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June 16, 2019
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Microsoft makes a push to simplify machine learning

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Ahead of its Build conference, Microsoft today released a slew of new machine learning products and tweaks to some of its existing services. These range from no-code tools to hosted notebooks, with a number of new APIs and other services in-between. The core theme, here, though, is that Microsoft is continuing its strategy of democratizing access to AI.

Ahead of the release, I sat down with Microsoft’s Eric Boyd, the company’s corporate vice president of its AI platform, to discuss Microsoft’s take on this space, where it competes heavily with the likes of Google and AWS, as well as numerous, often more specialized startups. And to some degree, the actual machine learning technologies have become table stakes. Everybody now offers pre-trained models, open-source tools and the platforms to train, build and deploy models. If one company doesn’t have pre-trained models for some use cases that its competitors support, it’s only a matter of time before it will. It’s the auxiliary services and the overall developer experience, though, where companies like Microsoft, with its long history of developing these tools, can differentiate themselves.

Microsoft’s Eric Boyd

“AI is really impacting the way the world does business,” Boyd said. “We see 75% of commercial enterprises are doing more with AI in the next several years. It’s tripled in the last couple years, according to Gartner. And so, we’re really seeing an explosion in the amount of work that’s coming from there. As people are driving this forward, as companies are driving this forward, developers are on the front lines, trying to figure out how to move their companies forward, how to build these models and how to build these applications, and help scale with all the changes that are moving through this.”

What these companies — and their developers — need is more powerful tools that allow them to become more productive and build their models faster. At Microsoft, where these companies are often large enterprises, that also includes being able to scale up to the needs of an enterprise and offer the security guarantees they need.

As companies start adopting machine learning, though, they are now also getting to a point where they have moved from a few tests to maybe running a hundred models in production. That comes with its own challenges. “They are trying to figure out how to manage the life cycle of these models,” he said. “How do I think of the operational cycle? How do I think about a new model that I’m ready to deploy? When is it ready to go?”

Only a few years ago, the industry started moving to a DevOps model for managing code. What Microsoft essentially wants to move to is MLOps for managing models. “It’s very similar to DevOps, but there’s some distinct differences in terms of how the tools operate,” Boyd noted. “At Microsoft, we’re really focusing on how do we solve these problems to make developers way more productive, using these enterprise tools to drive these changes that they need across their organization.” This means thinking about how to bring concepts like source control and continuous development to machine learning models, for example, and that will take new tools.

It’s no surprise then that adding more MLOps capabilities is a major part of today’s releases. The company is integrating some of these functions into Azure DevOps, for example, that allows them to trigger release pipelines. The company is also giving developers and data scientists tools for model version control, for example, to track and manage their assets and to share machine learning pipelines.

These are very much tools for advanced machine learning practitioners, though. On the other side of the spectrum, Microsoft also announced a number of automated machine learning tools, including one that essentially automates all of the processes, as well as a visual model builder, which grew out of the Azure ML Studio. As Boyd told me, even companies like British Petroleum and Oregon’s Deschutes Brewery (try their Black Butte Porter if you get a chance) now use these tools.

“We’ve added a bunch of features into automated machine learning to simplify how people are trying to use this kind of work,” Boyd noted.

Microsoft today also launched a number of new services in its Cognitive Services lineup, including a new personalization service, an API for recognizing handwriting and another one for transcribing conversations with multiple speakers. The personalization service stands out here because it uses reinforcement learning, a different machine learning technique from most other Cognitive Services tools, and because it is far easier to implement than similar services. For business users, there’s also the Form Recognizer, which makes extracting data from forms easy.

What’s more interesting that the specific features, though, is that Microsoft is shifting its emphasis here a little bit. “We’re moving away from some of the first-level problems of ‘here’s the table stakes, you have to have an AI platform,’ to much more sophisticated use cases around the operations of these algorithms, the simplification of them, new user experiences to really simplify how developers work and much richer cognitive services,” Boyd explained.

Idera acquires Travis CI

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Travis CI, the popular Berlin-based open source continuous integration service, has been acquired by Idera, a company that offers a number of SQL database management and administration tools for both on-premises and cloud applications. The move comes at a time where other continuous integration services, including the likes of Circle CI, seem to be taking market share away from Travis CI.

