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In latest JEDI contract drama, AWS files motion to stop work on project
When the Department of Defense finally made a decision in October on the decade-long, $10 billion JEDI cloud contract, it seemed that Microsoft had won. But nothing has been simple about this deal from the earliest days, so it shouldn’t come as a surprise that last night Amazon filed a motion to stop work on the project until the court decides on its protest of the DoD’s decision.
The company announced on November 22nd that it had filed suit in the U.S. Court of Federal Claims protesting the DoD’s decision to select Microsoft. Last night’s motion is an extension of that move to put the project on hold until the court decides on the merits of the case.
Sources tell us that AWS decided not protest the start of initial JEDI activities at the time of the court filing in November as an accommodation made at DoD’s request. DoD declined to comment on that.
As for why they are doing it now, an Amazon spokesperson had this to say in a statement last night: “It is common practice to stay contract performance while a protest is pending and it’s important that the numerous evaluation errors and blatant political interference that impacted the JEDI award decision be reviewed. AWS is absolutely committed to supporting the DoD’s modernization efforts and to an expeditious legal process that resolves this matter as quickly as possible.”
As we previously reported, the statement echoes sentiments AWS CEO Andy Jassy made at a press event during AWS re:Invent in December:
“I would say is that it’s fairly obvious that we feel pretty strongly that it was not adjudicated fairly,” he said. He added, “I think that we ended up with a situation where there was political interference. When you have a sitting president, who has shared openly his disdain for a company, and the leader of that company, it makes it really difficult for government agencies, including the DoD, to make objective decisions without fear of reprisal.”
This is just the latest turn in a contract procurement process for the ages. It will now be up to the court to decide if the project should stop or not, and beyond that if the decision process was carried out fairly.
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Despite JEDI loss, AWS retains dominant market position
AWS took a hard blow last year when it lost the $10 billion, decade-long JEDI cloud contract to rival Microsoft. Yet even without that mega deal for building out the nation’s Joint Enterprise Defense Infrastructure, the company remains fully in control of the cloud infrastructure market — and it intends to fight that decision.
In fact, AWS still owns almost twice as much cloud infrastructure market share as Microsoft, its closest rival. While the two will battle over the next decade for big contracts like JEDI, for now, AWS doesn’t have much to worry about.
There was a lot more to AWS’s year than simply losing JEDI. Per usual, the news came out with a flurry of announcements and enhancements to its vast product set. Among the more interesting moves was a shift to the edge, the fact the company is getting more serious about the chip business and a big dose of machine learning product announcements.
The fact is that AWS has such market momentum now, it’s a legitimate question to ask if anyone, even Microsoft, can catch up. The market is continuing to expand though, and the next battle is for that remaining market share. AWS CEO Andy Jassy spent more time than in the past trashing Microsoft at 2019’s re:Invent customer conference in December, imploring customers to move to the cloud faster and showing that his company is preparing for a battle with its rivals in the years ahead.
Numbers, please
AWS closed 2019 on a $36 billion run rate, growing from $7.43 billion in in its first report in January to $9 billion in earnings for its most recent earnings report in October. Believe it or not, according to CNBC, that number failed to meet analysts expectations of $9.1 billion, but still accounted for 13% of Amazon’s revenue in the quarter.
Regardless, AWS is a juggernaut, which is fairly amazing when you consider that it started as a side project for Amazon .com in 2006. In fact, if AWS were a stand-alone company, it would be a substantial business. While growth slowed a bit last year, that’s inevitable when you get as large as AWS, says John Dinsdale, VP, chief analyst and general manager at Synergy Research, a firm that follows all aspects of the cloud market.
“This is just math and the law of large numbers. On average over the last four quarters, it has incremented its revenues by well over $500 million per quarter. So it has grown its quarterly revenues by well over $2 billion in a twelve-month period,” he said.
Dinsdale added, “To put that into context, this growth in quarterly revenue is bigger than Google’s total revenues in cloud infrastructure services. In a very large market that is growing at over 35% per year, AWS market share is holding steady.”
