ML

Auto Added by WPeMatico

Abacus.AI raises another $22M and launches new AI modules

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.

Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.

Image Credits: Abacus.AI

It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups,” Yanda Erlich, a p artner at Coatue Ventures  told me. “Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”

 

Powered by WPeMatico

AI-tool maker Seldon raises £7.1M Series A from AlbionVC and Cambridge Innovation Capital

Seldon is a U.K. startup that specializes in the rarified world of development tools to optimize machine learning. What does this mean? Well, dear reader, it means that the “AI” that companies are so fond of trumpeting does actually end up working.

It has now raised a £7.1 million Series A round co-led by AlbionVC and Cambridge Innovation Capital . The round also includes significant participation from existing investors Amadeus Capital Partners and Global Brain, with follow-on investment from other existing shareholders. The £7.1 million funding will be used to accelerate R&D and drive commercial expansion, take Seldon Deploy — a new enterprise solution — to market and double the size of the team over the next 18 months.

More accurately, Seldon is a cloud-agnostic machine learning (ML) deployment specialist which works in partnership with industry leaders such as Google, Red Hat, IBM and Amazon Web Services.

Key to its success is that its open-source project Seldon Core has more than 700,000 models deployed to date, drastically reducing friction for users deploying ML models. The startup says its customers are getting productivity gains of as much as 92% as a result of utilizing Seldon’s product portfolio.

Alex Housley, CEO and founder of Seldon speaking to TechCrunch explained that companies are using machine learning across thousands of use cases today, “but the model actually only generates real value when it’s actually running inside a real-world application.”

“So what we’ve seen emerge over these last few years are companies that specialize in specific parts of the machine learning pipeline, such as training version control features. And in our case we’re focusing on deployment. So what this means is that organizations can now build a fully bespoke AI platform that suits their needs, so they can gain a competitive advantage,” he said.

In addition, he said Seldon’s open-source model means that companies are not locked-in: “They want to avoid locking as well they want to use tools from various different vendors. So this kind of intersection between machine learning, DevOps and cloud-native tooling is really accelerating a lot of innovation across enterprise and also within startups and growth-stage companies.”

Nadine Torbey, an investor at AlbionVC, added: “Seldon is at the forefront of the next wave of tech innovation, and the leadership team are true visionaries. Seldon has been able to build an impressive open-source community and add immediate productivity value to some of the world’s leading companies.”

Vin Lingathoti, partner at Cambridge Innovation Capital, said: “Machine learning has rapidly shifted from a nice-to-have to a must-have for enterprises across all industries. Seldon’s open-source platform operationalizes ML model development and accelerates the time-to-market by eliminating the pain points involved in developing, deploying and monitoring machine learning models at scale.”

Powered by WPeMatico

Arrikto raises $10M for its MLOps platform

Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.

“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”

Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.

Image Credits: Arrikto

“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.

Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.

Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto

At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.

As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.

While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.

And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.

“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”

Image Credits: Arrikto

Powered by WPeMatico

Dataloop raises $11M Series A round for its AI data management platform

Dataloop, a Tel Aviv-based startup that specializes in helping businesses manage the entire data life cycle for their AI projects, including helping them annotate their data sets, today announced that it has now raised a total of $16 million. This includes a $5 seed round that was previously unreported, as well as an $11 million Series A round that recently closed.

The Series A round was led by Amiti Ventures, with participation from F2 Venture Capital, crowdfunding platform OurCrowd, NextLeap Ventures and SeedIL Ventures.

“Many organizations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real-time validation that can only be achieved with human input into the system,” said Dataloop CEO Eran Shlomo. “With this investment, we are committed, along with our partners, to overcoming these roadblocks and providing next generation data management tools that will transform the AI industry and meet the rising demand for innovation in global markets.”

Image Credits: Dataloop

For the most part, Dataloop specializes in helping businesses manage and annotate their visual data. It’s agnostic to the vertical its customers are in, but we’re talking about anything from robotics and drones to retail and autonomous driving.

The platform itself centers around the “humans in the loop” model that complements the automated systems, with the ability for humans to train and correct the model as needed. It combines the hosted annotation platform with a Python SDK and REST API for developers, as well as a serverless Functions-as-a-Service environment that runs on top of a Kubernetes cluster for automating dataflows.

