Deploy AI to AWS, GCP, Azure, Vercel with MLOps, Bedrock, SageMaker, RAG, Agents, MCP: scalable, secure and observable

See my LinkedIn announcement here about the origin of this course – it all started with an argument! https://www.linkedin.com/feed/update/urn:li:activity:7376253585229029377/

This is a BIG course.

A few months ago, I asked all my 200,000 students which course they would most like me to make next. AI in Production is what the people voted for! In fact, more voted for this course than all the rest put together.

And, wow, do I have a BANGER of a course for you!!

By the end of it, you’ll be able to deliver full stack AI applications to Vercel, AWS, GCP and Azure; you’ll be comfortable working with Docker containers, Terraform and Github Actions. We go deep on AWS and you’ll be confident with many AWS services, but in particular Lambda, App Runner and of course Bedrock and SageMaker, along with Amazon Bedrock AgentCore.

You’ll be able to build RAG pipelines, and deploy multi-agent systems including MCP. You’ll have core MLOps expertise. You’ll be equipped to deploy SaaS applications with user management and subscriptions. Most importantly, you’ll do it at enterprise-grade, positioned for scalability, security, resiliency, monitoring and observability.

Somehow, we do all of that! And, in four weeks! But you’ll need to roll up sleeves: there’s tons of substantive work to be done. It will be exciting, and a little grueling in places, but always immensely satisfying — particularly when your AI product is live in production.

Key links including Repo, Setup and Slides

• The course itself on Udemy is linked here, along with my companion courses and how they fit together
• The main Github repo for the course with the README
• The other repos for weeks 3 and 4 are linked in that repo
• Slides for the course
• Common FAQ for my courses
• Other links: my Live Events and the Proficient AI Engineer program

And if you wish: please connect with me on LinkedIn, follow me on X, subscribe to me on YouTube, and register below! All the multi-modal forms of me 😂

Important – updating your code each week

I regularly push updates to the labs, including more tips, business applications and exercises. Please do pull from Github regularly to get the latest code – instructions are in the guides folder for those new to Git.

The definitive answer to the most common first question

People from a non-Data Science background often ask me a great question: so what exactly are these “parameters” that we keep hearing about?? I’ve made this short video playlist to explain what they are, and how they give GPT its super-powers, followed by a peek inside GPT.

Contributing to the repo

Many students to my courses have contributed their own solutions and extensions to the repo. I’m incredibly grateful! I love seeing your progress and innovative ideas, and it adds value for everyone else on the course. As an added benefit, you get recognition in GitHub as a contributor to the repo.

For this course, I suggest we do community_contributions a bit differently, as it will be hard to include complete repos within our repo. Instead:

1. Make your own repo with your deployment
2. Within the course production repo, within the community_contributions folder, make a new markdown file or notebook file with your name and project
3. In this file, describe your project, link to your repo, and include a screenshot, and if you have time, some experiences to share
4. Submit a PR! See the Github guide in the guides folder if you’re unsure

I will then merge in your contribution, with much thanks!

And please take a look at other students’ contributions.

Finally

Somehow you made it all the way to the end of the resources — thank you! If you’re not completely fed up with me by now, then please connect with me on LinkedIn! I’d love to stay in touch and I’m always available if you have feedback, questions or ideas.