MLOps Courses and Certifications

Databricks

MLOps on Azure Databricks with MLflow

1 review

Explore MLOps on Azure Databricks with MLflow, focusing on Model Registry, Webhooks, and automated pipelines for effective machine learning model lifecycle management and deployment.

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

Demystifying Machine Learning Operations (MLOps)

50 ratings at Pluralsight

Learn to implement MLOps practices throughout the machine learning lifecycle, from infrastructure setup to model development and deployment, enhancing collaboration, traceability, and monitoring.

• Pluralsight
• 2 hours 15 minutes
• On-Demand
• Free Trial Available

Pragmatic AI Labs

Python Fundamentals for MLOps

Master Python for Seamless MLOps: From Fundamentals to Automation and Workflow Efficiency.

• edX
• 5 weeks, 3-6 hours a week
• On-Demand
• Free Online Course (Audit)

Pragmatic AI Labs

DevOps, DataOps, MLOps

Streamline AI Operations: Leverage DevOps, DataOps, and MLOps for End-to-End Machine Learning Solutions.

• edX
• 4 weeks, 3-6 hours a week
• On-Demand
• Free Online Course (Audit)

Pragmatic AI Labs

MLOps Platforms: Amazon SageMaker and Azure ML

Elevate Your MLOps Game: Master AWS SageMaker and Azure ML for Production-Ready AI Solutions.

• edX
• 4 weeks, 3-6 hours a week
• On-Demand
• Free Online Course (Audit)

Pragmatic AI Labs

Building AI with Bedrock Agent

Develop enterprise-grade AI applications using AWS Bedrock and Python. Master MLOps, IaC, DataOps, and UI creation for generative AI solutions in this hands-on program.

• edX
• 4 weeks, 4-6 hours a week
• On-Demand
• Free Online Course (Audit)

Pragmatic AI Labs

Cloud Machine Learning Engineering and MLOps

Unleash the power of machine learning with engineering best practices, AutoML, and emerging technologies.

• edX
• 4 weeks, 3-6 hours a week
• On-Demand
• Free Online Course (Audit)

Open Data Science

Git-Based CI-CD for ML

Learn to implement Git-based MLOps for continuous delivery, automating workflows, and enabling collaboration. Explore open-source tools and hosted ML platforms with a live demo on streamlining ML pipelines.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Open Data Science

Making MLOps Uncool Again

Learn to build an automated MLOps workflow using Git, GitHub, and open-source tools, covering the entire ML model lifecycle from data labeling to prediction monitoring.

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

Data Professor

MLOps 101 - A Practical Tutorial on Creating a Machine Learning Project with DagsHub

Step-by-step tutorial on using DagsHub for machine learning projects, covering project creation, environment setup, remote storage configuration, version control, experiment tracking, and hypothesis exploration.

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

MLOPS

Learn MLOps fundamentals, implementation, deployment, and continuous integration using tools like MLFlow in this comprehensive program.

• YouTube
• 10 hours
• On-Demand
• Free Video

Data Science Dojo

Putting MLOps into Practice

Streamline your machine learning lifecycle with MLOps best practices, from project initiation to model deployment and monitoring. Learn practical implementation strategies and reference architectures.

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

Pragmatic AI Labs

MLOps Masterclass - Theory to DevOps to Cloud-Native to AutoML

Comprehensive guide to MLOps: from theory to implementation, covering DevOps, cloud-native solutions, and AutoML. Learn to build, deploy, and manage machine learning systems across various platforms and technologies.

• YouTube
• 2 hours 36 minutes
• On-Demand
• Free Video

Pragmatic AI Labs

MLOps Platforms From Zero - Databricks, MLFlow-MLRun-SKLearn

Comprehensive guide to building MLOps platforms using Databricks, AutoML, SKLearn, and AWS. Covers end-to-end process from project setup to model deployment, including CI/CD, experimentation, and containerization.

• YouTube
• 2 hours 26 minutes
• On-Demand
• Free Video

Data Science Dojo

MLOps Crash Course for Beginners

Learn to create and deploy reproducible MLOps pipelines, covering key concepts like continuous training, drift detection, and model deployment for real-world data science value beyond Jupyter notebooks.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video