Dinsdale says the cloud infrastructure market didn’t quite break $100 billion last year, but even without full Q4 results, his firm’s models project a total of around $95 billion, up 37% over 2018. AWS has more than a third of that. Microsoft is way back at around 17% with Google in third with around 8 or 9%.
While this is from Q1, it illustrates the relative positions of companies in the cloud market. Chart: Synergy Research
JEDI disappointment
It would be hard to do any year-end review of AWS without discussing JEDI. From the moment the Department of Defense announced its decade-long, $10 billion cloud RFP, it has been one big controversy after another.
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Startups Weekly: Oyo has issues + A farewell
Welcome back to Startups Weekly, a weekend newsletter that dives into the week’s noteworthy startups and venture capital news. Before I jump into today’s topic, let’s catch up a bit. Last week I wrote about the startups we lost in 2019. Before that, I noted the defining moments of VC in 2019.
Unfortunately, this will be my last newsletter, as I am leaving TechCrunch for a new opportunity. Don’t worry, Startups Weekly isn’t going anywhere. We’ll have a new writer taking over the weekly update soon enough; in the meantime, TechCrunch editor Henry Pickavet will be at the helm. You can still get in touch with me on Twitter @KateClarkTweets.
If you’re new here, you can subscribe to Startups Weekly here. Lots of good content will be coming your way in 2020.
India’s WeWork?
TechCrunch reporter Manish Singh penned an interesting piece on the state of Indian startups this week: As Indian startups raise record capital, losses are widening (Extra Crunch membership required). In it, he claims the financial performance of India’s largest startups are cause for concern. Gems like Flipkart, BigBasket and Paytm have lost a collective $3 billion in the last year.
“What is especially troublesome for startups is that there is no clear path for how they would ever generate big profits,” he writes. “Silicon Valley companies, for instance, have entered and expanded into India in recent years, investing billions of dollars in local operations, but yet, India has yet to make any substantial contribution to their bottom lines. If that wasn’t challenging enough, many Indian startups compete directly with Silicon Valley giants, which while impressive, is an expensive endeavor.”
Manish’s story came one day after The New York Times published an in-depth report on Oyo, a tech-enabled budget hotel chain and rising star in the Indian tech community. The NYT wrote that Oyo offers unlicensed rooms and has bribed police officials to deter trouble, among other toxic practices.
Whether Oyo, backed by billions from the SoftBank Vision Fund, will become India’s WeWork is the real cause for concern. India’s startup ecosystem is likely to face a number of barriers as it grows to compete with the likes of Silicon Valley.
Follow Manish here or on Twitter for more of TechCrunch’s growing India coverage.
Venture capital highlights (it’s been a slow week)
- Brazil’s Loft nabs $175M from a16z, Vulcan
- Shipfix raises $4.5M seed for its dry cargo shipping platform
- InsightFinder gets $2M seed to automate outage prevention
- The 5 biggest rounds in tech in 2019
- A look at Utah’s biggest venture rounds of 2019
- In the shadow of Amazon and Microsoft, Seattle startups are having a moment
How to find the right reporter to pitch your startup
If you’ve still not subscribed to Extra Crunch, now is the time. Longtime TechCrunch reporter and editor Josh Constine is launching a new series to teach you how to pitch your startup. In it he will examine embargoes, exclusives, press kit visuals, interview questions and more. The first of many, How to find the right reporter to pitch your startup, is online now.
Subscribe to Extra Crunch here.
#EquityPod
Another week, another new episode of TechCrunch’s venture capital-focused podcast, Equity. This week, we discussed a few of 2019’s largest scandals, Peloton’s strange holiday ad and the controversy over at the luggage startup Away. Listen here and be sure to subscribe, too.
For anyone wondering about changes at Equity following my departure from TechCrunch, the lovely Alex Wilhelm (founding Equity co-host) will keep the show alive and, soon enough, there will be a brand new co-host in my place. Please keep supporting the show and be sure to recommend it to all your podcast-adoring friends.
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Moving storage in-house helped Dropbox thrive
Back in 2013, Dropbox was scaling fast.
The company had grown quickly by taking advantage of cloud infrastructure from Amazon Web Services (AWS), but when you grow rapidly, infrastructure costs can skyrocket, especially when approaching the scale Dropbox was at the time. The company decided to build its own storage system and network — a move that turned out to be a wise decision.