Image Credits: Dataloop

The company was founded in 2017. It’ll use the new funding to grow its presence in the U.S. and European markets, something that’s pretty standard for Israeli startups, and build out its engineering team as well.

Powered by WPeMatico

Grid AI raises $18.6M Series A to help AI researchers and engineers bring their models to production

Grid AI, a startup founded by the inventor of the popular open-source PyTorch Lightning project, William Falcon, that aims to help machine learning engineers work more efficiently, today announced that it has raised an $18.6 million Series A funding round, which closed earlier this summer. The round was led by Index Ventures, with participation from Bain Capital Ventures and firstminute. 

Falcon co-founded the company with Luis Capelo, who was previously the head of machine learning at Glossier. Unsurprisingly, the idea here is to take PyTorch Lightning, which launched about a year ago, and turn that into the core of Grid’s service. The main idea behind Lightning is to decouple the data science from the engineering.

The time argues that a few years ago, when data scientists tried to get started with deep learning, they didn’t always have the right expertise and it was hard for them to get everything right.

“Now the industry has an unhealthy aversion to deep learning because of this,” Falcon noted. “Lightning and Grid embed all those tricks into the workflow so you no longer need to be a PhD in AI nor [have] the resources of the major AI companies to get these things to work. This makes the opportunity cost of putting a simple model against a sophisticated neural network a few hours’ worth of effort instead of the months it used to take. When you use Lightning and Grid it’s hard to make mistakes. It’s like if you take a bad photo with your phone but we are the phone and make that photo look super professional AND teach you how to get there on your own.”

As Falcon noted, Grid is meant to help data scientists and other ML professionals “scale to match the workloads required for enterprise use cases.” Lightning itself can get them partially there, but Grid is meant to provide all of the services its users need to scale up their models to solve real-world problems.

What exactly that looks like isn’t quite clear yet, though. “Imagine you can find any GitHub repository out there. You get a local copy on your laptop and without making any code changes you spin up 400 GPUs on AWS — all from your laptop using either a web app or command-line-interface. That’s the Lightning “magic” applied to training and building models at scale,” Falcon said. “It is what we are already known for and has proven to be such a successful paradigm shift that all the other frameworks like Keras or TensorFlow, and companies have taken notice and have started to modify what they do to try to match what we do.”

The service is now in private beta.

With this new funding, Grid, which currently has 25 employees, plans to expand its team and strengthen its corporate offering via both Grid AI and through the open-source project. Falcon tells me that he aims to build a diverse team, not in the least because he himself is an immigrant, born in Venezuela, and a U.S. military veteran.

“I have first-hand knowledge of the extent that unethical AI can have,” he said. “As a result, we have approached hiring our current 25 employees across many backgrounds and experiences. We might be the first AI company that is not all the same Silicon Valley prototype tech-bro.”

“Lightning’s open-source traction piqued my interest when I first learned about it a year ago,” Index Ventures’ Sarah Cannon told me. “So intrigued in fact I remember rushing into a closet in Helsinki while at a conference to have the privacy needed to hear exactly what Will and Luis had built. I promptly called my colleague Bryan Offutt who met Will and Luis in SF and was impressed by the ‘elegance’ of their code. We swiftly decided to participate in their seed round, days later. We feel very privileged to be part of Grid’s journey. After investing in seed, we spent a significant amount with the team, and the more time we spent with them the more conviction we developed. Less than a year later and pre-launch, we knew we wanted to lead their Series A.”

Powered by WPeMatico

WhyLabs brings more transparancy to ML ops

WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.

The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.

Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.

“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So I thought, “Okay, how bad could it be? I carried the pager for the retail website before. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why issues were happening.”

Image Credits: WhyLabs

But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”

The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.

For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.

Image Credits: WhyLabs

According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.

“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”

Powered by WPeMatico

Join us Wednesday, September 9 to watch Techstars Starburst Space Accelerator demo day live

The 2020 class of Techstars Starburst Space Accelerator is graduating with an official demo day on Wednesday at 10 a.m. PDT (1 p.m. EDT), and you can watch all the teams present their startups live via the stream above. This year’s class includes 10 companies building innovative new solutions to challenges either directly or indirectly related to commercial space.

