Machine Learning Engineer Masterclass

Get hands-on experience of an ML Engineer in a 8-week Online program led by ML Tech Leads from top companies like Google and Meta. From designing scalable ML systems to productionizing models, solve real-world problems that you can showcase on your career portfolio.

Next Kick Off: June 06

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Our 8-Week Curriculum

Our class is 90-minutes on Zoom from 4:30 to 6:00 P.M. PST / 7:30 to 9:00 P.M. EST every Tuesday, Thursday and Friday for the first 7 weeks. On week 8, we have one 90-minute class where you showcase your capstone project.

ML System Design Introduction

1 week - 3 classes

Learn to think like an ML Engineer. We will start by teaching you how to scope real-world business problems that require ML solutions and teach you how to architect scalable ML solutions.

Build a Recommender System

2 weeks - 6 classes

We will give you a real-world business case that requires a scalable recommender system. We will then teach you the math of the latest models then we will teach you how to code the model using Tensorflow and productionize the model.

Build a Large Language Model ChatBot

2 weeks - 6 classes

We will teach you how to build your own LLM chatbot like ChatGPT from scratch. You will learn the math behind core components (e.g. Transformer) and code the model. We will then show you how to deploy your solution to the cloud in the form of a model API.

Build a Face Recognition System

2 weeks - 6 classes

We will teach you how to build a scalable face recognition system by covering the math and code of computer vision algorithms and walk through model deployment.

Complete a Capstone Project

1 week - 1 class

The final week is the capstone project week where you and peer group will choose an end-to-end ML project to build. You will present your solution in the final class and receive the AI Edge Certification upon delivery.

Our instructors

Damien Benveniste

After a PhD in theoretical physics, I started my Machine Learning career 10 years ago. I have been a Data Scientist, Machine Learning Engineer and Software Engineer. Until recently, I was a Machine Learning Tech Lead at Meta on the automation at scale of model optimization for Ads ranking.

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Abhishek Kumar

Former Software Engineer at Google, Research Scientist at Facebook and Technical Lead/engineering manager at Meta. I enjoy helping businesses make the best out of their data and have been an ML practitioner for over 10 years. I look forward to helping you all on your ML journey!

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Dan Lee

For the past 7 years, I have been an ML practitioner with experiences in leading ML projects at a startup and Google. I am now an ML instructor who will teach you the ins-and-outs of model development and deployment scalable ML systems. I look forward to working with you all!

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Upskill in ML Engineering: $1597

Enroll now

Our first class starts at 4:30 P.M. to 6:00 P.M. PST / 7:30 P.M. to 9:00 P.M. EST on June 06, 2023 (Tuesday). We will meet at the same time every Tuesday, Wednesday and Friday for the first 7 weeks. Then, we will end our final week (week 8) with one class of capstone presentations on Thursday, July 27.

What's included:

  • 33 hours of live classes

  • 4 end-to-end ML projects

  • 3 MLE instructors

  • Certification

  • Best practices in ML engineering

  • Cohort-based learning

  • Slack community access

  • Alumni community

Frequently asked questions

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email at and we'll get back to you as soon as we can.

Am I ready for the masterclass?

This course is an advanced course in ML. We expect that you have had 1-year of professional experience in ML or completed a university degree in machine learning and/or data science.

What is the pre-requisite knowledge for the masterclass?

We expect that you already grasp ML fundamentals (e.g. the variance and bias trade-off), Python coding and neural network basics (e.g. back propagation).

How advanced in this course?

We will teach best practices in ML engineering in three major problem areas - recommender system, large language model, computer vision - and teach you ML system design and productionization.

How much math do I need to know?

The portion of the course will be dedicated on deconstructing the mathematics of SOTAs like Two-Tower Model, Transformer, and Reinforcement Learning. We expect that you have a base knowledge on how a neural network model works.

How much coding do I need to know?

We expect that you have at least 1-year experience in Python programming. Having a basic knowledge in TensorFlow is a plus as we will use this to develop and productionize models.

What is the tech stack covered in the course?

We will use Python, TensorFlow, Colab, Docker, Cloud (e.g. AWS), FastAPI.

I won't be able to attend some of the class, will the class be recorded? And, how long is the access?

Yes, the class will be recorded, and you will have access to it for a year.

What goes on in the Slack group?

You will gain access to community of instructors, your peers in current masterclass and alumni to exchange ideas and upskill in ML engineering together.

What happens after I purchase?

You will receive an onboarding content, including Slack group access and Zoom invites to your classes.

Can I get a company reimbursement?

Yes, we will send you an invoice that enables you to reimburse the purchase.

Is it possible to pay in installments?

Absolutely, we provide a payment plan that allows you to pay in 3 monthly installments.

How do I get the AiEdge certification?

After completion of the Capstone project on week 8, you will receive the AiEdge Certification which you can display on your professional portfolio (e.g. resume, LinkedIn).