Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

Feature tour of model-building capabilities

In this section, we'll dive into SageMaker's model-building capabilities. By the end of this section, you should understand when to use SageMaker Studio or SageMaker notebook instances, and how to choose between SageMaker's built-in algorithms, frameworks, and libraries, versus a bring your own (BYO) approach.

SageMaker Studio

SageMaker Studio is an integrated development environment (IDE) for ML. It brings together Jupyter notebooks, experiment management, and other tools into a unified user interface. You can easily share notebooks and notebook snapshots with other team members using Git or a shared filesystem. The following screenshot shows an example of one of SageMaker Studio's built-in visualizations:

Figure 1.9 – SageMaker Studio showing an experiment graph

Figure 1.9 – SageMaker Studio showing an experiment graph

SageMaker Studio can be used in all phases of the ML life cycle.

SageMaker notebook instances

If you prefer a more traditional Jupyter or JupyterLab experience, and you don't need the additional integrations and collaboration tools that Studio provides, you can use a regular SageMaker notebook instance. You choose the notebook instance compute capacity (that is, whether you want GPUs and how much storage you need), and SageMaker provisions the environment with the Jupyter Notebook and JupyterLab and several of the common ML frameworks and libraries installed.

The notebook instance also supports Docker in case you want to build and test containers with ML code locally. Best of all, the notebook instances come bundled with over 100 example notebooks. The following figure shows an example of the JupyterLab interface in a notebook:

Figure 1.10 – JupyterLab interface in a SageMaker notebook, showing a list of example notebooks

Figure 1.10 – JupyterLab interface in a SageMaker notebook, showing a list of example notebooks

Similar to SageMaker Studio, you can perform almost any part of the ML life cycle in a notebook instance.

SageMaker algorithms

SageMaker bundles open source and proprietary algorithms for many common ML use cases. These algorithms are a good starting point as they are tuned for performance, often supporting distributed training. The following table lists the SageMaker algorithms provided for different types of ML problems:

Figure 1.11 – SageMaker algorithms for various ML scenarios

Figure 1.11 – SageMaker algorithms for various ML scenarios

BYO algorithms and scripts

If you prefer to write your own training and inference code, you can work with a supported ML, graph, or RL framework, or bundle your own code into a Docker image. The BYO approach works well if you already have a library of model code, or if you need to build a model for a use case where a pre-built algorithm doesn't work well. Data scientists who use R like to use this approach. SageMaker supports the following frameworks:

  • Supported machine learning frameworks: XGBoost, sklearn
  • Supported deep learning frameworks: TensorFlow, PyTorch, MXNet, Chainer
  • Supported reinforcement learning frameworks: Ray RLLib, Coach
  • Supporting graph frameworks: Deep Graph Library

Now that we've introduced several SageMaker capabilities for model building, let's move on to training and tuning capabilities.