MLOps Engineering on AWS - Automate and Scale Machine Learning Workflows
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MLOps Engineering on AWS

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MLOps Engineering on AWS is a hands-on course that teaches you how to operationalize machine learning workflows using AWS services. It covers key concepts such as automating model deployment, monitoring, scaling, and governance. With tools like Amazon SageMaker, you’ll learn to streamline the ML lifecycle and ensure reliable, secure, and efficient production-level ML operations.

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Overview

In order to create, train, and implement machine learning (ML) models, this course expands and builds upon the DevOps methodology, which is widely used in software development. The four-level MLOPs maturity structure serves as the foundation for the training. The first three levels—the initial, repeatable, and reliable levels—are the main emphasis of the course. For ML deployments to be successful, the course emphasizes the significance of data, model, and code. It illustrates how to overcome the difficulties posed by handoffs between data scientists, data engineers, software developers, and operations by utilizing tools, automation, procedures, and collaboration. Using tools and procedures to keep an eye on and respond when the model prediction in production deviates from established KPIs is another topic covered in the course. 

Key Features

  • 1. MLOps Best Practices: Learn to operationalize and automate machine learning workflows.
  • 2. AWS Tools for MLOps: Use services like Amazon SageMaker for deployment, monitoring, and scaling ML models.
  • 3. Model Deployment: Automate and manage reliable model deployment pipelines.
  • 4. Monitoring and Scaling: Monitor model performance and scale solutions for production environments.
  • 5. Lifecycle Automation: Automate the entire ML lifecycle, from training to deployment.
  • 6. Governance and Security: Implement robust governance and security practices for ML operations.
  • 7. Hands-On Labs: Gain practical experience through real-world MLOps scenarios.
  • 8. Production-Ready Solutions: Build scalable and efficient ML pipelines ready for production.
  • 1. MLOps Best Practices: Learn to operationalize and automate machine learning workflows.
  • 2. AWS Tools for MLOps: Use services like Amazon SageMaker for deployment, monitoring, and scaling ML models.
  • 3. Model Deployment: Automate and manage reliable model deployment pipelines.
  • 4. Monitoring and Scaling: Monitor model performance and scale solutions for production environments.
  • 5. Lifecycle Automation: Automate the entire ML lifecycle, from training to deployment.
  • 6. Governance and Security: Implement robust governance and security practices for ML operations.
  • 7. Hands-On Labs: Gain practical experience through real-world MLOps scenarios.
  • 8. Production-Ready Solutions: Build scalable and efficient ML pipelines ready for production.

Curriculum

Study Plan

1Module 1: Introduction to MLOps
  • Processes 
  • People 
  • Technology
  • Security and governance 
  • MLOps maturity model 
1Module 1: Introduction to MLOps
  • Processes 
  • People 
  • Technology
  • Security and governance 
  • MLOps maturity model 
2Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

Bringing MLOps to experimentation 

Setting up the ML experimentation environment 

Creating and Updating a Lifecycle Configuration for SageMaker Studio

Provisioning a SageMaker Studio Environment with the AWS Service Catalog 

Workbook: Initial MLOps 

2Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

Bringing MLOps to experimentation 

Setting up the ML experimentation environment 

Creating and Updating a Lifecycle Configuration for SageMaker Studio

Provisioning a SageMaker Studio Environment with the AWS Service Catalog 

Workbook: Initial MLOps 

3Module 3: Repeatable MLOps: Repositories
  • Managing data for MLOps
  • Version control of ML models
  • Code repositories in ML 
3Module 3: Repeatable MLOps: Repositories
  • Managing data for MLOps
  • Version control of ML models
  • Code repositories in ML 
4Module 4: Repeatable MLOps: Orchestration
  • ML pipelines
  • Using SageMaker Pipelines to Orchestrate Model Building Pipelines 
  • End-to-end orchestration with AWS Step Functions
  • Automating a Workflow with Step Functions
  • End-to-end orchestration with SageMaker Projects
  • Standardizing an End-to-End ML Pipeline with SageMaker Projects
  • Using third-party tools for repeatability
  • Exploring Human-in-the-Loop During Inference
  • Governance and security
  • Exploring Security Best Practices for SageMaker 
  • Workbook: Repeatable MLOps 
4Module 4: Repeatable MLOps: Orchestration
  • ML pipelines
  • Using SageMaker Pipelines to Orchestrate Model Building Pipelines 
  • End-to-end orchestration with AWS Step Functions
  • Automating a Workflow with Step Functions
  • End-to-end orchestration with SageMaker Projects
  • Standardizing an End-to-End ML Pipeline with SageMaker Projects
  • Using third-party tools for repeatability
  • Exploring Human-in-the-Loop During Inference
  • Governance and security
  • Exploring Security Best Practices for SageMaker 
  • Workbook: Repeatable MLOps 
5Module 5: Reliable MLOps: Scaling and Testing
  • Scaling and multi-account strategies
  • Testing and traffic-shifting 
  • Demonstration: Using SageMaker Inference Recommender 
  • Testing Model Variants 
5Module 5: Reliable MLOps: Scaling and Testing
  • Scaling and multi-account strategies
  • Testing and traffic-shifting 
  • Demonstration: Using SageMaker Inference Recommender 
  • Testing Model Variants 
6Module 6: Reliable MLOps: Monitoring
  • The importance of monitoring in ML
  • Monitoring a Model for Data Drift 
  • Operations considerations for model monitoring
  • Remediating problems identified by monitoring ML solutions 
  • Workbook: Reliable MLOps
  • Building and Troubleshooting an ML Pipeline 
6Module 6: Reliable MLOps: Monitoring
  • The importance of monitoring in ML
  • Monitoring a Model for Data Drift 
  • Operations considerations for model monitoring
  • Remediating problems identified by monitoring ML solutions 
  • Workbook: Reliable MLOps
  • Building and Troubleshooting an ML Pipeline 

About Instructor

  • 3000+ Learner Trained
  • 42+ Corporate Recruiting Partners & 20+ College Partners
  • 1000+ Review
  • 200+ Classes/Month
  • aws solution architect certified
  • 3000+ Learner Trained
  • 42+ Corporate Recruiting Partners & 20+ College Partners
  • 1000+ Review
  • 200+ Classes/Month
  • aws solution architect certified
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