Idera, which itself is owned by private equity firm TA Associates, says that Travis is complementary to its current testing tools business and that the acquisition will benefit its current customers. Idera’s other tools in its Testing Tools division are TestRail, Ranorex and Kiuwan. “We admire the business value driven by Travis CI and look forward to helping more customers achieve better and faster results,” said Suhail Malhotra, Idera’s General Manager for Travis CI .

Idera clearly wants to move into the DevOps business and continuous integration is obviously a major building block. This still feels like a bit of an odd acquisition, given that Idera isn’t exactly known for being on the leading edge of today’s technology (if it’s known at all). But Travis CI also brings 700,000 users to Idera and customers like IBM and Zendesk, so while we don’t know the cost of the acquisition, this is a big deal in the CI ecosystem.

“We are excited about our next chapter of growth with the Idera team,” said Konstantin Haase, a founder of Travis CI, in today’s announcement. “Our customers and partners will benefit from Idera’s highly complementary portfolio and ability to scale software businesses to the next level. Our goal is to attract as many users to Travis CI as possible, while staying true to our open source roots and community.”

That’s pretty much what all founders write (or what the acquiring company’s PR team writes for them), so we’ll have to see how Idera will steer Travis CI going forward.

In his blog post, Haase says that nothing will change for Travis CI users. “With the support from our new partners, we will be able to invest in expanding and improving our core product, to have Travis CI be the best Continuous Integration and Development solution for software projects out there,” he writes and also notes that the Travis CI will stay open source. “This is who we are, this is what made us successful.”

Chaos engineering service Gremlin raises $18m, launches new resiliency tools

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“Slack is down.” It’s a headline we have had blaring at TechCrunch on numerous occasions (mostly because we actually get work done when not distracted by a constant waterfall of GIFs). But Slack is not alone — issues with uptime and reliability plague modern web services, from Alexa to WhatsApp to Apple Maps.

As any software engineer can atest, web application development is extraordinarily complicated. Databases, storage services, and business logic all need to work together perfectly so that users can buy their goods or watch their films.

But what happens when one piece of that application breaks down? Today, a small outage in one AWS availability zone could cascade and knock an entire service offline, as we have seen repeatedly. Today’s developer tools are decent at spotting bugs and other logic errors, but they don’t investigate applications systematically to ask how they can respond to various crises.

That’s where Gremlin comes in. The service, founded by CEO Kolton Andrus, who designed Netflix’s failure injection service and worked with CTO Matthew Fornaciari while at Amazon, is designed to throw a monkey wrench into any application, simulating faults like storage errors, database congestion, and sudden spikes in latency. It’s tagline is “break things on purpose” (something of a rift of Facebook’s “move fast and break things”).

Resiliency is clearly on investors’ minds, since the startup announced this morning at its Chaos Conf in SF that it has raised a $18 million Series B round led by Redpoint partner Tomasz Tunguz. That’s a follow-up to a $7.5 million series A led by Index Ventures partner Mike Volpi, which was announced less than a year ago.

In addition to announcing the funding today, the company unveiled its “Application Level Fault Injection” system — a mouthful of a name, but a feature that will help DevOps engineers test systems at the application level, including most importantly serverless environments.

Andrus said in a note to TechCrunch that “This past year has been a whirlwind. We spent a lot of time educating everyone from engineers to CIOs about chaos engineering and building up the community.” He said the new funding will be used to further build out Gremlin’s engineering team.

As I wrote about in-depth a few months ago, Gremlin is pioneering a field of software development dubbed “chaos engineering.” Rather than using formal verification to test whether code is accurate and performant, chaos engineers throw deliberate and systematic errors at an application in an attempt to simulate various types of failure and find brittle parts of software programs.

That sounds easy on the surface, but extremely complicated in practice: you want to simulate an outage without actually creating an outage on a mission-critical system. Netflix wants to test whether losing a database will cause video to stop playing, without physically pulling the plug on a database and seeing if your movie is still on the TV.

Gremlin’s platform provides something of a sandbox for engineers to slowly ramp up errors, and then more importantly, ramp down errors if a breakage is detected. So a DevOps engineer can add a few milliseconds of latency to a program and see how it responds, and then add a few more.

