Microsoft acquires Lobe, a drag-and-drop AI tool
Microsoft today announced that is has acquired Lobe, 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 beta earlier 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 co-founded by Mike Matas, who previously worked on the iPhone
and iPad, as well as Facebook’s Paper and Instant Articles products. The
other co-founders are Adam Menges and Markus Beissinger.
In addition to Lobe, Microsoft also recently bought Bonsai.ai, a deep
reinforcement learning platform, and Semantic Machines, a conversational
AI platform. Last year, it acquired Disrupt Battlefield participant
Maluuba. It’s no secret that machine learning talent is hard to come by,
so it’s no surprise that all of the major tech firms are acquiring as
much talent and technology as they can.
“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’s worth noting that Lobe’s approach complements Microsoft’s existing
Azure ML Studio platform, which also offers a drag-and-drop interface
for building machine learning models, though with a more utilitarian
design than the slick interface that the Lobe team built. Both Lobe and
Azure ML Studio aim to make machine learning easy to use for anybody,
without having to know the ins and outs of TensorFlow, Keras or PyTorch.
Those approaches always come with some limitations, but just like
low-code tools, they do serve a purpose and work well enough for many
use cases.
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