Techstars Starburst is a program with a lot of heavyweight backing from both private industry and public agencies, including from NASA’s JPL, the U.S. Air Force, Lockheed Martin, Maxar Technologies, SAIC, Israel Aerospace Industries North America and The Aerospace Corporation. The program, led by managing director Matt Kozlov, is usually based locally in LA, where much of the space industry has significant presence, but this year the demo day is going online due to the ongoing COVID-19 situation.

Few, if any, programs out there can claim such a broad representation of big-name partners from across commercial, military and general civil space in terms of stakeholders, which is the main reason it manages to attract a range of interesting startups.  This is the second class of graduating startups from the Starburst Space Accelerator; last year’s batch included some exceptional standouts like in-orbit refueling company Orbit Fab (also a TechCrunch Battlefield participant), imaging microsatellite company Pixxel and satellite propulsion company Morpheus.

As for this year’s class, you can check out a full list of all 10 participating companies below. The demo day presentations begin tomorrow, September 9 at 10 a.m. PDT/1 p.m. PDT, so you can check back in here then to watch live as they provide more details about what it is they do.

Bifrost

A synthetic data API that allows AI teams to generate their own custom datasets up to 99% faster — no tedious collection, curation or labelling required.
founders@bifrost.ai

Holos Inc.

A virtual reality content management system that makes it super easy for curriculum designers to create and deploy immersive learning experiences.
founders@holos.io

Infinite Composites Technologies

The most efficient gas storage systems in the universe.
founders@infinitecomposites.com

Lux Semiconductors

Lux is developing next generation System-on-Foil electronics.
founders@luxsemiconductors.com

Natural Intelligence Systems, Inc.

Developer of next-generation pattern-based AI/ML systems.
leadership@naturalintelligence.ai

Prewitt Ridge

Engineering collaboration software for teams building challenging deep tech projects.
founders@prewittridge.com

SATIM

Providing satellite radar-based intelligence for decision makers.
founders@satim.pl

Urban Sky

Developing stratospheric microballoons to capture the freshest, high-res earth observation data.
founders@urbansky.space

vRotors

Real-time remote robotic controls.
founders@vrotors.com

WeavAir

Proactive air insights.
founders@weavair.com

Powered by WPeMatico

Typewise taps $1M to build an offline next word prediction engine

Swiss keyboard startup Typewise has bagged a $1 million seed round to build out a typo-busting, ‘privacy-safe’ next word prediction engine designed to run entirely offline. No cloud connectivity, no data mining risk is the basic idea.

They also intend the tech to work on text inputs made on any device, be it a smartphone or desktop, a wearable, VR — or something weirder that Elon Musk might want to plug into your brain in future.

For now they’ve got a smartphone keyboard app that’s had around 250,000 downloads — with some 65,000 active users at this point.

The seed funding breaks down into $700K from more than a dozen local business angels; and $340K via the Swiss government through a mechanism (called “Innosuisse projects“), akin to a research grant, which is paying for the startup to employ machine learning experts at Zurich’s ETH research university to build out the core AI.

The team soft launched a smartphone keyboard app late last year, which includes some additional tweaks (such as an optional honeycomb layout they tout as more efficient; and the ability to edit next word predictions so the keyboard quickly groks your slang) to get users to start feeding in data to build out their AI.

Their main focus is on developing an offline next word prediction engine which could be licensed for use anywhere users are texting, not just on a mobile device.

“The goal is to develop a world-leading text prediction engine that runs completely on-device,” says co-founder David Eberle. “The smartphone keyboard really is a first use case. It’s great to test and develop our algorithms in a real-life setting with tens of thousands of users. The larger play is to bring word/sentence completion to any application that involves text entry, on mobiles or desktop (or in future also wearables/VR/Brain-Computer Interfaces).

“Currently it’s pretty much only Google working on this (see Gmail’s auto completion feature). Applications such as Microsoft Teams, Slack, Telegram, or even SAP, Oracle, Salesforce would want such productivity increase – and at that level privacy/data security matters a lot. Ultimately we envision that every “human-machine interface” is, at least on the text-input level, powered by Typewise.”

You’d be forgiven for thinking all this sounds a bit retro, given the earlier boom in smartphone AI keyboards — such as SwiftKey (now owned by Microsoft).

