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.

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

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 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
Topics
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.

Meet Your Instructor

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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