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.

As the health tech landscape rapidly evolves, another startup is making its presence known. HealNow has closed a $1.3 million round of funding from SoftBank Opportunity Fund and Alabama Futures Fund.

The company was founded by Halston Prox and Joshua Smith. Prox has worked in healthcare for more than a decade with major organizations such as Providence Health, Mount Sinai and Baylor Scott & White, mostly focused on digitizing health records and designing and implementing software for doctors, nurses, etc. Smith, CTO at the company, has been a developer since 2012.

The duo founded HealNow to become the central nervous system for order and delivery of prescriptions, according to Prox. Your average payments processing system isn’t necessarily applicable to pharmacies large and small because of the complexities of health insurance and the regulatory landscape.

Not only is it costly to facilitate online payments for pharmacies, but they also have their own pharmacy management systems and workflows that can be easily disrupted by moving to a new payments system.

HealNow has built a system that’s specifically tailored to pharmacies of any shape or size, from grocery stores to mom and pop pharmacies and everything in between. It’s a white label solution, meaning that any pharmacy can put their brand language on the product.

“We’re embedded in their current workflows and pharmacies don’t have to do anything manual, even if they’re using a pharmacy management system,” said Prox.

When a user looks to get a prescription from their pharmacy, they are sent a link that allows them to securely answer any questions that may be necessary for the pickup, enter insurance info, make a payment and schedule a curbside pickup or a delivery. The tech also integrates with third-party delivery services for pharmacies that offer deliveries.

This technology has been particularly important during the COVID-19 pandemic, giving smaller pharmacies the chance to compete with bigger chains who have digital solutions already set up that allow for curbside pick up. This is especially true now that Amazon has gotten into the space with the launch of Amazon Pharmacy.

HealNow is a SaaS company, charging a monthly subscription fee for use of the platform, as well as a service fee for prescriptions purchased on the platform. However, that service fee is a flat rate that never changes based on the cost of the prescription.

The space is crowded and growing more crowded, with competitors like NimbleRX and Capsule offering their own spin on simplifying and digitizing the pharmacy. One big difference for HealNow, says Prox, is that the startup has no intention of ever being a pharmacy, but rather serving pharmacies in a way that doesn’t disrupt their current workflow or system.

“We’re not a pharmacy, and we want to enable all these pharmacies to be online,” said Prox. “To do that we have to do that in an unbiased way by focusing on being a complete tech company.”

The funding is going primarily toward building out the sales and marketing arms of the company to continue fueling growth. HealNow has a foothold in the West, Southwest and Middle America, and is opening an office in Birmingham to sprint across the East Coast. Prox says the company is processing thousands of orders a day and tens of thousands of orders each month.

HealNow launched in 2018 after graduating from the Entrepreneurs Roundtable Accelerator .

Power Automate is Microsoft’s platform for streamlining repetitive workflows — you may remember it under its original name: Microsoft Flow. The market for these robotic process automation (RPA) tools is hot right now, so it’s no surprise that Microsoft, too, is doubling down on its platform. Only a few months ago, the team launched Power Automate Desktop, based on its acquisition of Softomotive, which helps users automate workflows in legacy desktop-based applications, for example. After a short time in preview, Power Automate Desktop is now generally available.

The real news today, though, is that the team is also launching a new tool, the Process Advisor, which is now in preview as part of the Power Automate platform. This new process mining tool provides users with a new collaborative environment where developers and business users can work together to create new automations.

The idea here is that business users are the ones who know exactly how a certain process works. With Process Advisor, they can now submit recordings of how they process a refund, for example, and then submit that to the developers, who are typically not experts in how these processes usually work.

What’s maybe just as important is that a system like this can identify bottlenecks in existing processes where automation can help speed up existing workflows.

Image Credits: Microsoft

“This goes back to one of the things that we always talk about for Power Platform, which, it’s a corny thing, but it’s that development is a team sport,” Charles Lamanna, Microsoft’s corporate VP for its Low Code Application Platform, told me. “That’s one of our big focuses: how to bring people to collaborate and work together who normally don’t. This is great because it actually brings together the business users who live the process each and every day with a specialist who can build the robot and do the automation.”

The way this works in the backend is that Power Automate’s tools capture exactly what the users do and click on. All this information is then uploaded to the cloud and — with just five or six recordings — Power Automate’s systems can map how the process works. For more complex workflows, or those that have a lot of branches for different edge cases, you likely want more recordings to build out these processes, though.

Image Credits: Microsoft

As Lamanna noted, building out these workflows and process maps can also help businesses better understand the ROI of these automations. “This kind of map is great to go build an automation on top of it, but it’s also great because it helps you capture the ROI of each automation you do because you’ll know for each step how long it took you,” Lamanna said. “We think that this concept of Process Advisor is probably going to be one of the most important engines of adoption for all these low-code/no-code technologies that are coming out. Basically, it can help guide you to where it’s worth spending the energy, where it’s worth training people, where it’s worth building an app, or using AI, or building a robot with our RPA like Power Automate.”