The founders have also pushed specific elements of their current keyboard app — such as the distinctive honeycomb layout — before, going down a crowdfunding route back in 2015, when they were calling the concept Wrio. But they reckon it’s now time to go all in — hence relaunching the business as Typewise and shooting to build a licensing business for offline next word prediction.

“We’ll use the funds to develop advanced text predictions… first launching it in the keyboard app and then bringing it to the desktop to start building partnerships with relevant software vendors,” says Eberle, noting they’re working on various enhancements to the keyboard app and also plan to spend on marketing to try to hit 1M active users next year.

“We have more ‘innovative stuff’ [incoming] on the UX side as well, e.g. interacting with auto correction (so the user can easily intervene when it does something wrong — in many countries users just turn it off on all keyboards because it gets annoying), gamifying the general typing experience (big opportunity for kids/teenagers, also making them more aware of what and how they type), etc.”

The competitive landscape around smartphone keyboard tech, largely dominated by tech giants, has left room for indie plays, is the thinking. Nor is Typewise the only startup thinking that way (Fleksy has similar ambitions, for one). However gaining traction vs such giants — and over long established typing methods — is the tricky bit.

Android maker Google has ploughed resource into its Gboard AI keyboard — larding it with features. While, on iOS, Apple’s interface for switching to a third party keyboard is infamously frustrating and finicky; the opposite of a seamless experience. Plus the native keyboard offers next word prediction baked in — and Apple has plenty of privacy credit. So why would a user bother switching is the problem there.

Competing for smartphone users’ fingers as an indie certainly isn’t easy. Alternative keyboard layouts and input mechanism are always a very tough sell as they disrupt people’s muscle memory and hit mobile users hard in their comfort and productivity zone. Unless the user is patient and/or stubborn enough to stick with a frustratingly different experience they’ll soon ditch for the keyboard devil they know.  (‘Qwerty’ is an ancient typewriter layout turned typing habit we English speakers just can’t kick.)

Given all that, Typewise’s retooled focus on offline next word prediction to do white label b2b licensing makes more sense — assuming they can pull off the core tech.

And, again, they’re competing at a data disadvantage on that front vs more established tech giant keyboard players, even as they argue that’s also a market opportunity.

“Google and Microsoft (thanks to the acquisition of SwiftKey) have a solid technology in place and have started to offer text predictions outside of the keyboard; many of their competitors, however, will want to embed a proprietary (difficult to build) or independent technology, especially if their value proposition is focused on privacy/confidentiality,” Eberle argues.

“Would Telegram want to use Google’s text predictions? Would SAP want that their clients’ data goes through Microsoft’s prediction algorithms? That’s where we see our right to win: world-class text predictions that run on-device (privacy) and are made in Switzerland (independent environment, no security back doors, etc).”

Early impressions of Typewise’s next word prediction smarts (gleaned by via checking out its iOS app) are pretty low key (ha!). But it’s v1 of the AI — and Eberle talks bullishly of having “world class” developers working on it.

“The collaboration with ETH just started a few weeks ago and thus there are no significant improvements yet visible in the live app,” he tells TechCrunch. “As the collaboration runs until the end of 2021 (with the opportunity of extension) the vast majority of innovation is still to come.”

He also tells us Typewise is working with ETH’s Prof. Thomas Hofmann (chair of the Data Analytic Lab, formerly at Google), as well as having has two PhDs in NLP/ML and one MSc in ML contributing to the effort.

“We get exclusive rights to the [ETH] technology; they don’t hold equity but they get paid by the Swiss government on our behalf,” Eberle also notes. 

Typewise says its smartphone app supports more than 35 languages. But its next word prediction AI can only handle English, German, French, Italian and Spanish at this point. The startup says more are being added.

Powered by WPeMatico

Enterprise companies find MLOps critical for reliability and performance

Rish Joshi
Contributor

Rish is an entrepreneur and investor. Previously, he was a VC at Gradient Ventures (Google’s AI fund), co-founded a fintech startup building an analytics platform for SEC filings and worked on deep-learning research as a graduate student in computer science at MIT.

Enterprise startups UIPath and Scale have drawn huge attention in recent years from companies looking to automate workflows, from RPA (robotic process automation) to data labeling.

