4.83
Course Overview
Learning Options
Schedule Dates
MLOps on Azure: From Data Science to Deployment
MLOps on Azure: From Data Science to Deployment
MLOps on Azure: From Data Science to Deployment
MLOps on Azure: From Data Science to Deployment
Course Content
- Identify and analyze business requirements for a machine learning solution
- Design a machine learning solution architecture
- Select appropriate compute resources for a machine learning solution
- Provision Azure resources for a machine learning solution
- Manage data storage for machine learning workloads
- Implement security and access controls for machine learning resources
- Explore and prepare data for machine learning
- Select and apply appropriate data featurization techniques
- Select and train appropriate machine learning models
- Evaluate and compare machine learning models
- Optimize machine learning models for performance
- Package and deploy machine learning models
- Implement model governance and lifecycle management
- Monitor and troubleshoot machine learning models
- Manage and optimize machine learning pipelines
- Implement responsible AI principles
- Deploy machine learning models to production environments
- Implement continuous integration and continuous delivery (CI/CD) for machine learning solutions
- Monitor and manage deployed machine learning models
- Retrain and update machine learning models
- Troubleshoot and debug deployed machine learning models
- What is MLOps?
- The benefits of MLOps
- Key MLOps concepts
- Introduction to CI/CD tools: Azure DevOps & GitHub
- Azure Boards
- Azure Repos & GitHub
- Azure Pipeline (Build & Release) and GitHub Actions
- Introduction to Infrastructure as a Code (IaaC) in Azure Pipeline
- Azure Artifacts
- Creating an Azure Machine Learning workspace
- Connecting to your workspace
- Setting up a Git repository
- Creating a GitHub Actions workflow
- Triggering your workflow
- Monitoring your workflow
- Creating a branch protection rule
- Using branch policies
- Enabling required reviews
- Setting up continuous integration (CI)
- Running code checks
- Configuring code coverage
- Creating and managing environments
- Training and testing models
- Deploying models to Azure
- Creating a deployment workflow
- Testing your deployment
- Monitoring your deployment
- ntroduction and Structure of Accelerator Solutions
- Supported Machine Learning Patterns
- Deployment of ML Solution using
FAQs
Basic knowledge of machine learning concepts and experience with Microsoft Azure are recommended. Familiarity with programming (preferably in Python) and cloud computing concepts will also be beneficial.
The course is divided into several modules covering:
- Developing ML Models: Using Azure ML service to build and train models.
- Version Control: Managing different versions of ML models.
- Model Validation: Strategies for validating model performance.
- Retraining Pipelines: Creating pipelines for model retraining.
- Model Deployment: Deploying models into production.
- Model Monitoring: Monitoring models for performance and reliability.
Yes, the course includes practical hands-on labs and projects that provide experience in applying the concepts learned to real-world scenarios.
You will learn to streamline the ML Life Cycle using Azure’s MLOps capabilities. This includes developing, validating, deploying, and monitoring ML models, as well as managing model versions and creating retraining pipelines.
To enroll, visit the CounselTrain website and follow the registration process. For additional assistance, you can contact our support team.
MLOps is valuable across various industries, including finance, healthcare, technology, and manufacturing. It helps organizations manage and operationalize ML models efficiently, leading to improved decision-making and business outcomes.
Duration
Language
Batch Options
Upcoming Batch
Soft copy of Courseware
Certificate of Course Complition
Experienced and Professional Trainer
Lab and Exercises During Training 
Sample Questions for Exam Prepration
Mock Test on the Last Day of Training 
Exam Registration Guidance 
5-Star Training Environmment