In a time when going from on-prem to cloud and closing private data centers was typical, Dropbox took a big chance by going the other way. The company still uses AWS for certain services, regional requirements and bursting workloads, but ultimately when it came to the company’s core storage business, it wanted to control its own destiny.
Storage is at the heart of Dropbox’s service, leaving it with scale issues like few other companies, even in an age of massive data storage. With 600 million users and 400,000 teams currently storing more than 3 exabytes of data (and growing) if it hadn’t taken this step, the company might have been squeezed by its growing cloud bills.
Controlling infrastructure helped control costs, which improved the company’s key business metrics. A look at historical performance data tells a story about the impact that taking control of storage costs had on Dropbox.
The numbers
In March of 2016, Dropbox announced that it was “storing and serving” more than 90% of user data on its own infrastructure for the first time, completing a 3-year journey to get to this point. To understand what impact the decision had on the company’s financial performance, you have to examine the numbers from 2016 forward.
There is good financial data from Dropbox going back to the first quarter of 2016 thanks to its IPO filing, but not before. So, the view into the impact of bringing storage in-house begins after the project was initially mostly completed. By examining the company’s 2016 and 2017 financial results, it’s clear that Dropbox’s revenue quality increased dramatically. Even better for the company, its revenue quality improved as its aggregate revenue grew.
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In the shadow of Amazon and Microsoft, Seattle startups are having a moment
Venture capital investment exploded across a number of geographies in 2019 despite the constant threat of an economic downturn.
San Francisco, of course, remains the startup epicenter of the world, shutting out all other geographies when it comes to capital invested. Still, other regions continue to grow, raking in more capital this year than ever.
In Utah, a new hotbed for startups, companies like Weave, Divvy and MX Technology raised a collective $370 million from private market investors. In the Northeast, New York City experienced record-breaking deal volume with median deal sizes climbing steadily. Boston is closing out the decade with at least 10 deals larger than $100 million announced this year alone. And in the lovely Pacific Northwest, home to tech heavyweights Amazon and Microsoft, Seattle is experiencing an uptick in VC interest in what could be a sign the town is finally reaching its full potential.
Seattle startups raised a total of $3.5 billion in VC funding across roughly 375 deals this year, according to data collected by PitchBook. That’s up from $3 billion in 2018 across 346 deals and a meager $1.7 billion in 2017 across 348 deals. Much of Seattle’s recent growth can be attributed to a few fast-growing businesses.
Convoy, the digital freight network that connects truckers with shippers, closed a $400 million round last month bringing its valuation to $2.75 billion. The deal was remarkable for a number of reasons. Firstly, it was the largest venture round for a Seattle-based company in a decade, PitchBook claims. And it pushed Convoy to the top of the list of the most valuable companies in the city, surpassing OfferUp, which raised a sizable Series D in 2018 at a $1.4 billion valuation.
Convoy has managed to attract a slew of high-profile investors, including Amazon’s Jeff Bezos, Salesforce CEO Marc Benioff and even U2’s Bono and the Edge. Since it was founded in 2015, the business has raised a total of more than $668 million.
Remitly, another Seattle-headquartered business, has helped bolster Seattle’s startup ecosystem. The fintech company focused on international money transfer raised a $135 million Series E led by Generation Investment Management, and $85 million in debt from Barclays, Bridge Bank, Goldman Sachs and Silicon Valley Bank earlier this year. Owl Rock Capital, Princeville Global, Prudential Financial, Schroder & Co Bank AG and Top Tier Capital Partners, and previous investors DN Capital, Naspers’ PayU and Stripes Group also participated in the equity round, which valued Remitly at nearly $1 billion.
Up-and-coming startups, including co-working space provider The Riveter, real estate business Modus and same-day delivery service Dolly, have recently attracted investment too.
A number of other factors have contributed to Seattle’s long-awaited rise in venture activity. Top-performing companies like Stripe, Airbnb and Dropbox have established engineering offices in Seattle, as has Uber, Twitter, Facebook, Disney and many others. This, of course, has attracted copious engineers, a key ingredient to building a successful tech hub. Plus, the pipeline of engineers provided by the nearby University of Washington (shout-out to my alma mater) means there’s no shortage of brainiacs.
