sagemaker-mlflow plugin is required to authenticate all MLflow API queries made by the MLflow SDK using AWS Signature V4 in order to register models from the MLflow Model Registry to the SageMaker Model Registry.

Enumerate potential models

The model whose metrics best fit your needs can be registered in the MLflow Model Registry once you’ve compared the several runs as explained in Step 4. A model’s registration suggests that it might be suitable for production deployment; additional testing will be required to confirm this suitability. You may deploy MLflow models to SageMaker inference by having them automatically appear in the SageMaker Model Registry after they have been registered in MLflow. This provides a consistent model governance experience. This makes it possible for data scientists who use MLflow primarily for testing to transfer their models to ML engineers, who utilise the SageMaker Model Registry to oversee and manage model deployments in production.

Pricing An MLflow Tracking Server will cost you money once it is built until you stop it or delete it. The amount of data logged to the tracking servers, the size chosen, and the length of time the servers have been operational all factor into the cost of the tracking servers. When not in use, Tracking Servers can be terminated using the SageMaker Studio UI or API to reduce expenses. See the Amazon SageMaker pricing for additional information on costs.

Currently accessible

With the exception of China and US GovCloud Regions, SageMaker with MLflow is typically accessible in all AWS Regions where SageMaker Studio is available. AWS cordially encourage you to investigate this new feature and see how it might improve the control and efficiency of your machine learning endeavours.

Read more on Govindhtech.com

[damilola-doodles]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[dammyanimation]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-ai-automation]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-warrior-mindset]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-moyo]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[thedebugdiary]

A Minimal Guide to Deploying MLflow 2.6 on Kubernetes

Introduction

Deploying MLflow on Kubernetes can be a straightforward process if you know what you're doing. This blog post aims to provide a minimal guide to get you up and running with MLflow 2.6 on a Kubernetes cluster. We'll use the namespace my-space for this example.

Prerequisites

A running Kubernetes cluster

kubectl installed and configured to interact with your cluster

Step 1: Create the Deployment YAML

Create a file named mlflow-minimal-deployment.yaml and paste the following content:

apiVersion: v1 kind: Namespace metadata: name: my-space --- apiVersion: apps/v1 kind: Deployment metadata: name: mlflow-server namespace: my-space spec: replicas: 1 selector: matchLabels: app: mlflow-server template: metadata: labels: app: mlflow-server name: mlflow-server-pod spec: containers: - name: mlflow-server image: ghcr.io/mlflow/mlflow:v2.6.0 command: ["mlflow", "server"] args: ["--host", "0.0.0.0", "--port", "5000"] ports: - containerPort: 5000 ---

apiVersion: v1 kind: Service metadata: name: mlflow-service namespace: my-space spec: selector: app: mlflow-server ports: - protocol: TCP port: 5000 targetPort: 5000

Step 2: Apply the Deployment

Apply the YAML file to create the deployment and service:

kubectl apply -f mlflow-minimal-deployment.yaml

Step 3: Verify the Deployment

Check if the pod is running:

kubectl get pods -n my-space

Step 4: Port Forwarding

To access the MLflow server from your local machine, you can use Kubernetes port forwarding:

kubectl port-forward -n my-space mlflow-server-pod 5000:5000

After running this command, you should be able to access the MLflow server at http://localhost:5000 from your web browser.

Step 5: Access MLflow within the Cluster

The cluster-internal URL for the MLflow service would be:

http://mlflow-service.my-space.svc.cluster.local:5000

You can use this tracking URL in other services within the same Kubernetes cluster, such as Kubeflow, to log your runs.

Troubleshooting Tips

Pod not starting: Check the logs using kubectl logs -n my-space mlflow-server-pod.

Service not accessible: Make sure the service is running using kubectl get svc -n my-space.

Port issues: Ensure that the port 5000 is not being used by another service in the same namespace.

Conclusion

Deploying MLflow 2.6 on Kubernetes doesn't have to be complicated. This guide provides a minimal setup to get you started. Feel free to expand upon this for your specific use-cases.