AI Model deployment on AWS

This course teaches you how to deploy ML models on AWS using real-world tools and strategies. You'll explore AWS services and learn how to choose the right deployment strategy. Through practical cases, you'll also master rollout strategies like canary, blue/green, and shadow deployments, and ensure your models are production-ready, secure, and responsive at scale.

1 hour of content 17 students
Start for Free

What you get:

  • 1 hour of content
  • 21 Interactive exercises
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

AI Model deployment on AWS

A course by Stefan Micic
Start for Free

What you get:

  • 1 hour of content
  • 21 Interactive exercises
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement
Start for Free

What you get:

  • 1 hour of content
  • 21 Interactive exercises
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • What AWS services can we use for deployment
  • What strategies can we use to deploy ML models
  • How to optimize cost for their ML deployments

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

In today’s fast-paced machine learning landscape, building a great model is only half the battle—getting it into production quickly, reliably, and securely is where real impact happens. This course is designed to teach you exactly that.

You’ll learn how to deploy machine learning models on AWS, using real-world tools and workflows tailored for both small-scale and enterprise-grade applications. We’ll cover a range of deployment options—from lightweight and serverless setups with AWS Lambda, to managed ML services like Amazon SageMaker, containerized solutions with ECS + Fargate, and large-scale infrastructure using Amazon EKS and EC2.

But this course doesn’t stop at “how to deploy.” You’ll also learn:

  • When and why to choose a specific service (e.g., latency-sensitive APIs vs. batch processing)
  • How to manage traffic using blue/green, canary, and shadow deployments
  • How to ensure scalability with autoscaling policies and serverless concurrency
  • How to monitor, secure, and roll back deployments effectively
  • How to integrate deployments into CI/CD pipelines for seamless automation

Through case studies, hands-on walkthroughs, and deployment strategy deep-dives, you’ll develop the skills to move models into production with confidence—whether you're building a quick prototype, scaling a SaaS platform, or working in a high-stakes regulated industry.

By the end of this course, you'll be able to:

  • Select the most appropriate AWS service for your deployment needs
  • Implement scalable, cost-effective, and secure ML inference endpoints
  • Apply progressive rollout strategies to mitigate risk
  • Automate and monitor deployments in a cloud-native, production-grade environment

Curriculum

  • 1. Introduction to AI Deployment
    2 Lessons 2 Min
    Introduction to the course
    1 min
    What will be covered in this course
    1 min
  • 2. Serverless Model Deployment Options on AWS
    6 Lessons 16 Min
    Introduction to Serverless Model Deployment
    3 min
    Introduction to AWS Lambda for lightweight models
    3 min
    Hands on: Deploy Lambda by coding it in UI Read now
    2 min
    Hands on: Deploy Lambda from blueprint Read now
    1 min
    Hands on: Deploy AWS Lambda using Containers Read now
    3 min
    ECS + Fargate for custom container deployment Read now
    4 min
  • 3. Long Running AI Model Serving
    4 Lessons 13 Min
    Section Introduction
    2 min
    Deployment with EC2 Read now
    6 min
    Deployment with ECS + EC2 Read now
    3 min
    Deployment with EKS
    2 min
  • 4. Sagemaker Deployment
    5 Lessons 11 Min
    Introduction to SageMaker Deployment
    2 min
    Real-time endpoints Read now
    3 min
    Batch Transform – Scalable Offline Inference Read now
    2 min
    Asynchronous Inference with SageMaker Read now
    3 min
    Serverless Inference Read now
    1 min
  • 5. Cost Management for AI Deployment
    4 Lessons 10 Min
    Endpoint auto-scaling Read now
    4 min
    Using spot instances for batch jobs Read now
    3 min
    Part 1: Deploy SageMaker endpoint using autoscaling policy Read now
    2 min
    Part 2: Deploy SageMaker endpoint using autoscaling policy Read now
    1 min
  • 6. A/B Testing on AWS
    5 Lessons 15 Min
    Introduction to A/B Testing in ML Model Deployment Read now
    2 min
    Strategies for A/B Testing ML Models
    4 min
    Example: General A/B Testing Setup (Conceptual) Read now
    3 min
    A/B Testing with Amazon SageMaker Endpoints Read now
    2 min
    Configuring Multiple Models on One Endpoint Read now
    4 min
  • 7. Other deployment strategies
    6 Lessons 13 Min
    Introduction to Blue/Green Deployment Read now
    3 min
    Blue/Green Deployment on SageMaker Read now
    1 min
    Canary Deployment Read now
    3 min
    Canary Rollout on SageMaker Read now
    2 min
    When to use what? Read now
    1 min
    Bonus: Shadow Deployment
    3 min
  • 8. Outro
    2 Lessons 4 Min
    What have we learned in this course?
    3 min
    Thanks for watching!
    1 min

Topics

MLOps conceptsMLOpsAWSCloud ComputingAIMachine and Deep Learning

Tools & Technologies

theory

Course Requirements

  • Basic understanding of Cloud
  • Understanding of Machine Learning

Who Should Take This Course?

Level of difficulty: Intermediate

  • MLOps enthusiasts
  • ML Engineers
  • MLOps professionals
  • Data Scientists

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Stefan Micic

Stefan Micic

Founder at

3 Courses

17 Reviews

305 Students

Stefan Mićić is a seasoned MLOps and Data Engineer with nearly a decade of experience in machine learning, MLOps, and data engineering. His expertise spans across MLOps automation, infrastructure design, model optimization, and deployment of AI solutions at scale. With a Master’s degree in AI and a strong foundation in computer science, Stefan has worked across multiple industries, building end-to-end machine learning pipelines, optimizing deep learning models, and integrating DevOps best practices into ML workflows. His work has involved: MLOps & Cloud Infrastructure – Designing and implementing scalable MLOps pipelines on AWS, Azure, and Kubernetes using Terraform, SageMaker, and other cloud-native tools. Model Deployment & Optimization – Specializing in deep learning model optimization, inference acceleration, and cost reduction strategies, including ONNX, quantization, and containerization. LLM & AI Engineering – Deploying LLMs and computer vision models in production, with expertise in tools like OpenAI GPT, Falcon, LLaMA, and federated learning techniques. CI/CD & Automation – Implementing automated CI/CD pipelines for machine learning models, ensuring seamless integration and retraining with tools like MLflow, GitHub Actions, and Docker. Data Engineering & Big Data – Building ETL pipelines, working with Snowflake, PySpark, and Databricks, and designing data workflows for large-scale ML applications. Leadership & Mentorship – Leading ML teams, mentoring engineers, designing technical roadmaps, and participating in hiring and pre-sales activities. His work is driven by a passion for streamlining ML workflows through MLOps, making AI more scalable, cost-effective, and production-ready.

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