Jul
01
2019
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Tara.ai, which uses machine learning to spec out and manage engineering projects, nabs $10M

Artificial intelligence has become an increasingly important component of how a lot of technology works; now it’s also being applied to how technologists themselves work. Today, one of the startups building such a tool has raised some capital, Tara.ai, a platform that uses machine learning to help an organization get engineering projects done — from identifying and predicting the work that will need to be tackled, to sourcing talent to execute that, and then monitoring the project of that project — has raised a Series A of $10 million to continue building out its platform.

The funding for the company cofounded by Iba Masood (she is the CEO) and Syed Ahmed comes from an interesting group of investors that point to Tara’s origins, as well as how it sees its product developing over time.

The round was led by Aspect Ventures (the female-led firm that puts a notable but not exclusive emphasis on female-founded startups) with participation also from Slack, by way of its Slack Fund. Previous investors Y Combinator and Moment Ventures also participated in the round. (Y Combinator provides an avenue to companies from its cohorts to help them source their Series A rounds, and Tara.ai went through this process.)

Tara.ai was originally founded as Gradberry out of Y Combinator, with its initial focus on using an AI platform for organizations to evaluate and help source engineering talent: Tara.ai was originally that name of its AI engine.

(The origin of how Masood and Ahmed identified this problem was through their own direct experience: both were grads (she in finance, he in engineering) from the American University of Sharjah in the U.A.E. that had problems getting hired because no one had ever heard of their university. Even so, they had won an MIT-affiliated startup competition in Morocco and relocated to Boston. The idea with Gradberry was to cut through the big names and focus just on what people could do.)

Masood and Syed (who eventually got married) eventually realised that using that engine to evaluate the wider challenges of executing engineering projects came as a natural progression once the team started digging into the challenges and identifying what actually needed to be solved.

A study that McKinsey (where Masood once worked) conducted across some 5,000 projects found that $66 billion dollars were identified as “lost” due to projects running past the expected completion time, lack of adequate talent and just overall poor planning.

“We realised that recruiting was actually the final decision you make, not the first, and we wanted to be involved earlier in the decision-making process,” Masood said in an interview. “We saw a much bigger opportunity looking not at the people, but the whole project.”

In action, that means that Tara.ai is used not just to scope out the nature of the problem that needed to be solved, or the goal that an organization wanted to achieve; it is also used to suggest which frameworks will need to be used to execute on that goal, and then suggest a timeline to follow.

Then, it starts to evaluate a company’s own staff expertise, along with that from other recruiting platforms, to figure out which people to source from within the company. Eventually, that will also be complemented with sourcing information from outside the organization — either contractors or new hires.

Masood noted that a large proportion of users in the tech world today use Jira and platforms like it to manage projects. While there are some tools in Jira to help plan out projects better, Tara is proposing its platform as a kind of virtual project manager, or an assistant to an existing project manager, to conceive of the whole project, not just help with the admin of getting it done.

Notably, right now she says that some 75% of Tara.ai’s users — customers include Cisco, Orange Silicon Valley and Mower Digital — are “not technical,” meaning they themselves do not ship or use code. “This helps them understand what could be considered and the dependencies that can be expected out of a project,” she notes.

Lauren Kolodny, the partner at Aspect who led the investment, said that one of the things that stood out for her, in fact, with Tara.ai, was precisely how it could be applied exactly in those kinds of scenarios.

Today, tech is such a fundamental part of how a lot of businesses operate, but that doesn’t mean that every business is natively a technology one (think here of food and beverage companies as an example, or government agencies). In those cases, these companies would have traditionally had to turn to outside consultants to identify opportunities, and then build and potentially long-term operate whatever the solutions become. Now there is an opportunity to rethink how technology is used in these kinds of organizations.

“Projects have been hacked together from multiple systems, not really built in combination,” Kolodny said of how much development happens at these traditional businesses. “We are really excited about the machine learning scoping and mapping of internal and external talent, which is looking to be particularly important as traditional enterprises are required to get level with newer businesses, and the amount of talent they need to execute on these projects becomes challenging.”

Tara.ai’s next steps will involve essentially taking the building blocks of what you can think of as a very powerful talent and engineering project search engine, and making it more powerful. That will include integrating databases of external consultants and figuring out how best to have these in tandem with internal teams while keeping them working well together. And soon to come also will be bug prediction: how to identify these before they arise in a project. The company is releasing an updated AI engine to coincide with the funding.

Tara AI launch

The Slack investment is also a notable nod to what direction Tara.ai will take. Masood said that Slack was one of three “big tech” companies interested in investing in this round, and she and Syed chose Slack because from what they could see of its existing and target customers, many were already using it and some have already started requesting closer collaboration so that events in one could come up as updates in the other.

“Our largest customers are heavy Slack users and they are already having conversations in Slack related to projects in Tara.ai,” she said. “We are tackling the scoping element and now seeing how to link up even command line interfaces between the two.”

She noted that this does not rule out closer integrations with communications and other platforms that people use on a daily basis to get their work done: the idea is to become a tool to work better overall.

Jun
21
2019
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Three years after moving off AWS, Dropbox infrastructure continues to evolve

Conventional wisdom would suggest that you close your data centers and move to the cloud, not the other way around, but in 2016 Dropbox undertook the opposite journey. It (mostly) ended its long-time relationship with AWS and built its own data centers.

Of course, that same conventional wisdom would say, it’s going to get prohibitively expensive and more complicated to keep this up. But Dropbox still believes it made the right decision and has found innovative ways to keep costs down.

