Data scientists and machine learning professionals face a quandary of choices when trying to figure out how to scale their data science experiments, from deciding what tools to use (Keras, TensorFlow, scikit-learn, PyTorch, etc.) and the best ways to share models with customers and partners to managing cost efficiently. Arshak Navruzyan details the landscape of available options and explains how to make best use of the free and open source tools available.
Arshak Navruzyan is chief technology officer at Sentient, where he is responsible for leading the engineering direction and vision for Sentient’s core distributed artificial intelligence (DAI) platform and leads the data science team in support of Sentient’s intelligent commerce offerings and trading for Sentient Investment Management. Arshak has delivered AI solutions for multibillion dollar quantitative hedge funds, venture-funded startups, and some of the largest telecoms in the world. Previously, he held technology leadership roles at Argyle Data, Alpine Data Labs, and Endeca/Oracle. He’s also the founder of Fellowship.AI, a machine learning fellowship program.
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