Experiment Tracking Courses and Certifications

Weights & Biases

W&B Models End-to-End Demo - Complete ML Experiment Tracking and Model Management

Explore W&B Models for end-to-end ML lifecycle management, from training metrics and GPU monitoring to hyperparameter tuning, artifact tracking, and automated evaluations.

• YouTube
• 16 minutes
• On-Demand
• Free Video

PyTorch for Deep Learning Bootcamp

5870 ratings at Udemy

Learn PyTorch. Become a Deep Learning Engineer. Get Hired.

• Udemy
• 2 days 4 hours 13 minutes
• On-Demand
• Paid Course

MLflow in Action - Master the art of MLOps using MLflow tool

1054 ratings at Udemy

A master guide to unleash the full potential of MLflow to optimize MLOps. Streamline MLOps workflows using MLflow tool

• Udemy
• 9 hours 15 minutes
• On-Demand
• Paid Course

End-to-End Machine Learning: From Idea to Implementation

425 ratings at Udemy

Build, Manage, and Deploy Machine Learning (AI) Projects with Python and MLOps

• Udemy
• 1 day 11 hours 56 minutes
• On-Demand
• Paid Course

Coursera Microcredential

LLM Optimization & Evaluation

Master the complete lifecycle of LLM optimization through hands-on experience with production-ready techniques, evaluation methods, cost reduction, and deployment strategies.

• Coursera
• 4 weeks, 10 hours a week
• On-Demand
• Paid Course
• 12 courses

Learn MLOps for Machine Learning

Discover how to streamline ML workflows with DVC, MLFlow, and AWS tools for tracking experiments, versioning data, and automating model deployment processes.

• Coursera
• 6 hours 39 minutes
• On-Demand
• Paid Course

Coursera

Databricks Machine Learning Fundamentals

Master production-grade machine learning on Databricks with hands-on experience in data preparation, MLflow tracking, and model deployment for real-world applications.

• Coursera
• 3 hours 53 minutes
• On-Demand
• Paid Course

Coursera

Automate, Evaluate and Deploy ML Models Confidently

Master production-grade MLOps by building automated CI/CD pipelines with GitHub Actions, analyzing Optuna trials, and implementing validation gates for confident model deployment.

• Coursera
• 2 hours 49 minutes
• On-Demand
• Paid Course

Coursera

Track and Evaluate ML Model Experiments

Master systematic ML experiment tracking with DVC and Weights & Biases to build reproducible, auditable workflows that eliminate "it worked on my machine" problems.

• Coursera
• 2 hours 57 minutes
• On-Demand
• Paid Course

Pragmatic AI Labs

Machine Learning with Databricks and MLflow

Master MLflow and Databricks to build, track, and deploy production-ready machine learning models while solving the reproducibility crisis through structured experiment tracking.

• Coursera
• 4 hours 32 minutes
• On-Demand
• Paid Course

Coursera

ML: Build, Train, Justify Models

Master practical ML workflows: transform business problems into ML tasks, train logistic regression, random forest, and gradient boosting models, and justify selections using bias-variance reasoning.

• Coursera
• 3 hours 4 minutes
• On-Demand
• Paid Course

KodeKloud

ML Model Development and Tracking: Hands-on Guide

Master MLOps "Middle Loop" with MLflow tracking, model versioning, and production deployment through hands-on insurance claim automation project.

• Coursera
• 3 hours 3 minutes
• On-Demand
• Paid Course

MLOps Essentials: Model Development and Integration

383 ratings at LinkedIn Learning

Get started with MLOps Concepts for Model Development and Integration, to organize machine learning (ML) development and deliver scalable and reliable ML products.

• LinkedIn Learning
• 1 hour 31 minutes
• On-Demand
• Free Trial Available

PyCon US

Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit

Explore building a customizable ML experiment tracking system using DVC for versioning and Streamlit for data exploration, focusing on practical implementation with Python APIs.

• YouTube
• 33 minutes
• On-Demand
• Conference Talk

End-to-End Machine Learning Project: From EDA to MLOps Integration

Master AI and MLOps through hands-on EDA, feature engineering, model implementation, and integration of tools like ZenML & MLflow for experiment tracking and deployment.

• freeCodeCamp
• 2 hours 48 minutes
• On-Demand
• Free Video