There’s long been plenty of smart people in Seattle, mostly working at Microsoft and Amazon, however. The issue has been a shortage of entrepreneurs, or those willing to exit a well-paying gig in favor of a risky venture. Fortunately for Seattle venture capitalists, new efforts have been made to entice corporate workers to the startup universe. Pioneer Square Labs, which I profiled earlier this year, is a prime example of this movement. On a mission to champion Seattle’s unique entrepreneurial DNA, Pioneer Square Labs cropped up in 2015 to create, launch and fund technology companies headquartered in the Pacific Northwest.
Boundless CEO Xiao Wang at TechCrunch Disrupt 2017
Operating under the startup studio model, PSL’s team of former founders and venture capitalists, including Rover and Mighty AI founder Greg Gottesman, collaborate to craft and incubate startup ideas, then recruit a founding CEO from their network of entrepreneurs to lead the business. Seattle is home to two of the most valuable businesses in the world, but it has not created as many founders as anticipated. PSL hopes that by removing some of the risk, it can encourage prospective founders, like Boundless CEO Xiao Wang, a former senior product manager at Amazon, to build.
“The studio model lends itself really well to people who are 99% there, thinking ‘damn, I want to start a company,’ ” PSL co-founder Ben Gilbert said in March. “These are people that are incredible entrepreneurs but if not for the studio as a catalyst, they may not have [left].”
Boundless is one of several successful PSL spin-outs. The business, which helps families navigate the convoluted green card process, raised a $7.8 million Series A led by Foundry Group earlier this year, with participation from existing investors Trilogy Equity Partners, PSL, Two Sigma Ventures and Founders’ Co-Op.
Years-old institutional funds like Seattle’s Madrona Venture Group have done their part to bolster the Seattle startup community too. Madrona raised a $100 million Acceleration Fund earlier this year, and although it plans to look beyond its backyard for its newest deals, the firm continues to be one of the largest supporters of Pacific Northwest upstarts. Founded in 1995, Madrona’s portfolio includes Amazon, Mighty AI, UiPath, Branch and more.
Voyager Capital, another Seattle-based VC, also raised another $100 million this year to invest in the PNW. Maveron, a venture capital fund co-founded by Starbucks mastermind Howard Schultz, closed on another $180 million to invest in early-stage consumer startups in May. And new efforts like Flying Fish Partners have been busy deploying capital to promising local companies.
There’s a lot more to say about all this. Like the growing role of deep-pocketed angel investors in Seattle have in expanding the startup ecosystem, or the non-local investors, like Silicon Valley’s best, who’ve funneled cash into Seattle’s talent. In short, Seattle deal activity is finally climbing thanks to top talent, new accelerator models and several refueled venture funds. Now we wait to see how the Seattle startup community leverages this growth period and what startups emerge on top.
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Rivian adds $1.3 billion in funding for its electric utility and adventure vehicles
American automotive technology startup Rivian has raised $1.3 billion in new funding, the company announced today. The new investment is the fourth round of capital announced by the company in 2019 alone, following prior announcements of $700 million led by Amazon, $500 million from Ford (which includes a collaboration on electric vehicle technology) and $350 million from Cox Automotive.
That’s a lot of money, but Rivian’s not your typical startup, as it’s aiming to bring fully electric vehicles to market, including the R1T pickup truck and the R1S sport utility vehicle. Both of those are consumer cars, which the company aims to bring to market starting at the end of next year — and Rivian is also working with Amazon on all-electric delivery vans, of which the commerce giant has ordered 100,000, with a target of starting deliveries of the first of those in 2021.
Rivian’s new monster round includes participation from Amazon and Ford Motor Company, along with funds advised by T. Rowe Price Associates and BlackRock, the company said in a release. It’s not adding any new board seats attached to this funding, and it’s not sharing any further details on the specific funds involved in the investment at this time.
