Largest product based IT company Microsoft today in a news revealed that they have acquired Lobe, which is a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into a beta version as of now in the beginning of this year to build upon its own efforts to make building AI models easier, though, for the time being, Lobe will operate as before.
“As part of Microsoft, Lobe will be able to leverage world-class AI research, global infrastructure, and decades of experience building developer tools,” the team writes. “We plan to continue developing Lobe as a standalone service, supporting open source standards and multiple platforms.”
Lobe was a startup which was cofounded by the Mike Matas, who previously worked on the iPhone and iPad, as well as Facebook’s Paper and Instant Articles products. Apart from that there are other two cofounders as well that is Adam Menges and Markus Beissinger.
Apart from just Lobe, the company has also acquired Bonsai.ai, which is a deep reinforcement learning platform, and Semantic Machines, which is a conversational AI platform. In the year 2017, it acquired Disrupt Battlefield participant Maluuba.
“In many ways though, we’re only just beginning to tap into the full potential AI can provide,” Microsoft’s EVP and CTO Kevin Scott writes in today’s announcement. “This in large part is because AI development and building deep learning models are slow and complex processes even for experienced data scientists and developers. To date, many people have been at a disadvantage when it comes to accessing AI, and we’re committed to changing that.”
It is also worth seeing that the Lobe’s approach complements Microsoft’s existing Azure ML Studio platform, which also offers a drag-and-drop interface for building various model of the machine learning with the help of which with a more utilitarian design than the slick interface that the Lobe team built., but just like low-code tools, they do serve a purpose and work well enough for many use cases.
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