3 years ago

ML and deep learning used to be stuck in prototyping phase as technology was evolving and AI talent was scarce. As AI programs in universities around the world are becoming main attraction for students, ML engineers are becoming part of every tech company personnel and software tools for AI are becoming strategic products of cloud giants we are seeing transition where ML and deep learning is becoming standard part of products and internal tools that can provide competitive edge for companies in many industries.

NVIDIA is famous for developing CUDA which enables software to communicate with GPU and CUDnn which extends CUDA in order to run deep learning.

This is creating similar effect as cloud: startups are building product rapidly since the didn’t have to spend time with training and researching NLP models just as cloud based company outsources its infrastructure work to cloud company which is premise on which AWS was build(access to world class infrastructure through every laptop instead of hiring lots of engineers and waiting for them to set everything up).

In my opinion companies sometimes require extremely low latency and some other specifics which requires custom platform, but as with some big data platforms it’s hard to satisfy all demand with managed services(SaaS) with SaaS still being used for fast iteration and quick MVP which is important for startups in AI.