The company, founded in 2009, has R&D facilities in a number of cities globally, and also has a 2.6-million-square-foot manufacturing facility in Normal, Ill. It debuted its pickup and SUV at the LA Auto Show last November, and the vehicles will launch with higher-end trim levels first, including up to 410 miles of range on a single charge. Base prices for the R1T pickup start at $69,000 before any tax credits are applied, while the R1S SUV starts at $72,500; Rivian has been taking pre-order reservations, available with a $1,000 deposit.
For a company that in many ways has seemed to appear out of nowhere, Rivian’s capitalization and partnerships make it one of the better existing contenders to take on Tesla, especially in the truck and SUV categories, where Tesla has less presence, with only the high-end Model X actually available to purchase so far.
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Anyscale, from the creators of the Ray distributed computing project, launches with $20.6M led by a16z
Open source has become a critical building block of modern software, and today a new startup is coming out of stealth to capitalise on one of the newer frontiers in open source: using it to build and manage distributed application environments, an approach being used increasingly to handle large computing projects, such as those involving artificial intelligence or scientific or other complex calculations.
Anyscale, a startup founded by the same team that built the Project Ray open-source distributed programming framework out of UC Berkeley — Robert Nishihara, Philipp Moritz and Ion Stoica, and Berkeley professor Michael I. Jordan — has raised $20.6 million in a Series A round of funding led by Andreessen Horowitz, with participation also from NEA, Intel Capital, Ant Financial, Amplify Partners, 11.2 Capital and The House Fund.
The company plans to use the money to build out its first commercial products — details of which are still being kept under wraps but will more generally include the ability to easily scale out a computing project from one laptop to a cluster of machines; and a group of libraries and applications to manage projects. These are expected to launch next year.
“Right now we are focused on making Ray a standard for building applications,” said Stoica in an interview. “The company will build tools and a runtime platform for Ray. So, if you want to run a Ray application securely and with high performance then you will use our product.”
The funding is partly strategic: Intel is one of the big companies that has been using Ray for its own computing projects, alongside Amazon, Microsoft and Ant Financial.
“Intel IT has been leveraging Ray to scale Python workloads with minimal code modifications,” said Moty Fania, principal engineer and chief technology officer for Intel IT’s Enterprise and Platform Group, in a statement. “With the implementation into Intel’s manufacturing and testing processes, we have found that Ray helps increase the speed and scale of our hyperparameter selection techniques and auto modeling processes used for creating personalized chip tests. For us, this has resulted in reduced costs, additional capacity and improved quality.”
With an impressive user list like this for the free-to-use Ray, you might ask yourself, what is the purpose of Anyscale? As Stoica and Nishihara explained, the idea will be to create simpler and easier ways to implement Ray, to make it usable whether you’re one of the Amazons of the world, or a more modest, and possibly less tech-centric operation.
“We see that this will be valuable mostly for companies who do not have engineering experts,” Stoica said.
The problem that Anyscale is solving is a central one to the future of large-scale, involved computing projects: there are an increasing array of problems that are being tackled with computing solutions, but as the complexity of the work involved increases, there is a limit to how much work a single machine (even a big one) can handle. (Indeed, Anyscale cites IDC figures estimating that the amount of data created and copied annually will reach 175 zettabytes by 2025.)
While one day there may be quantum-computing machines that can run efficiently and at scale to address these kinds of tasks, today this isn’t a realistic option, and so distributed computing has emerged as a solution.
Ray was devised as a standard to use to implement distributed computing environments, but on its own it’s too technical for the uninitiated to use.
“Imagine you’re a biologist,” added Nishihara. “You can write a simple program and run it at a large scale, but to do that successfully you need not only to be a biology expert but a computing expert. That’s just way too high a barrier.”
The people behind Anyscale (and Ray) have a long and very credible list of other work behind them that speaks to the opportunities that are being spotted here. Stoica, for example, was also the co-founder of Databricks, Conviva and one of the original developers of Apache Spark.
“I worked on Databricks with Ion and that’s how it started,” Andreessen Horowitz co-founder Ben Horowitz said in an interview. He added that the firm has been a regular investor into projects coming out of UC Berkeley. Ray, and more specifically Anyscale, is notable for its relevance to today’s computing needs.
“With Ray it was a very attractive project because of the open-source metrics but also because of the issue it addresses,” he said.
