Azure MLOps Course Overview
The Azure MLOps Course Overview provides essential insights into integrating machine learning and operations using Microsoft Azure. This course is vital for data scientists, machine learning engineers, and DevOps professionals, enabling them to streamline deployment, monitoring, and management of ML models. Enhancing collaboration and efficiency, it equips teams with the necessary skills for successful MLOps practices.
Course outline & what you'll learn
- Definition and Importance of MLOps
- MLOps Lifecycle Overview
Overview of Azure Machine Learning Services
- Setting Up Azure Environment for ML
- Importance of Versioning in ML
- Using Git and Azure Repos
- CI/CD Concepts in MLOps
- Implementing CI/CD Pipelines in Azure
- Tracking and Managing Experiments
- Using Azure Machine Learning for Experimentation
- Training Models at Scale
- Hyperparameter Tuning with Azure
- Deployment Options: Real-Time vs Batch
- Deploying Models as Web Services
- Model Monitoring Techniques
- Ensuring Compliance and Governance in ML
- Strategies for Model Retraining
- Managing Model Versions and Retirement
- Practical Exercises Using Azure Machine Learning
- Case Studies and Industry Applications
Why train with Traincrest
This Microsoft course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.