Experiment Tracking Courses and Certifications

Introduction to Data Versioning with DVC

Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

• DataCamp
• 3 hours
• On-Demand
• Free Trial Available

Evaluating and Debugging Generative AI

Learn to evaluate and debug generative AI using MLOps tools. Master experiment tracking, data versioning, and team collaboration with Weights & Biases platform for enhanced productivity in AI projects.

• Coursera
• 1 hour 30 minutes
• On-Demand
• Paid Course

MLOps.community

Experiment Tracking in the Age of LLMs - MLOps Podcast

Explore experiment tracking in the age of LLMs with Neptune's CEO. Gain insights on managing ML experiments, prompt engineering, and achieving control and confidence in model development.

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

Building a Reproducible Model Workflow

Learn to build efficient, reproducible machine learning pipelines using MLflow, pytest, and Weights & Biases. Master data validation, experiment tracking, and model deployment for real-world ML projects.

• Udacity
• 4 weeks, 5-6 hours a week
• On-Demand
• Paid Course

PyTorch for Deep Learning Bootcamp

3806 ratings at Udemy

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

• Udemy
• 2 days 4 hours 14 minutes
• On-Demand
• Paid Course

Introduction to MLflow

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Fully Automated MLOps

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

MLOps Concepts

26 ratings at DataCamp

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

• DataCamp
• 2 hours
• On-Demand
• Free Trial Available

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

Getting Started with MLflow

29 ratings at Pluralsight

Efficiently delivering machine learning products is not easy, therefore good tools that support ML model development are needed. This course will teach you MLflow.

• Pluralsight
• 1 hour 22 minutes
• On-Demand
• Free Trial Available

Databricks

Simplifying Model Development and Management with MLflow - New Features and Innovations

Explore MLflow's latest features for simplified experiment tracking, improved model portability, schema management, and faster deployment in machine learning lifecycle management.

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

Databricks

Managing the Complete Machine Learning Lifecycle with MLflow - Introduction to MLflow Tracking

Introduction to MLflow: tracking experiments, packaging projects, deploying models, and managing lifecycle. Learn to record, reproduce, and compare ML runs using MLflow's components and UI.

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

Prodramp

Smart ML Experiment Tracking and Model Registry with Neptune.AI Platform

Explore Neptune.ai for ML experiment tracking and model registry. Learn to log, compare, and organize model metadata efficiently, enhancing productivity and reproducibility in machine learning projects.

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

Open Data Science

Deploying Optimized Deep Learning Pipelines

Survey of deep learning pipeline optimization techniques, exploring quantization, distillation, and math library selection. Discusses trade-offs for deployment and provides insights for efficient ML implementation.

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

Databricks

Data Versioning and Reproducible ML with DVC and MLflow

Learn to enhance ML reproducibility using DVC and MLflow. Explore data versioning, model tracking, and artifact management for efficient ML development and deployment.

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