AI News, Deepak Agarwal artificial intelligence

My Top 8 Moments at TWIMLcon 2019

While building ML platforms requires data scientists to partner with platform engineers and SREs, building machine learning products involves incorporating additional expertise from UX, Product, and Design.

In order to attract and keep high quality UX designers on ML projects, says Hussein Mehanna, you need to treat them as first class citizens along with your data scientists and machine learning engineers.

In their discussion on human-in-the-loop machine learning, Robert Munro and Radha Basu discussed best practices for ensuring that data labelers are treated fairly and humanely, regardless of whether they’re internal employees or externally sourced.

When deciding whether or not to build a platform at Uber, Franziska said she considers three questions: 1) Will the investment in the platform lead to a step function improvement in the modeling?

Ameen Kazerouni, the Director of Data Science at Zappos described how his team uses tools like protocol buffers and avro/parquet files to establish contract first protocols and promote cross-language data exchanges.

Rather than constrain data scientists to develop in a single language, which tends to reduce operational concerns, platform engineers are building solutions that rely on efficient data transfer and containerization to enhance flexibility.

In his opening statement, Sam Charrington declared that in the ML community, Kubernetes has gone from a bleeding edge idea to "almost passè", and that TWIMLcon had recieved a large number of proposals from individuals seeking to speak about their experience with the container orchestration platform.

Judging by the number of photos taken by audience members, the private messages I received in the conference app, and the number of people who came up to me afterwards to ask questions, I’d say that the talk was extremely well received ;

Past Presenters

We are so thankful to the amazing speakers that participate in our events.

Deepak Agarwal

Deepak Agarwal is a vice president of engineering at LinkedIn where he is responsible for all AI and machine learning efforts across the company.

He has published extensively in top-tier computer science conferences, co-authored several patents, and has served on the committees of various conferences in the field of AI and computer science including the Executive Committee of Knowledge Discovery and Data Mining (KDD).

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