Lamanna likened this to the advent of digital advertising, which for the first time helped marketers quantify the ROI of advertising.

The new process mining capabilities in Power Automate are now available in preview.

While the enterprise world likes to talk about “big data”, that term belies the real state of how data exists for many organizations: the truth of the matter is that it’s often very fragmented, living in different places and on different systems, making the concept of analysing and using it in a single, effective way a huge challenge.

Today, one of the big up-and-coming startups that has built a platform to get around that predicament is announcing a significant round of funding, a sign of the demand for its services and its success so far in executing on that.

SingleStore, which provides a SQL-based platform to help enterprises manage, parse and use data that lives in silos across multiple cloud and on-premise environments — a key piece of work needed to run applications in risk, fraud prevention, customer user experience, real-time reporting and real-time insights, fast dashboards, data warehouse augmentation, modernization for data warehouses and data architectures and faster insights — has picked up $80 million in funding, a Series E round that brings in new strategic investors alongside its existing list of backers.

The round is being led by Insight Partners, with new backers Dell Technologies Capital, Hercules Capital; and previous backers Accel, Anchorage, Glynn Capital, GV (formerly Google Ventures) and Rev IV also participating.

Alongside the investment, SingleStore is formally announcing a new partnership with analytics powerhouse SAS. I say “formally” because they two have been working together already and it’s resulted in “tremendous uptake,” CEO Raj Verma said in an interview over email.

Verma added that the round came out of inbound interest, not its own fundraising efforts, and as such, it brings the total amount of cash it has on hand to $140 million. The gives the startup money to play with not only to invest in hiring, R&D and business development, but potentially also M&A, given that the market right now seems to be in a period of consolidation.

Verma said the valuation is a “significant upround” compared to its Series D in 2018 but didn’t disclose the figure. PitchBook notes that at the time it was valued at $270 million post-money.

When I last spoke with the startup in May of this year — when it announced a debt facility of $50 million — it was not called SingleStore; it was MemSQL. The company rebranded at the end of October to the new name, but Verma said that the change was a long time in the planning.

“The name change is one of the first conversations I had when I got here,” he said about when he joined the company in 2019 (he’s been there for about 16 months). “The [former] name didn’t exactly flow off the tongue and we found that it no longer suited us, we found ourselves in a tiny shoebox of an offering, in saying our name is MemSQL we were telling our prospects to think of us as in-memory and SQL. SQL we didn’t have a problem with but we had outgrown in-memory years ago. That was really only 5% of our current revenues.”

He also mentioned the hang up many have with in-memory database implementations: they tend to be expensive. “So this implied high TCO, which couldn’t have been further from the truth,” he said. “Typically we are ⅕-⅛ the cost of what a competitive product would be to implement. We were doing ourselves a disservice with prospects and buyers.”

The company liked the name SingleStore because it is based a conceptual idea of its proprietary technology. “We wanted a name that could be a verb. Down the road we hope that when someone asks large enterprises what they do with their data, they will say that they ‘SingleStore It!’ That is the vision. The north star is that we can do all types of data without workload segmentation,” he said.

That effort is being done at a time when there is more competition than ever before in the space. Others also providing tools to manage and run analytics and other work on big data sets include Amazon, Microsoft, Snowflake, PostgreSQL, MySQL and more.

SingleStore is not disclosing any metrics on its growth at the moment but says it has thousands of enterprise customers. Some of the more recent names it’s disclosed include GE, IEX Cloud, Go Guardian, Palo Alto Networks, EOG Resources, SiriusXM + Pandora, with partners including Infosys, HCL and NextGen.

“As industry after industry reinvents itself using software, there will be accelerating market demand for predictive applications that can only be powered by fast, scalable, cloud-native database systems like SingleStore’s,” said Lonne Jaffe, managing director at Insight Partners, in a statement. “Insight Partners has spent the past 25 years helping transformational software companies rapidly scale-up, and we’re looking forward to working with Raj and his management team as they bring SingleStore’s highly differentiated technology to customers and partners across the world.”

“Across industries, SAS is running some of the most demanding and sophisticated machine learning workloads in the world to help organizations make the best decisions. SAS continues to innovate in AI and advanced analytics, and we partner with companies like SingleStore that share our curiosity about how data and analytics can help organizations reimagine their businesses and change the world,” said Oliver Schabenberger, COO and CTO at SAS, added. “Our engineering teams are integrating SingleStore’s scalable SQL-based database platform with the massively parallel analytics engine SAS Viya. We are excited to work with SingleStore to improve performance, reduce cost, and enable our customers to be at the forefront of analytics and decisioning.”

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.