AWS re:Invent 2020
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AWS introduces new Chaos Engineering as a Service offering
When large companies like Netflix or Amazon want to test the resilience of their systems, they use chaos engineering tools designed to help them simulate worst-case scenarios and find potential issues before they even happen. Today at AWS re:Invent, Amazon CTO Werner Vogels introduced the company’s Chaos Engineering as a Service offering called AWS Fault Injection Simulator.
The name may lack a certain marketing panache, but Vogels said that the service is designed to help bring this capability to all companies. “We believe that chaos engineering is for everyone, not just shops running at Amazon or Netflix scale. And that’s why today I’m excited to pre-announce a new service built to simplify the process of running chaos experiments in the cloud,” Vogels said.
As he explained, the goal of chaos engineering is to understand how your application responds to issues by injecting failures into your application, usually running these experiments against production systems. AWS Fault Injection Simulator offers a fully managed service to run these experiments on applications running on AWS hardware.
Image Credits: Amazon / Getty Images
“FIS makes it easy to run safe experiments. We built it to follow the typical chaos experimental workflow where you understand your steady state, set a hypothesis and inject faults into your application. When the experiment is over, FIS will tell you if your hypothesis was confirmed, and you can use the data collected by CloudWatch to decide where you need to make improvements,” he explained.
While the company was announcing the service today, Vogels indicated it won’t actually be available until some time next year.
It’s worth noting that there are other similar services out there by companies, like Gremlin, which are already providing a broad Chaos Engineering Service as a Service offering.
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AWS expands on SageMaker capabilities with end-to-end features for machine learning
Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.
As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.
“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”
Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.
The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.
Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.
Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.
To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.
Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.
Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.
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AWS announces SageMaker Clarify to help reduce bias in machine learning models
As companies rely increasingly on machine learning models to run their businesses, it’s imperative to include anti-bias measures to ensure these models are not making false or misleading assumptions. Today at AWS re:Invent, AWS introduced Amazon SageMaker Clarify to help reduce bias in machine learning models.
“We are launching Amazon SageMaker Clarify. And what that does is it allows you to have insight into your data and models throughout your machine learning lifecycle,” Bratin Saha, Amazon VP and general manager of machine learning told TechCrunch.
He says that it is designed to analyze the data for bias before you start data prep, so you can find these kinds of problems before you even start building your model.
“Once I have my training data set, I can [look at things like if I have] an equal number of various classes, like do I have equal numbers of males and females or do I have equal numbers of other kinds of classes, and we have a set of several metrics that you can use for the statistical analysis so you get real insight into easier data set balance,” Saha explained.
After you build your model, you can run SageMaker Clarify again to look for similar factors that might have crept into your model as you built it. “So you start off by doing statistical bias analysis on your data, and then post training you can again do analysis on the model,” he said.
There are multiple types of bias that can enter a model due to the background of the data scientists building the model, the nature of the data and how they data scientists interpret that data through the model they built. While this can be problematic in general it can also lead to racial stereotypes being extended to algorithms. As an example, facial recognition systems have proven quite accurate at identifying white faces, but much less so when it comes to recognizing people of color.
It may be difficult to identify these kinds of biases with software as it often has to do with team makeup and other factors outside the purview of a software analysis tool, but Saha says they are trying to make that software approach as comprehensive as possible.
“If you look at SageMaker Clarify it gives you data bias analysis, it gives you model bias analysis, it gives you model explainability it gives you per inference explainability it gives you a global explainability,” Saha said.
Saha says that Amazon is aware of the bias problem and that is why it created this tool to help, but he recognizes that this tool alone won’t eliminate all of the bias issues that can crop up in machine learning models, and they offer other ways to help too.
“We are also working with our customers in various ways. So we have documentation, best practices, and we point our customers to how to be able to architect their systems and work with the system so they get the desired results,” he said.
SageMaker Clarify is available starting to day in multiple regions.
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AWS expands startup assistance program
Last year, AWS launched the APN Global Startup Program, which is sort of AWS’s answer to an incubator for mid to late-stage startups deeply involved with AWS technology. This year, the company wants to expand that offering, and today it announced some updates to the program at the Partner keynote at AWS re:Invent.
While startups technically have to pay a $2,500 fee if they are accepted to the program, AWS typically refunds that fee, says Doug Yeum, head of the Global Partner Organization at AWS — and they get a lot of benefits for being part of the program.
“While the APN has a $2,500 annual program fee, startups that are accepted into the invite-only APN Global Startup Program get that fee back, as well as free access to substantial additional resources both in terms of funding as well as exclusive program partner managers and co-sell specialists resources,” Yeum told TechCrunch.
And those benefits are pretty substantial, including access to a new “white glove program” that lets them work with a program manager with direct knowledge of AWS and who has experience working with startups. In addition, participants get access to an ISV program to work more directly with these vendors to increase sales and access to data exchange services to move third-party data into the AWS cloud.
What’s more, they can apply to the new AI/ML Acceleration program. As AWS describes it, “This includes up to $5,000 AWS credits to fund experiments on AWS services, enabling startups to explore AWS AI/ML tools that offer the best fit for them at low risk.”
Finally, they get partially free access to the AWS Marketplace, offsetting the normal marketplace listing fees for the first five offerings. Some participants will also get access to AWS sales to help use the power of the large company to drive a startup’s sales.
While you can apply to the program, the company also recruits individual startups that catch its attention. “We also proactively invite mid to late-stage startups built on AWS that, based on market signals, are showing traction and offer interesting use cases for our mutual enterprise customers,” Yeum explained.
Among the companies currently involved in the program are HashiCorp, Logz.io and Snapdocs. Interested startups can apply on the APN Global Startup website.
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