Santiago – Machine Learning School

Santiago – Machine Learning School

Many people know how to train Machine Learning models.

Unfortunately, this is around 5% of the work required to build an end-to-end system.

This program will show you the other 95%.

What Do You Get From Joining?

When you join the program, you get access to the following:

1. A 14-hour live cohort. Every month, there are six live sessions of 2 hours each (12 hours total) and two optional Office Hours. You’ll learn about Production Machine Learning and build an end-to-end system from scratch. You can attend live or watch the recording of the sessions.
2. Multi-Choice Questions and Assignments. This is a program for builders, and you will have plenty to do. Every session will give you a list of multi-choice questions and assignments to practice what you learned.
3. A Community. You’ll join a community of professionals from every corner of the world with something in common: They are all building Machine Learning systems for a living.

Three things will happen when you finish this program:

1. You’ll have a solid understanding of most theoretical aspects concerning Machine Learning systems.
2. You’ll have experience building an end-to-end system using SageMaker. You’ll understand how to process data, train, tune, evaluate, deploy, and monitor models in a production environment. You’ll know a few tricks from somebody who spent many nights trying to figure these things out.
3. You’ll build connections with like-minded professionals working in the industry.

Who Is This Program For?

1. This program is split into three levels: the conversational level (theory), the implementation level (code), and the execution level (practice.) Non-technical students will benefit from the first and some portions of the second level. Students with technical skills can practice what they learned at the third level.
2. This is a hands-on, technical program. It’s packed and tough.
3. This program doesn’t cover basic machine learning theory. While you don’t need experience in machine learning to succeed in the program, I expect you have a certain level of familiarity with machine learning terminology. For example, you don’t need to understand the architecture of a Deep Learning model, but you should understand what “training” a model means.

Schedule

Every month, the time of the cohort changes. We will meet 3 times for 2 consecutive weeks. There will be 2 optional Office Hours. Every session will be recorded, so you can attend live or watch the recorded version later.

Here are the upcoming cohorts:

• Cohort #8: Nov 6 – Nov 17. 10 am EST. (Monday, Wednesday, and Friday)
• Cohort #9: Dec 4 – Dec 15. 2 pm EST. (Monday, Wednesday, and Friday)

We will start the program with a simple problem and build an entire end-to-end system for the six sessions. Every session is packed with information and code. It will be intense but fun.

Syllabus

Session 1 – Production Machine Learning is Different

• What makes production machine learning different from what you’ve learned
• Unlearning what you think Machine Learning is and how to start thinking like an engineer
• Sampling strategies when collecting data. An introduction to nonprobability sampling, random sampling, stratified sampling, and weighted sampling
• Labeling strategies. An introduction to weak supervision, active learning, and the blessing of natural labels
• Building good features. An introduction to data imputation, standardization, and encoding
• The importance of splitting data and why you should always do it before transforming your data. How data leakage can destroy your models
• How to use pipelines to orchestrate machine learning workflows. Preparing a transformation pipeline, a training pipeline, and an inference pipeline
• A template architecture to solve some of the most critical aspects of any production machine learning system
• How to process incoming data automatically without having people work on weekends. Handling large amounts of data using Distributed Processing
• Understanding the SageMaker’s Processing Step and Processing Jobs
• A quick look into Data Wrangler for data preparation and feature engineering

Session 2 – Building a Model and the Training Pipeline

• Handling class imbalances and dealing with rare events. Choosing the right metric, sampling, cost-sensitive learning, and class weighting
• An introduction to data augmentation
• The first rule of Machine Learning Engineering and the reason you don’t want to use machine learning in the first place
• Building a model from simple heuristics to complex machine learning algorithms
• How to train a model when we can’t fit the data or the model in a single node. Distributed Training using Data and Model Parallelism
• Squeezing a bit more performance using Hyperparameter Tuning in a training pipeline
• How to track, recreate, and compare experiments. Tracking and versioning everything you need to go back in time
• Understanding SageMaker’s Training Step and Training Jobs
• Understanding SageMaker’s Tuning Step and Tuning Jobs

