May
14
2019
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Algorithmia raises $25M Series B for its AI automation platform

Algorithmia, a Seattle-based startup that offers a cloud-agnostic AI automation platform for enterprises, today announced a $25 million Series B funding round led by Norwest Partners. Madrona, Gradient Ventures, Work-Bench, Osage University Partners and Rakuten Ventures also participated in this round.

While the company started out five years ago as a marketplace for algorithms, it now mostly focuses on machine learning and helping enterprises take their models into production.

“It’s actually really hard to productionize machine learning models,” Algorithmia CEO Diego Oppenheimer told me. “It’s hard to help data scientists to not deal with data infrastructure but really being able to build out their machine learning and AI muscle.”

To help them, Algorithmia essentially built out a machine learning DevOps platform that allows data scientists to train their models on the platform and with the framework of their choice, bring it to Algorithmia — a platform that has already been blessed by their IT departments — and take it into production.

“Every Fortune 500 CIO has an AI initiative but they are bogged down by the difficulty of managing and deploying ML models,” said Rama Sekhar, a partner at Norwest Venture Partners, who has now joined the company’s board. “Algorithmia is the clear leader in building the tools to manage the complete machine learning life cycle and helping customers unlock value from their R&D investments.”

With the new funding, the company will double down on this focus by investing in product development to solve these issues, but also by building out its team, with a plan to double its headcount over the next year. A year from now, Oppenheimer told me, he hopes that Algorithmia will be a household name for data scientists and, maybe more importantly, their platform of choice for putting their models into production.

“How does Algorithmia succeed? Algorithmia succeeds when our customers are able to deploy AI and ML applications,” Oppenheimer said. “And although there is a ton of excitement around doing this, the fact is that it’s really difficult for companies to do so.”

The company previously raised a $10.5 million Series A round led by Google’s AI fund. It’s customers now include the United Nations, a number of U.S. intelligence agencies and Fortune 500 companies. In total, more than 90,000 engineers and data scientists are now on the platform.

May
02
2019
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Microsoft launches a drag-and-drop machine learning tool

Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users.

Getting started with machine learning is hard. Even to run the most basic of experiments take a good amount of expertise. All of these new tools great simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.

The new interface for Azure’s automated machine learning tool makes creating a model as easy importing a data set and then telling the service which value to predict. Users don’t need to write a single line of code, while in the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. While most of this is automated, Microsoft stresses that the service provides “complete transparency into algorithms, so developers and data scientists can manually override and control the process.”

For those who want a bit more control from the get-go, Microsoft also today launched a visual interface for its Azure Machine Learning service into preview that will allow developers to build, train and deploy machine learning models without having to touch any code.

This tool, the Azure Machine Learning visual interface looks suspiciously like the existing Azure ML Studio, Microsoft’s first stab at building a visual machine learning tool. Indeed, the two services look identical. The company never really pushed this service, though, and almost seemed to have forgotten about it despite that fact that it always seemed like a really useful tool for getting started with machine learning.

Microsoft says that this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is almost identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding that services’ security, deployment and lifecycle management capabilities.

The service provides an easy interface for cleaning up your data, training models with the help of different algorithms, evaluating them and, finally, putting them into production.

While these first two services clearly target novices, the new hosted notebooks in Azure Machine Learning are clearly geared toward the more experiences machine learning practitioner. The notebooks come pre-packaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure, enterprise-ready environment.” While using these notebooks isn’t trivial either, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.

Apr
10
2019
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The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

Apr
03
2019
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Okta unveils $50M in-house venture capital fund

Identity management software provider Okta, which went public two years ago in what was one of the first pure-cloud subscription-based company IPOs, wants to fund the next generation of identity, security and privacy startups.

At its big customer conference Oktane, where the company has also announced a new level of identity protection at the server level, chief operating officer Frederic Kerrest (pictured above, right, with chief executive officer Todd McKinnon) will unveil a $50 million investment fund meant to back early-stage startups leveraging artificial intelligence, machine learning and blockchain technology.

“We view this as a natural extension of what we are doing today,” Okta senior vice president Monty Gray told TechCrunch. Gray was hired last year to oversee corporate development, i.e. beef up Okta’s M&A strategy.

