Experiment Tracking Courses and Certifications

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

1082 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
• Self-Paced
• Paid Course

Coursera Credential

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/week
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Paid Course

Coursera

Optimize ML Dev: Version, Reproduce, and Save

Master ML workflows with Git branching, Poetry environments, and resource monitoring to build reproducible, cost-efficient training pipelines that scale in real engineering teams.

• Coursera
• 3 hours 15 minutes
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Paid Course

Coursera

Evaluate and Create ML Workflows Visually

Master visual ML experiment evaluation using TensorBoard and refactor training code into reusable workflows with LightningModules and DataModules for real-world ML development.

• Coursera
• 1 hour 33 minutes
• Self-Paced
• Paid Course

Coursera

Optimizing AI Workflows and Deploying Edge Models

Master optimizing ML workflows with PyTorch, GPU acceleration, data pipelines, and edge deployment using TensorFlow Lite for real-world AI production environments.

• Coursera
• 7 hours 35 minutes
• Self-Paced
• Paid Course

Coursera

Databricks Machine Learning Quickstart

Master rapid ML deployment on Databricks by tracking experiments with MLflow, leveraging AutoML for model selection, and deploying production-grade serving endpoints with performance monitoring.

• Coursera
• 2 hours 59 minutes
• Self-Paced
• Paid Course

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

432 ratings at Udemy

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

• Udemy
• 1 day 11 hours 56 minutes
• Self-Paced
• Paid Course

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
• Self-Paced
• Conference Talk