Session 3 – Model Evaluation and Versioning

• Why good models aren’t necessarily useful and useful models aren’t necessarily good
• Dealing with competing priorities when building machine learning systems. Decoupling objectives
• A different way to apply machine learning in the real world. Augmenting and creating instead of replacing
• Framing evaluation metrics to affect business performance
• Contextualizing evaluation metrics with a baseline. Human and random baselines, simple heuristics, and using existing systems for context
• Evaluating the robustness and fairness of a model. Techniques to identify biases
• Evaluating whether individual predictions are useful
• An introduction to backtests and how to use them to evaluate models
• The importance of versioning models
• Understanding SageMaker’s Model Registry
• Understanding SageMaker’s Condition Step
• Understanding SageMaker’s Model Step

Session 4 – Model Deployment and Inference Pipelines

• On-demand predictions versus batch inference. Understanding when to use each of them and how to combine them
• The disadvantages of batch inference and how to work around them
• Making models run fast. Model compression and an introduction to Quantization and Knowledge Distillation
• Deploying multiple models that work together. A comparison between dedicated and multi-model endpoints
• Designing an Inference Pipeline using the transformation pipeline we used to preprocess the data
• Understanding the SageMaker Lambda Step. A quick introduction to serverless functions
• The internal structure of a SageMaker Endpoint
• Customizing SageMaker models with a custom inference process
• Understanding SageMaker’s PipelineModel

Session 5 – Data Distribution Shifts and Model Monitoring

• An introduction to data distribution shifts
• Catastrophic predictions and the problem with edge cases
• Unintended feedback loops and how to work around them
• An introduction to covariate shift and concept drift. How can these changes happen
• How to identify data distribution shifts. An introduction to model monitoring
• How to respond to data distribution shifts. An introduction to defensive design, retraining, and the advantage of additional data
• Making the case for Continual Learning
• Understanding SageMaker’s Transform Step and Transform Jobs
• Understanding SageMaker’s QualityCheck Step
• Understanding SageMaker’s Data and Model Monitoring Jobs

Session 6 – Continual Learning and Testing in Production

• The importance of Continual Learning and why every company wants to be here
• The three main challenges when implementing Continual Learning
• A four-step plan to implement Continual Learning
• How to determine what data to use to retrain a model
• How frequently should we retrain a model
• Retraining strategies. Training from scratch and incremental training. Advantages and disadvantages
• Using offline evaluation and backtests during Continual Learning
• An introduction to Testing in Production
• Five strategies to test models in production. An introduction to A/B testing, Shadow deployments, Canary releases, Interleaving experiments, and Multi-armed bandits.

An important note about joining the program: You pay once to join and get lifetime access to every class, session, lesson, and resource in the community. No recurrent payments. Ever.

What students are saying

• The Machine Learning School Community is your go-to resource for venturing into machine learning. It focuses on the model deployment and maintenance area of Machine Learning and offers valuable insights, best practices, and a vast library of curated resources. You’ll learn from your peers and Santiago, a seasoned expert in the field. Consider joining! — Farooq Anjum
• I have just finished Session 5 and have to say this is the best ML course I’ve done ever. (…) worth every cent. — Jose Reyes
• Thanks for your great content, the money I paid here is the best investment I’ve ever made. — Nour Araar
• I completed Cohort 3 and recommend it to anyone looking to improve their Machine Learning skills. The program offers a comprehensive approach to building complete Machine Learning systems, balancing theory, and practical application. The group sessions and assignments were challenging but beneficial, and lifelong access to all resources is included with a one-time payment. Santiago’s passion for teaching made the experience engaging and impactful. — Julian Jaramillo
• (…) buying access to the community and courses is one of my best purchases. The value-for-money ratio is fantastic, and some of the additional work you have done on top of the SageMaker course is great. I was not expecting that much value other than a SageMaker course, and you have gone above and beyond that, so thank you very much! — A student who asked to remain anonymous.
• As part of the original cohort for the machine learning program developed by Santiago, I must say that it exceeded my expectations! The program equipped me with knowledge and skills to create and deploy models through an entire process so they are real-world-ready. This program went beyond ML and delved into the crucial aspects of deploying them effectively to production. The practical approach to creating these systems is what sets this program apart. One of the standout features is Santiago’s continuous improvement and support. Classes are refined on an ongoing basis from the feedback received from students. This ensures that content is current and keeps up with the latest advancements in this space, guaranteeing relevance. Lastly, the one-time payment structure and lifetime resource access demonstrate the commitment to providing ever-growing value! — A student who asked to remain anonymous.

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