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AWS expands on SageMaker capabilities with end-to-end features for machine learning

By Jonathan Shieber

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 over 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 its workflow management and automation toolkit, Pipelines. 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 towards 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 to 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 cross 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.

SAP latest enterprise software giant to offer low code workflow

By Ron Miller

Low code workflow has become all the rage among enterprise tech giants and SAP joined the group of companies offering simplified workflow creation today when it announced SAP Cloud Platform Workflow Management, but it didn’t stop there.

It also announced SAP Ruum, a new departmental workflow tool and SAP Intelligent Robotic Process Automation, its entry into the RPA space. The company made the announcements at SAP TechEd, its annual educational conference that has gone virtual this year due to the pandemic.

Let’s start with the Cloud Platform Workflow Management tool. It enables people with little or no coding skills to build operational workflows. It includes predefined workflows like employee onboarding and can be used in combination with Qualtrics, the company it bought for $8 billion 2018, to include experience data.

As SAP CTO Juergen Mueller told me, the company sees these types of activities in a much larger context. In the hiring example, that means it’s more than simply the act of being hired and getting started. “We like to think in end-to-end processes, and the one fitting into the employee onboarding would be recruit to retire. So it would start at talent acquisition,” he said.

Hiring and employee onboarding is the first part of the larger process, but there are other workflows that develop out of that throughout the employee’s time at the company. “Basically this is a collection of different workflow steps that are happening with some in parallel, some in sequence,” he said.

If there are experience questions involved like which benefits you want, you could add Qualtrics questionnaires to that part of the workflow. It’s designed to be very flexible. As with all of these kinds of tools, you can drag and drop components and do some basic configuration and you’re good to go. In reality, the more complex these become, the more expertise would be required, but this type of tool is designed with non-technical end users in mind as a starting point.

SAP Ruum is a simplified version of Cloud Platform Workflow Management designed for building departmental processes, and if there is an automation element involved where you want to let the machine take care of some mundane, repeatable tasks, then the RPA solution comes into play. The latter tends to be more complex and require more IT involvement, but it enables companies to build automation into workflows where the machine pushes data along through the workflow and does at least some of the work for you.

The company joins Salesforce, which announced Einstein Workflow Automation last week at Dreamforce and Google Workflows, the tool the company introduced in August. There are many others out there from companies large and small including Okta, Slack and Airtable, which all have no-code workflow tools built in.

The SAP TechEd conference has been going on for 24 years, and usually takes place in three separate venues — Barcelona, Las Vegas and Bangalore —  throughout the year. This year, the company is running a single-combined virtual conference for free to all comers. It runs for 48 hours straight starting today with a worldwide audience of over 60,000 sign-ups as of yesterday.

Salesforce applies AI to workflow with Einstein Automate

By Ron Miller

While Salesforce made a big splash yesterday with the announcement that it’s buying Slack for $27.7 billion, it’s not the only thing going on for the CRM giant this week. In fact, Dreamforce, the company’s customer extravaganza, is also on the docket. While it is virtual this year, there are still product announcements aplenty, and today the company announced Einstein Automate, a new AI-fueled set of workflow solutions.

Sarah Franklin, EVP & GM of Platform, Trailhead and AppExchange at Salesforce says that she is seeing companies facing a digital imperative to automate processes as things move ever more quickly online, being driven there even faster by the pandemic. “With Einstein Automate, everyone can change the speed of work and be more productive through intelligent workflow automation,” she said in a statement.

Brent Leary, principal analyst at CRM Essentials says that combined these tools are designed to help customers get to work more quickly. “It’s not only about identifying the insight, it’s about making it easier to leverage it at the the right time. And this should make it easier for users to do it without spending more time and effort,” Leary told TechCrunch.

Einstein is the commercial name given to Salesforce’s artificial intelligence platform that touches every aspect of the company’s product line, bringing automation to many tasks and making it easier to find the most valuable information on customers, which is often buried in an avalanche of data.

Einstein Automate encompasses several products designed to improve workflows inside organizations. For starters, the company has created Flow Orchestrator, a tool that uses a low-code, drag and drop approach for building workflows, but it doesn’t stop there. It also relies on AI to provide help suggest logical next steps to speed up workflow creation.

Salesforce is also bringing MuleSoft, the integration company it bought for $6.5 billion in 2018 into the mix. Instead of processes like a mortgage approval workflow, the Mulesoft piece lets IT build complex integrations between applications across the enterprise, and the Salesforce family of products more easily.

To make it easier to build these workflows, Salesforce is announcing the Einstein Automate collection page available in AppExchange, the company’s application marketplace. The collection includes over 700 pre-built connectors so customers can grab and go as they build these workflows, and finally it’s updating the OmniStudio, their platform for generating customer experiences. As Salesforce describes it, “Included in OmniStudio is a suite of resources and no-code tools, including pre-built guided experiences, templates and more, allowing users to deploy digital-first experiences like licensing and permit applications quickly and with ease. ”

Per usual with Salesforce Dreamforce announcements, the Flow Orchestrator being announced today won’t be available in beta until next summer. The Mulesoft component will be available in early 2021, but the OmniStudio updates and the Einstein connections collection are available today.

AWS launches SageMaker Data Wrangler, a new data preparation service for machine learning

By Frederic Lardinois

AWS launched a new service today, Amazon SageMaker Data Wrangler, that makes it easier for data scientists to prepare their data for machine learning training. In addition, the company is also launching SageMaker Feature Store, available in the SageMaker Studio, a new service that makes it easier to name, organize, find and share machine learning features.

AWS is also launching Sagemaker Pipelines, a new service that’s integrated with the rest of the platform and that provides a CI/CD service for machine learning to create and automate workflows, as well as an audit trail for model components like training data and configurations.

As AWS CEO Andy Jassy pointed out in his keynote at the company’s re:Invent conference, data preparation remains a major challenge in the machine learning space. Users have to write their queries and the code to get the data from their data stores first, then write the queries to transform that code and combine features as necessary. All of that is work that doesn’t actually focus on building the models but on the infrastructure of building models.

Data Wrangler comes with over 300 pre-configured data transformation built-in, that help users convert column types or impute missing data with mean or median values. There are also some built-in visualization tools to help identify potential errors, as well as tools for checking if there are inconsistencies in the data and diagnose them before the models are deployed.

All of these workflows can then be saved in a notebook or as a script so that teams can replicate them — and used in SageMaker Pipelines to automate the rest of the workflow, too.

 

It’s worth noting that there are quite a few startups that are working on the same problem. Wrangling machine learning data, after all, is one of the most common problems in the space. For the most part, though, most companies still build their own tools and as usual, that makes this area ripe for a managed service.

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