The MLOps Engineering on AWS Training course helps organisations automate the development, training, deployment, and operational management of machine learning models using AWS services and modern MLOps practices. By combining DevOps principles with Machine Learning Operations (MLOps), the course promotes effective collaboration between data engineers, data scientists, developers, and operations teams.

Participants will learn how to design and automate end-to-end machine learning workflows using Amazon SageMaker, AWS CodeBuild, Apache Airflow, Kubernetes, and other MLOps technologies. Key topics include model development, Continuous Training, CI/CD for Machine Learning, model deployment, performance monitoring, Data Drift and Model Drift detection, governance, and production-grade ML operations.

Through hands-on labs and real-world scenarios, learners will gain practical experience building scalable, secure, and reliable machine learning pipelines while applying industry best practices for managing ML workloads in production environments.

We can organize this training at your preferred date and location.

Prerequisites

Required

AWS Technical Essentials
DevOps Engineering on AWS or equivalent experience
Practical Data Science with Amazon SageMaker or equivalent experience

Who Should Attend

This course is designed for:

MLOps Engineers
Machine Learning Engineers
ML Platform Engineers
Data Engineers
DevOps Engineers
Cloud Engineers
Data Scientists
Technical teams responsible for operationalising machine learning models

What You Will Learn

By the end of this course, participants will be able to:

Explain the principles of Machine Learning Operations (MLOps).
Understand the key differences between DevOps and MLOps.
Design end-to-end machine learning workflows.
Build automated ML pipelines using Amazon SageMaker.
Automate model training, testing, deployment, and retraining.
Implement model packaging and deployment strategies.
Monitor machine learning models in production.
Detect Data Drift and Model Drift.
Monitor and mitigate Model Bias.
Implement Human-in-the-Loop AI review processes.

Training Outline

Day 1

Module 0: Welcome

Course Introduction
AWS AI & ML Ecosystem Overview
MLOps Fundamentals

Module 1: Introduction to MLOps

Machine Learning Operations Fundamentals

What is MLOps?
MLOps Goals
Data, Code, and Model Management
Enterprise AI Operations

From DevOps to MLOps

DevOps vs MLOps
Machine Learning Lifecycle
MLOps Workflows
Cross-Team Collaboration

MLOps Use Cases

Production AI Systems
Enterprise Machine Learning Projects
Operational Challenges and Solutions

Module 2: MLOps Development

Building Machine Learning Models

Model Development
Model Training
Model Evaluation
Security and Governance

Automation and Workflow Management

Apache Airflow
Kubernetes Integration
Workflow Automation
CI/CD for Machine Learning

Amazon SageMaker for MLOps

Amazon SageMaker
SageMaker Pipelines
Automated ML Lifecycle Management

Hands-On Labs

Bring Your Own Algorithm (BYOA)
Building MLOps Pipelines
Serving Models with AWS CodeBuild

MLOps Action Plan Workshop

Organisational MLOps Strategy
Transformation Planning

Day 2

Module 3: MLOps Deployment

Model Deployment

Deployment Operations
Model Packaging
Version Control

Inference Strategies

Batch Inference
Real-Time Inference
Asynchronous Inference

SageMaker Production Variants

Canary Deployments
Blue/Green Deployments
Shadow Deployments

Edge AI Deployment

Deploying ML Models to Edge Devices
Edge AI Use Cases

Hands-On Labs

Production Deployment
A/B Testing
Deployment Validation

Day 3

Module 4: Model Monitoring and Operations

Monitoring Machine Learning Models

Performance Monitoring
Observability
Operational Metrics

Data Drift and Model Drift

Detecting Data Changes
Model Performance Degradation
Drift Management Strategies

Monitoring Model Bias

Fair AI Principles
Bias Detection and Mitigation

Amazon SageMaker Monitoring Tools

Amazon SageMaker Model Monitor
Model Registry
Feature Store
Pipeline Observability

Human-in-the-Loop AI

Human Review Processes
Model Validation Workflows

Hands-On Labs

Pipeline Troubleshooting
Model Monitoring
Drift Detection
Bias Analysis

Module 5: Wrap-Up

Course Review
Enterprise MLOps Strategy
Best Practices
Future of AI Operations

Why Choose Us

Experience MLOps Engineering on AWS through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

Delivery Method: Online Instructor-Led
Participation Model: Public / Private (In-house)
Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
Worldwide Access: Join our live virtual classrooms from anywhere in the world or arrange a dedicated online training program for your organization.

Experience MLOps Engineering on AWS in a professional face-to-face classroom environment. Classroom Based training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class exclusively for your organization.

Delivery Method: Classroom Based
Participation Model: Public / Private (In-house)
Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange MLOps Engineering on AWS as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

Delivery Method: Onsite
Participation Model: Private (In-house)
Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
Worldwide Onsite Delivery: Bilginç IT Academy trainers can travel internationally to deliver dedicated training programs at your preferred location.

Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

MLOps Engineering on AWS Training

Prerequisites

Required

Recommended

Who Should Attend

What You Will Learn

Training Outline

Day 1

Module 0: Welcome

Module 1: Introduction to MLOps

Machine Learning Operations Fundamentals

From DevOps to MLOps

MLOps Use Cases

Module 2: MLOps Development

Building Machine Learning Models

Automation and Workflow Management

Amazon SageMaker for MLOps

Hands-On Labs

MLOps Action Plan Workshop

Day 2

Module 3: MLOps Deployment

Model Deployment

Inference Strategies

SageMaker Production Variants

Edge AI Deployment

Hands-On Labs

Day 3

Module 4: Model Monitoring and Operations

Monitoring Machine Learning Models

Data Drift and Model Drift

Monitoring Model Bias

Amazon SageMaker Monitoring Tools

Human-in-the-Loop AI

Hands-On Labs

Module 5: Wrap-Up

Why Choose Us