“We’ve been grappling with Moore’s Law being over, but more interestingly, it’s inadequate for things like artificial intelligence applications,” where increasing computing power is needed that outstrips what any single machine can do. “You have to be able to deal with distributed computing, but the problem for everyone but Google is that distributed computing is hard, so we have been looking for a solution.”
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Amazon launches Audible Suno free app featuring short-stories in India
Amazon is having another go at expanding its reach to listeners in India. The company, which launched pay-to-use Audible in the country last year, today introduced a new service called Audible Suno that offers free access to “hundreds of hours of audio entertainment, enlightenment and learning.”
And it’s banking on major Indian celebrities to draw the listeners.
Audible Suno, which is exclusively available to users in India, features more than 60 original and exclusive episodes (of 20 to 60 minutes in length) in both Hindi and English languages. Audible, the world’s largest seller and producer of audio content, said Suno is aimed at filling the “idle time” listeners have each day during their commutes and performing other daily chores.
The company says Audible Suno, available to users through a dedicated Android app and via iOS Audible app, is also free of advertisements.
The launch of Audible Suno in India illustrates the commitment the company has in the country, said Audible founder and chief executive Don Katz. Amazon has invested more than $5.5 billion in its business in India to date. The company’s tentacles today reach a number of categories in the country, including e-commerce, payments, online ticketing business, video and audio streaming and VC deals.
“I’ve always been passionate about the transformative power of the spoken word, and I’m delighted to be able to offer this breadth of famous voices and culturally resonant genres with unlimited access, ad-free and free of charge,” said Katz.
Who are these famous voices you ask? Here’s the list: Amitabh Bachchan, Katrina Kaif, Karan Johar, Anil Kapoor, Farhan Akhtar, Mouni Roy, Anurag Kashyap, Neelesh Misra, Tabu, Nawazuddin Siddiqui, Diljit Dosanjh, Vir Das and Vicky Kaushal.
Audible Suno currently offers shows in a range of genres, including horror (Kaali Awaazein), romance and relationships (Matrimonial Anonymous and Piya Milan Chowk), suspense (Thriller Factory) and comedy series (The Unexperts by Abish Mathew). Non-fiction series include interviews with some of the country’s biggest stars, and socially relevant subjects such as mental health, sex education and the rights of the LGBTQI+ community.
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New Amazon tool simplifies delivery of containerized machine learning models
As part of the flurry of announcements coming this week out of AWS re:Invent, Amazon announced the release of Amazon SageMaker Operators for Kubernetes, a way for data scientists and developers to simplify training, tuning and deploying containerized machine learning models.
Packaging machine learning models in containers can help put them to work inside organizations faster, but getting there often requires a lot of extra management to make it all work. Amazon SageMaker Operators for Kubernetes is supposed to make it easier to run and manage those containers, the underlying infrastructure needed to run the models and the workflows associated with all of it.
“While Kubernetes gives customers control and portability, running ML workloads on a Kubernetes cluster brings unique challenges. For example, the underlying infrastructure requires additional management such as optimizing for utilization, cost and performance; complying with appropriate security and regulatory requirements; and ensuring high availability and reliability,” AWS’ Aditya Bindal wrote in a blog post introducing the new feature.
When you combine that with the workflows associated with delivering a machine learning model inside an organization at scale, it becomes part of a much bigger delivery pipeline, one that is challenging to manage across departments and a variety of resource requirements.
This is precisely what Amazon SageMaker Operators for Kubernetes has been designed to help DevOps teams do. “Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale,” Bindal wrote.
The promise of Kubernetes is that it can orchestrate the delivery of containers at the right moment, but if you haven’t automated delivery of the underlying infrastructure, you can over (or under) provision and not provide the correct amount of resources required to run the job. That’s where this new tool, combined with SageMaker, can help.
“With workflows in Amazon SageMaker, compute resources are pre-configured and optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near 100% utilization,” Bindal wrote.
Amazon SageMaker Operators for Kubernetes are available today in select AWS regions.
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New Amazon capabilities put machine learning in reach of more developers
Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.
While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.
By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.
“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.
For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.
The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.
The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.
“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.
Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.
As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.
“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.
As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.
Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.
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