What’s been overlooked in the wake of such workflow-specific tools has been the base class of products that enterprises are using to build the core of their machine learning (ML) workflows, and the shift in focus toward automating the deployment and governance aspects of the ML workflow.

That’s where MLOps comes in, and its popularity has been fueled by the rise of core ML workflow platforms such as Boston-based DataRobot. The company has raised more than $430 million and reached a $1 billion valuation this past fall serving this very need for enterprise customers. DataRobot’s vision has been simple: enabling a range of users within enterprises, from business and IT users to data scientists, to gather data and build, test and deploy ML models quickly.

Founded in 2012, the company has quietly amassed a customer base that boasts more than a third of the Fortune 50, with triple-digit yearly growth since 2015. DataRobot’s top four industries include finance, retail, healthcare and insurance; its customers have deployed over 1.7 billion models through DataRobot’s platform. The company is not alone, with competitors like H20.ai, which raised a $72.5 million Series D led by Goldman Sachs last August, offering a similar platform.

Why the excitement? As artificial intelligence pushed into the enterprise, the first step was to go from data to a working ML model, which started with data scientists doing this manually, but today is increasingly automated and has become known as “auto ML.” An auto-ML platform like DataRobot’s can let an enterprise user quickly auto-select features based on their data and auto-generate a number of models to see which ones work best.

As auto ML became more popular, improving the deployment phase of the ML workflow has become critical for reliability and performance — and so enters MLOps. It’s quite similar to the way that DevOps has improved the deployment of source code for applications. Companies such as DataRobot and H20.ai, along with other startups and the major cloud providers, are intensifying their efforts on providing MLOps solutions for customers.

We sat down with DataRobot’s team to understand how their platform has been helping enterprises build auto-ML workflows, what MLOps is all about and what’s been driving customers to adopt MLOps practices now.

The rise of MLOps

Powered by WPeMatico

Freshworks acquires AnsweriQ

Customer engagement platform Freshworks today announced that it has acquired AnsweriQ, a startup that provides AI tools for self-service solutions and agent-assisted use cases where the ultimate goal is to quickly provide customers with answers and make agents more efficient.

The companies did not disclose the acquisition price. AnsweriQ last raised a funding round in 2017, when it received $5 million in a Series A round from Madrona Venture Group.

Freshworks founder and CEO Girish Mathrubootham tells me that he was introduced to the company through a friend, but that he had also previously come across AnsweriQ as a player in the customer service automation space for large clients in high-volume call centers.

“We really liked the team and the product and their ability to go up-market and win larger deals,” Mathrubootham said. “In terms of using the AI/ML customer service, the technology that they’ve built was perfectly complementary to everything else that we were building.”

He also noted the client base, which doesn’t overlap with Freshworks’, and the talent at AnsweriQ, including the leadership team, made this a no-brainer.

AnsweriQ, which has customers that use Freshworks and competing products, will continue to operate its existing products for the time being. Over time, Freshworks, of course, hopes to convert many of these users into Freshworks users as well. The company also plans to integrate AnsweriQ’s technology into its Freddy AI engine. The exact branding for these new capabilities remains unclear, but Mathrubootham suggested FreshiQ as an option.

As for the AnsweriQ leadership team, CEO Pradeep Rathinam will be joining Freshworks as chief customer officer.

Rathinam told me that the company was at the point where he was looking to raise the next round of funding. “As we were going to raise the next round of funding, our choices were to go out and raise the next round and go down this path, or look for a complementary platform on which we can vet our products and then get faster customer acquisition and really scale this to hundreds or thousands of customers,” he said.

He also noted that as a pure AI player, AnsweriQ had to deal with lots of complex data privacy and residency issues, so a more comprehensive platform like Freshworks made a lot of sense.

Freshworks has always been relatively acquisitive. Last year, the company acquired the customer success service Natero, for example. With the $150 million Series H round it announced last November, the company now also has the cash on hand to acquire even more customers. Freshworks is currently valued at about $3.5 billion and has 2,700 employees in 13 offices. With the acquisition of AnsweriQ, it now also has a foothold in Seattle, which it plans to use to attract local talent to the company.

Powered by WPeMatico