Gray and Kerrest tell TechCrunch that Okta Ventures will invest capital in existing Okta partners, as well as other companies in the burgeoning identity management ecosystem. The team managing the fund will look to Okta’s former backers, Sequoia, Andreessen Horowitz and Greylock, for support in the deal sourcing process.

Okta Ventures will write checks sized between $250,000 and $2 million to eight to 10 early-stage businesses per year.

“It’s just a way of making sure we are aligning all our work and support with the right companies who have the right vision and values because there’s a lot of noise around identity, ML and AI,” Kerrest said. “It’s about formalizing the support strategy we’ve had for years and making sure people are clear of the fact we are helping these organizations build because it’s helpful to our customers.”

Okta Ventures’ first bet is Trusted Key, a blockchain-based digital identity platform that previously raised $3 million from Founders Co-Op. Okta’s investment in the startup, founded by former Microsoft, Oracle and Symantec executives, represents its expanding interest in the blockchain.

“Blockchain as a backdrop for identity is cutting edge if not bleeding edge,” Gray said.

Okta, founded in 2009, had raised precisely $231 million from Sequoia, Andreessen Horowitz, Greylock, Khosla Ventures, Floodgate and others prior to its exit. The company’s stock has fared well since its IPO, debuting at $17 per share in 2017 and climbing to more than $85 apiece with a market cap of $9.6 billion as of Tuesday closing.

Mar
28
2019
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Vizion.ai launches its managed Elasticsearch service

Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.

Vizion.ai’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.

Vizion.ai GM and VP Geoff Tudor

“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.

What VVizion.ai hasdone here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.

There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.

He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”

Dec
05
2018
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Workato raises $25M for its integration platform

Workato, a startup that offers an integration and automation platform for businesses that competes with the likes of MuleSoft, SnapLogic and Microsoft’s Logic Apps, today announced that it has raised a $25 million Series B funding round from Battery Ventures, Storm Ventures, ServiceNow and Workday Ventures. Combined with its previous rounds, the company has now received investments from some of the largest SaaS players, including Salesforce, which participated in an earlier round.

At its core, Workato’s service isn’t that different from other integration services (you can think of them as IFTTT for the enterprise), in that it helps you to connect disparate systems and services, set up triggers to kick off certain actions (if somebody signs a contract on DocuSign, send a message to Slack and create an invoice). Like its competitors, it connects to virtually any SaaS tool that a company would use, no matter whether that’s Marketo and Salesforce, or Slack and Twitter. And like some of its competitors, all of this can be done with a drag-and-drop interface.

What’s different, Workato founder and CEO Vijay Tella tells me, is that the service was built for business users, not IT admins. “Other enterprise integration platforms require people who are technical to build and manage them,” he said. “With the explosion in SaaS with lines of business buying them — the IT team gets backlogged with the various integration needs. Further, they are not able to handle all the workflow automation needs that businesses require to streamline and innovate on the operations.”

Battery Ventures’ general partner Neeraj Agrawal also echoed this. “As we’ve all seen, the number of SaaS applications run by companies is growing at a very rapid clip,” he said. “This has created a huge need to engage team members with less technical skill-sets in integrating all these applications. These types of users are closer to the actual business workflows that are ripe for automation, and we found Workato’s ability to empower everyday business users super compelling.”

Tella also stressed that Workato makes extensive use of AI/ML to make building integrations and automations easier. The company calls this Recipe Q. “Leveraging the tens of billions of events processed, hundreds of millions of metadata elements inspected and hundreds of thousands of automations that people have built on our platform — we leverage ML to guide users to build the most effective integration/automation by recommending next steps as they build these automations,” he explained. “It recommends the next set of actions to take, fields to map, auto-validates mappings, etc. The great thing with this is that as people build more automations — it learns from them and continues to make the automation smarter.”

The AI/ML system also handles errors and offers features like sentiment analysis to analyze emails and detect their intent, with the ability to route them depending on the results of that analysis.

As part of today’s announcement, the company is also launching a new AI-enabled feature: Automation Editions for sales, marketing and HR (with editions for finance and support coming in the future). The idea here is to give those departments a kit with pre-built workflows that helps them to get started with the service without having to bring in IT.

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