With the rise of serverless services like AWS Lambda, the complexity around applications gets even more challenging. Now, applications aren’t just on a single instance, but individual functions could be scattered across multiple instances and potentially multiple data centers. That can save developer time and reduce costs, but it also exponentially increases the risk of something going wrong and harming an application’s reliability.

Gremlin’s new ALFI feature is designed to allow more fine-grain tuning of attacks, so that DevOps engineers can target just particular aspects of an application living in a serverless environment. It’s inspired by Andrus’ work at Netflix around Failure Injection Testing, which was a sort of successor to the company’s earlier Chaos Monkey tools.

Gremlin’s ALFI feature allows developers to simulate more fine-grained failures.

It’s these sorts of features that partly intrigued Tunguz at Redpoint, who is well-known for his thoughts on SaaS. He said in a note to TechCrunch that “In the modern cloud era — where systems are distributed, containerized, and highly ephemeral — it’s become nearly impossible to have a complete understanding of system behavior without doing the kind of proactive testing Gremlin offers.”

Gremlin’s work is to not just sell a service, but to reshape how developers think about building and testing applications. Perhaps someday all of our web services will be reliable – and then how will we get work done?

Atlassian launches Jira Ops for managing incidents

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Atlassian today announced the first beta of a new edition of its flagship Jira project and issue tracking tool that is meant to help ops teams handle incidents faster and more efficiently.

Jira Ops integrates with tools like OpsGenie, PagerDuty, xMatters, Statuspage, Slack and others. Many teams already use these tools when their services go down, but Atlassian argues that most companies currently use a rather ad hoc approach to working with them. Jira Ops aims to be the glue that keeps everybody on the same page and provides visibility into ongoing incidents.

This is obviously not the first time Atlassian is using Jira to branch out from its core developer audience. Jira Service Desk and Jira Core, for example, aim at a far broader audience. Ops, however, goes after a very specific vertical.

“Service Desk was the first step,” Jens Schumacher, Head of Software Teams at Atlassian, told me. And we were looking at what are the other verticals that we can attack with Jira.” Schumacher also noted that Atlassian built a lot of tools for its internal ops teams over the years to glue together all the different pieces that are necessary to track and manage incidents. With Jira Ops, the company is essentially turning its own playbook into a product.

In a way, though, using Jira Ops adds yet another piece to the puzzle. Schumacher, however, argues that the idea here is to have a single place to manage the process. “The is that when an incident happens, you have a central place where you can go, where you can find out everything about the incident,” he said. “You can see who has been paged and alerted; you can alert more people if you need to right from there; you know what Slack channel the incident is being discussed in.”

Unlike some of Atlassian’s other products, the company doesn’t currently have any plans to launch a self-hosted version of Jira Ops. The argument here is pretty straightforward: if your infrastructure goes down, then Jira Opes could also go do down — and then you don’t have a tool for managing that downtime.

Jira Ops is now available for free for early access beta users. The company expects to launch version 1.0 in early 2019. By then Atlassian will surely also have figured out a pricing plan, something it didn’t announce today.

Codefresh raises $8M Series B round for its container-centric CI/CD platform

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Codefresh, a continuous integration and delivery platform built for the Kubernetes container ecosystem, today announced that it has raised an $8 million Series B round led by M12, Microsoft’s venture fund. Viola Ventures, Hillsven and CEIF also participated in this round, which brings the company’s total funding to $15.1 million.

In a market where there are seemingly more CI/CD platforms every day, Codefresh sets itself apart thanks to its focus on Kubernetes, which is now essentially the de facto standard for container orchestration services and which is seeing a rapid growth in adoption. The service promises that it can help developers to automate their application deployments to Kubernetes and that teams will see “up to 24X faster development times.” That number seems a bit optimistic, but the whole point of adoption Kubernetes and CI/CD is obviously to speed up the development and deployment process.

“The meteoric rise of Kubernetes is happening so fast that most toolchains haven’t kept up, and M12 knows it,” said Raziel Tabib, Codefresh co-founder and CEO. “With this latest round of funding we’re going to aggressively accelerate our roadmap and expand our customer base.”

The Codefresh platform hit general availability in 2017 and the company currently claims about 20,000 users, including the likes Giphy.

 

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