AWS ramps up SageMaker tools at Re:Invent

Cloud giant loads more capabilities into its machine learning hub with AWS SageMaker Studio

CEO Andy Jassy announced a barrage of new machine learning capabilities for AWS SageMaker during his re:Invent keynote on Tuesday.

SageMaker is Amazon's big machine learning hub that aims to remove most of the heavy lifting for developers and let them use ML more expansively. Launched in 2017, there have been numerous features and capabilities introduced over the years, with more than 50 added to it in 2019 alone.

Of the SageMaker announcements made at the company's annual conference in Las Vegas, the biggest was AWS SageMaker Studio, an IDE that allows developers and data scientists to build, code, develop, train and tune machine learning workflows all in a single interface. Within it information can be viewed, stored, collected and used to collaborate with others through the studio.

AWS SageMaker Studio

In addition to SageMaker Studio, the company announced a further five new capabilities: Notebooks, Experiment Management, Autopilot, Debugger and Model Monitor.

The first of these is described as a 'one-click' notebook with elastic compute.

"In the past, Notebooks is frequently where data scientists would work and it was associated with a single EC2 instance," explained Larry Pizette, the global head of ML solutions Lab. "If a developer or data scientist wanted to switch capabilities, so they wanted more compute capacity, for instance, they had to shut that down and instantiate a whole new notebook.

"This can now be done dynamically, in just seconds, so they can get more compute or GPU capability for doing training or inference, so its a huge improvement over what was done before."

All of the updates to SageMaker have a specific purpose to simplify the machine learning workflows, like Experiment Management, which enables developers to visualise and compare ML model iterations, training parameters, and outcomes.

Autopilot lets developers submit simple data in CSV files and have ML models automatically generated. SageMaker Debugger provides real-time monitoring for ML models to improve predictive accuracy, reduce training times.

And finally, Amazon SageMaker Model Monitor detects concept drift to discover when the performance of a model running in production begins to deviate from the original trained model.

"We recognised that models get used over time and there can be changes to the underlying assumptions that the models were built with - such as housing prices which inflate," said Pizette. "If interest rates change it will affect the prediction of whether a person will by a home or not."

"When the model is initially built to keep statistics, it will notice what we call 'Concept Drift' if that concept drift is happening, and the model gets out of sync with the current conditions, it will identify where that's happening and provide the developer or data scientist with the information to help them retrain and retool that model."

The company also announced a ML service to help write code - AWS Code Guru. This is an automated tool that's been trained on several decades of code reviews at Amazon, according to the company. If it discovers an issue, it will add human-readable comments to pull requests that identify lines of code with a specific issue and recommended remediation, including example code and links to relevant documentation.

Featured Resources

How to scale your organisation in the cloud

How to overcome common scaling challenges and choose the right scalable cloud service

Download now

The people factor: A critical ingredient for intelligent communications

How to improve communication within your business

Download now

Future of video conferencing

Optimising video conferencing features to achieve business goals

Download now

Improving cyber security for remote working

13 recommendations for security from any location

Download now

Recommended

How to become a machine learning engineer
Careers & training

How to become a machine learning engineer

23 Dec 2020
Data science fails: Building AI you can trust
Whitepaper

Data science fails: Building AI you can trust

2 Dec 2020
MLOps 101: The foundation for your AI strategy
Whitepaper

MLOps 101: The foundation for your AI strategy

2 Dec 2020
Realising the benefits of automated machine learning
Whitepaper

Realising the benefits of automated machine learning

2 Dec 2020

Most Popular

How to build a CMS with React and Google Sheets
content management system (CMS)

How to build a CMS with React and Google Sheets

24 Feb 2021
How to find RAM speed, size and type
Laptops

How to find RAM speed, size and type

26 Feb 2021
How to connect one, two or more monitors to your laptop
Laptops

How to connect one, two or more monitors to your laptop

25 Feb 2021