Akhil Gupta, VP of Engineering at Dropbox, says that when Dropbox decided to build its own data centers, it realized that as a massive file storage service, it needed control over certain aspects of the underlying hardware that was difficult for AWS to provide, especially in 2016 when Dropbox began making the transition.

“Public cloud by design is trying to work with multiple workloads, customers and use cases and it has to optimize for the lowest common denominator. When you have the scale of Dropbox, it was entirely possible to do what we did,” Gupta explained.

Alone again, naturally

One of the key challenges of trying to manage your own data centers, or build a private cloud where you still act like a cloud company in a private context, is that it’s difficult to innovate and scale the way the public cloud companies do, especially AWS. Dropbox looked at the landscape and decided it would be better off doing just that, and Gupta says even with a small team — the original team was just 30 people — it’s been able to keep innovating.

Apr
24
2019
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Databricks open-sources Delta Lake to make data lakes more reliable

Databricks, the company founded by the original developers of the Apache Spark big data analytics engine, today announced that it has open-sourced Delta Lake, a storage layer that makes it easier to ensure data integrity as new data flows into an enterprise’s data lake by bringing ACID transactions to these vast data repositories.

Delta Lake, which has long been a proprietary part of Databrick’s offering, is already in production use by companies like Viacom, Edmunds, Riot Games and McGraw Hill.

The tool provides the ability to enforce specific schemas (which can be changed as necessary), to create snapshots and to ingest streaming data or backfill the lake as a batch job. Delta Lake also uses the Spark engine to handle the metadata of the data lake (which by itself is often a big data problem). Over time, Databricks also plans to add an audit trail, among other things.

“Today nearly every company has a data lake they are trying to gain insights from, but data lakes have proven to lack data reliability. Delta Lake has eliminated these challenges for hundreds of enterprises. By making Delta Lake open source, developers will be able to easily build reliable data lakes and turn them into ‘Delta Lakes’,” said Ali Ghodsi, co-founder and CEO at Databricks.

What’s important to note here is that Delta lake runs on top of existing data lakes and is compatible with the Apache spark APIs.

The company is still looking at how the project will be governed in the future. “We are still exploring different models of open source project governance, but the GitHub model is well understood and presents a good trade-off between the ability to accept contributions and governance overhead,” Ghodsi said. “One thing we know for sure is we want to foster a vibrant community, as we see this as a critical piece of technology for increasing data reliability on data lakes. This is why we chose to go with a permissive open source license model: Apache License v2, same license that Apache Spark uses.”

To invite this community, Databricks plans to take outside contributions, just like the Spark project.

“We want Delta Lake technology to be used everywhere on-prem and in the cloud by small and large enterprises,” said Ghodsi. “This approach is the fastest way to build something that can become a standard by having the community provide direction and contribute to the development efforts.” That’s also why the company decided against a Commons Clause licenses that some open-source companies now use to prevent others (and especially large clouds) from using their open source tools in their own commercial SaaS offerings. “We believe the Commons Clause license is restrictive and will discourage adoption. Our primary goal with Delta Lake is to drive adoption on-prem as well as in the cloud.”

Jun
28
2018
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Facebook is using machine learning to self-tune its myriad services

Regardless of what you may think of Facebook as a platform, they run a massive operation, and when you reach their level of scale you have to get more creative in how you handle every aspect of your computing environment.

Engineers quickly reach the limits of human ability to track information, to the point that checking logs and analytics becomes impractical and unwieldy on a system running thousands of services. This is a perfect scenario to implement machine learning, and that is precisely what Facebook has done.

The company published a blog post today about a self-tuning system they have dubbed Spiral. This is pretty nifty, and what it does is essentially flip the idea of system tuning on its head. Instead of looking at some data and coding what you want the system to do, you teach the system the right way to do it and it does it for you, using the massive stream of data to continually teach the machine learning models how to push the systems to be ever better.

In the blog post, the Spiral team described it this way: “Instead of looking at charts and logs produced by the system to verify correct and efficient operation, engineers now express what it means for a system to operate correctly and efficiently in code. Today, rather than specify how to compute correct responses to requests, our engineers encode the means of providing feedback to a self-tuning system.”

They say that coding in this way is akin to declarative code, like using SQL statements to tell the database what you want it to do with the data, but the act of applying that concept to systems is not a simple matter.

“Spiral uses machine learning to create data-driven and reactive heuristics for resource-constrained real-time services. The system allows for much faster development and hands-free maintenance of those services, compared with the hand-coded alternative,” the Spiral team wrote in the blog post.

If you consider the sheer number of services running on Facebook, and the number of users trying to interact with those services at any given time, it required sophisticated automation, and that is what Spiral is providing.

The system takes the log data and processes it through Spiral, which is connected with just a few lines of code. It then sends commands back to the server based on the declarative coding statements written by the team. To ensure those commands are always being fine-tuned, at the same time, the data gets sent from the server to a model for further adjustment in a lovely virtuous cycle. This process can be applied locally or globally.

The tool was developed by the team operating in Boston, and is only available internally inside Facebook. It took lots of engineering to make it happen, the kind of scope that only Facebook could apply to a problem like this (mostly because Facebook is one of the few companies that would actually have a problem like this).

Sep
16
2014
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3D Printing Company Stratasys Is Buying GrabCAD For Around $100M, Beating Out Autodesk, Adobe

Screen Shot 2014-09-16 at 13.23.08 Some M&A activity afoot in the world of hardware design: GrabCAD, an online community that has been described as the ‘Github for mechanical engineers’, is getting acquired by 3D printing giant Stratasys, a source tells us. The companies plan to announce the deal a little later today [Update: confirmed]. We have heard that the deal is in the region of nine figures, and… Read More

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