Learn End-to-End ML Model Deployment with Amazon SageMaker  

Stand out from the crowd with this course by Luigi Patruno of ML in Production

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Check out our recent webinar about the course

Become an End-to-End ML Model Production Pro


Organize ML Experiments from end-to-end

Bring together software engineering, DevOps, and data engineering skills to manage ML experiments from end-to-end


Advance Your Career and Stand Out from the Crowd

Differentiate yourself from other ML practitioners by developing skills across the ML model deployment workflow  


Save Time Configuring and Focus on Improving Your Models

No more losing time to model configuration. Focus on improving your models and deploying them at scale

You Will Learn to Execute Real World End-to-End ML Experiments

Debug Machine Learning Model Training

Debugging Model Training

Train machine learning models using top frameworks like scikit-learn, XGBoost, Tensorflow, and PyTorch

Deploy Machine Learning Models

Deploying Models to Production Environments

Deploy trained models as API endpoints that automatically scale with demand

Scaling up Machine Learning Models

Scaling Up ML Inference

Perform batch inference on datasets and run large scale experiments like hyperparameter sweeps over a distributed cluster without any knowledge of EC2

Monitor Machine Learning Models

Monitoring Models in Production

Monitor deployed endpoints to detect concept drift and easily collect, store, and analyze data from experiments

Unfortunately, study group enrollment has closed. However, you may access the pre-recorded course using the link below. Enroll today and enhance your career! Use discount code TWIML to get an additional 10% off!

A six chapter pre-recorded version of the course with supporting lectures is available here for $199

This course is fantastic! It supplied our team with all the information needed to mature our machine learning process."

- James Walker, Sr. Software Engineer

"This course is a great starting point to get into AWS SageMaker. Luigi's step-by -step approach makes it a very effective course"

- Harini Kannan, Data Scientist

Your Study Group Journey:

Aug 8

Introduction to SageMaker

  • Challenges of Running Production Machine Learning Systems
  • What is Amazon SageMaker?
  • SageMaker Architecture Overview
  • Course Breakdown

Aug 15

Setting up your development environment

  • Introduction to SM Studio
  • Creating a Studio Instance from the AWS Console
  • Walk-thru of Studio and Jupyter Notebook
  • Connect to Git Repository Studio
  • Course Codebase

Aug 22

Interactive Model Training

  • Introduction to Model Training in SageMaker
  • Training an XGBoost model using Built-in Algorithms
  • Training a scikit-learn model using Pre-built Docker Images and Custom Code
  • Installing Custom python Requirements
  • Preprocessing Data with SageMaker Preprocessor

Aug 29

Experiment Management

  • Introduction to SageMaker Experiments
  • Understanding the SageMaker Experiments SDK
  • Running a single Trial Experiment with Tensorflow
  • Distributed Hyperparameter Optimization with PyTorch
  • Tracking Lineage: Finding the Best Model

Sept 5

Model Deployment

  • Introducing Model Deployment
  • Performing Batch Inference using SageMaker Batch Transform
  • Deploying Models as Persistent Endpoints for Online Inference
  • Automatically Scaling Deployed Models to Meet Demand
  • Cleaning Up Deployed Models

Sept 12

Model Monitoring

  • Introducing Model Monitoring
  • Capturing Live Data with a Deployed Model Endpoint
  • Generating Constraints and Suggestions from a Baseline dataset
  • Creating a Monitoring Schedule
  • Visualizing Drift by Comparing Data Distributions

Meet Luigi

I'm the Founder of MLinProduction.com.

I lead machine learning teams. In the past I've held roles as a data scientist, ML engineer, and data engineer. I've worked for large public companies and for tiny startups, taught graduate courses in data analysis and big data engineering, and have consulted for Fortune 100 companies.

I've had one consistent goal throughout my career: To build real machine learning systems that deliver massive amounts of business value. Now I'm teaching other ML practitioners how to do the same. I'm excited to be learning with you!

