AWS re:Invent 2019 — AI/ML recap — Part 2: Amazon SageMaker
Tags: kubernetes, aws, machinelearning, deeplearning
Published: 2019-12-18 21:51:56

---
title: AWS re:Invent 2019 — AI/ML recap — Part 2: Amazon SageMaker
published: true
date: 2019-12-11 15:29:12 UTC
tags: kubernetes, aws, machinelearning, deeplearning
canonical_url: https://medium.com/@julsimon/aws-re-invent-2019-ai-ml-recap-part-2-amazon-sagemaker-fee83f05a1bc
---

### AWS re:Invent 2019 — AI/ML recap — Part 2: Amazon SageMaker

In a previous [post](https://dev.to/juliensimon/aws-re-invent-2019-ai-ml-recap-part-1-ai-services-lai), I introduced you to our new high-level services. Now let’s go down one layer and talk about the new capabilities added to [Amazon SageMaker](https://aws.amazon.com/sagemaker): **SageMaker Processing, SageMaker Experiments, SageMaker AutoPilot, SageMaker Debugger, SageMaker Model Monitor, SageMaker Notebooks, SageMaker Studio, and SageMaker Operators for Kubernetes.**

![](https://cdn-images-1.medium.com/max/1024/1*FnxFcRnXFnLqbAOuT3EoEQ.png)

If you’re just looking for an overview of the new capabilities, this is the one.

{% youtube Ac5m4-xWtCU %}

If you want the full enchilada, keep reading. I’ll share learning resources along the way.

As always, happy to answer questions here or on [Twitter](https://twitter.com/julsimon).

Here we go!

### Amazon SageMaker Processing

Amazon SageMaker Processing lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure. At launch, you can use either Scikit-learn or Spark.

![](https://cdn-images-1.medium.com/max/1024/1*SmK7zLyegFfkSDBdZowRIw.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/)

Documentation: [https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html](https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html)

Examples: [https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker\_processing](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_processing)

### Amazon SageMaker Experiments

Amazon SageMaker Experiments is a capability of Amazon SageMaker that lets you organize, track, compare, and evaluate your machine learning experiments. It’s nicely integrated with hyperparameter tuning and SageMaker Autopilot: no code needed!

![](https://cdn-images-1.medium.com/max/1024/1*czfZkWV-tuSdK8jPQSSWyg.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/](https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/)

Documentation: [https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html)

Examples:

- AIM361 workshop at re:Invent2019: [https://gitlab.com/juliensimon/aim361/](https://gitlab.com/juliensimon/aim361/blob/master/Lab2.ipynb)

### Amazon SageMaker AutoPilot

Amazon SageMaker AutoPilot Amazon lets you automatically create the best classification and regression machine learning models, while allowing full control and visibility.

![](https://cdn-images-1.medium.com/max/1024/1*IMnu2eks9VMfxN2HZ-Stiw.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/](https://aws.amazon.com/blogs/aws/amazon-sagemaker-autopilot-fully-managed-automatic-machine-learning/)

Documentation: [https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html)

Examples:

- [https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker\_processing](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/autopilot)
- AIM361 workshop at re:Invent2019: [https://gitlab.com/juliensimon/aim361/blob/master/Lab2.ipynb](https://gitlab.com/juliensimon/aim361/blob/master/Lab2.ipynb)

Here’s a gentle introduction.

{% youtube mKVZukgtkcA %}

I also recorded a complete 4-part demo using Amazon SageMaker Studio.

{% youtube qMEtqJPhqpA %}

{% youtube WsfRAeGzgm8 %}

{% youtube KZSTsWrDGXs %}

{% youtube vRHyX3kDstI %}

### Amazon SageMaker Debugger

Amazon SageMaker Debugger provides full visibility into the training of machine learning models by monitoring, recording, and analyzing the tensor data that captures the state of a machine learning training job.

![](https://cdn-images-1.medium.com/max/1024/1*-9U5u82Qc-I5IHzci0rrqg.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-sagemaker-debugger-debug-your-machine-learning-models/](https://aws.amazon.com/blogs/aws/amazon-sagemaker-debugger-debug-your-machine-learning-models/)

Documentation: [https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html)

Examples: [https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger)

{% youtube x-HoZLdUOyU %}

### Amazon SageMaker Model Monitor

Amazon SageMaker Model Monitor automatically monitors ML models in production and notifies you when data quality issues arise.

![](https://cdn-images-1.medium.com/max/1024/1*AeHl0T_VMs2T6SLXxA_BsQ.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-sagemaker-model-monitor-fully-managed-automatic-monitoring-for-your-machine-learning-models/](https://aws.amazon.com/blogs/aws/amazon-sagemaker-model-monitor-fully-managed-automatic-monitoring-for-your-machine-learning-models/)

Documentation: [https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html)

Examples: [https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker\_model\_monitor](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_model_monitor)

### Amazon SageMaker Studio and Amazon SageMaker Notebooks (preview)

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It includes Amazon SageMaker Notebooks, one-click Jupyter notebooks that you can start working with in seconds

![](https://cdn-images-1.medium.com/max/1024/1*f2CXTX5ArQQrNeVUSre_IA.png)

![](https://cdn-images-1.medium.com/max/1024/1*lb7hYuEIIFafiOmuPdOJSw.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-sagemaker-model-monitor-fully-managed-automatic-monitoring-for-your-machine-learning-models/](https://aws.amazon.com/blogs/aws/amazon-sagemaker-studio-the-first-fully-integrated-development-environment-for-machine-learning/)

Documentation: [https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks.html](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks.html)

{% youtube kou1gQmSmCU %}

### Amazon SageMaker Operators for Kubernetes

Amazon SageMaker Operators for Kubernetes makes it easier for developers and data scientists using Kubernetes to train, tune, and deploy ML models in Amazon SageMaker.

![](https://cdn-images-1.medium.com/max/1024/1*NsJc0gBOFZK-3RWejs7Gww.png)

Blog: [https://aws.amazon.com/blogs/machine-learning/introducing-amazon-sagemaker-operators-for-kubernetes/](https://aws.amazon.com/blogs/machine-learning/introducing-amazon-sagemaker-operators-for-kubernetes/)

Documentation: [https://sagemaker.readthedocs.io/en/stable/amazon\_sagemaker\_operators\_for\_kubernetes.html](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_operators_for_kubernetes.html)

Examples: [https://github.com/aws/amazon-sagemaker-operator-for-k8s](https://github.com/aws/amazon-sagemaker-operator-for-k8s)

{% youtube fgLlEnhFZQA %}