MLOps on Azure: From Data Science to Deployment Course Overview
The 'MLOps on Azure: From Data Science to Deployment Course' empowers data scientists, machine learning engineers, and IT professionals to streamline model deployment and operations on Azure. This course is vital for enhancing collaboration, optimizing workflows, and ensuring scalable AI solutions, equipping participants with essential skills to integrate machine learning into real-world applications effectively.
Course outline & what you'll learn
- Introduction to MLOps
- Definition and importance of MLOps
Overview of the MLOps lifecycle
- Setting Up Your Environment
- Azure Machine Learning workspace setup
- Tools and technologies for MLOps on Azure
- Data Preparation and Management
- Data ingestion and cleaning
- Dataset versioning and management
- Model Development
- Selecting and training machine learning models
- Hyperparameter tuning and optimization
- Continuous Integration and Continuous Deployment (CI/CD)
- Implementing CI/CD pipelines for machine learning
- Tools for automating workflows in Azure
- Model Deployment
- Deploying models as web services
- Model endpoint management
- Monitoring and Maintaining Models
- Implementing monitoring solutions
- Model retraining and updating strategies
- Governance and Compliance
- Ensuring compliance in machine learning workflows
- Best practices for model governance
- Case Studies and Real-world Applications
Examples of MLOps implementations on Azure
- Lessons learned and key takeaways
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.