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%.

Three things will happen when you finish this program:

You’ll have a solid understanding of most theoretical aspects concerning Machine Learning systems.

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.

You’ll build connections with like-minded professionals working in the industry.

What You’ll Learn In Machine Learning School?

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.

Sale Page: Santiago – Machine Learning School

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