Luigi Patruno Machine Learning with Amazon SageMaker


How do I enroll in the course? Enrollment can be completed via the course enrollment section here. Enroll today and use the discount code TWIML to get an additional 10% off!

What will I learn in this course? The techniques taught in this course will help you build scalable, efficient, and fault-tolerant machine learning systems. You will learn to use Amazon SageMaker to:

  • Train machine learning models using top frameworks like scikit-learn, xgboost, tensorflow, and PyTorch
  • Run large scale experiments like hyperparameter sweeps over a distributed cluster without needing to know anything about EC2
  • Easily collect and analyze data from experiments.
  • Perform offline inference on a batch of data
  • Deploy models as persistent endpoints that automatically scale with demand.
  • Monitor deployed endpoints to detect concept drift.

What does this course include? The hands-on resources and materials to enhance your learning throughout this course include:

  • Condensed overview of the challenges of running production machine learning systems
  • In-depth explanations of how Amazon SageMaker solves production ML challenges
  • A step-by-step walkthrough of setting up your own SageMaker Studio development environment and connecting to a GitHub repository
  • Jupyter Notebooks containing sample code demonstrating how to train, deploy and monitor ML models
  • Screencasts walking you through how to use the AWS console to interact with SageMaker and S3
  • Recommendations on the best ways to set up common ML workflows like automated model retraining

How will this course benefit me and my career? The benefits of taking this course include:

  • Equip yourself with the best practices and patterns for production ML systems
  • Improve your marketability as an ML professional by gaining highly in demand MLOps skills
  • Learn directly from a trusted industry expert and advanced practitioner

Who is this course for? This course is designed for the following types of people:

  • ML engineers and data scientists who want to deploy their models to production
  • Managers and Leaders of Data Science teams who wish to enable efficient practices within their organizations
  • Software developers seeking to move into machine learning
  • Site Reliability Engineers (SREs) and other DevOps responsible for deploying ML models
  • Note: If you aren't familiar with Amazon Web Services, no problem! We'll provide overviews of the relevant services used in the course.

How long will each course run? What is the level of effort expected? The study group for this course will kick-off at 9am PT / 12pm ET on August 8th. These hour and a half sessions will run for six consecutive weeks with a targeted completion date of September 12th.

Is there a discount for TWIML participants? Glad you asked. Yes, to kick off this partnership, Luigi has agreed to extend a discount to TWIML community members who register. Please use discount code TWIML when you enroll to get the TWIML community discount!

What programming languages/frameworks are used in this course? This course utilizes Python 3 as the main programming language. In order to interact with Amazon SageMaker, we rely on the SageMaker Python SDK and the SageMaker Experiments Python SDK. Additionally, we'll train models using the scikit-learn, XGBoost, Tensorflow, and PyTorch frameworks and associated Python clients.

Is TWIML paid as part of this arrangement? Yes, we are a Luigi Patruno / Teachable affiliate and get a commission as part of the partnership. Whatever we earn through this relationship will help support our broader community and educational efforts. That said, we would never recommend a course we didn’t think was a good use of your time and a good value.

How long will students have access to the course materials? After you enroll, you will have access to the materials indefinitely.

How long will enrollment for the course be open? How long will the discount be available? While the course itself is fundamentally designed for self-paced study, with Luigi running a live weekly study group, we will be closing enrollment on Friday, August 7th at 11:59 PT. The TWIML discount will be available until the close of enrollment.

Will the weekly study group sessions be open to anyone? Luigi's weekly study group sessions are intended for enrollees and will assume that learners have at least gone over that week’s lectures at a high level.

Is there a detailed syllabus? Yes, the syllabus can be found here.

What can I expect from the weekly online sessions? The weekly online sessions are live study group sessions presented by Luigi, the instructor and author of the Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker course. At the sessions, Luigi will present a summary of that week’s course materials and open the floor for student discussion.

What if I cannot participate in the weekly online sessions? The weekly study group sessions will be recorded and will be available to TWIML enrollees.

What is the refund policy? There is a 14-day refund policy on the course.