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Why is Machine Learning Hard?

Posted Apr 12
# Data engineering
# Organization and Processes
# Production Use Case
# Systems and Architecture
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SPEAKER
Tal Shaked
Tal Shaked
Tal Shaked
ML Architect @ Snowflake

Tal Shaked is SnowflakeÕs Machine Learning (ML) Architect. Prior to Snowflake, Tal spent 16 years at Google, culminating in the role of Distinguished Engineer / Senior Director. He was responsible for a broad set of ML projects such as TensorFlow Extended and Sibyl Ñ two of the most widely deployed ML platforms at Google, and specific applications of ML for Google Ads and Google Search. Tal completed his BS in Computer Science at the University of Arizona and his MS in Computer Science at the University of Washington. Tal is also a chess grandmaster and won the World Junior Chess Championship in 1997.

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Tal Shaked is SnowflakeÕs Machine Learning (ML) Architect. Prior to Snowflake, Tal spent 16 years at Google, culminating in the role of Distinguished Engineer / Senior Director. He was responsible for a broad set of ML projects such as TensorFlow Extended and Sibyl Ñ two of the most widely deployed ML platforms at Google, and specific applications of ML for Google Ads and Google Search. Tal completed his BS in Computer Science at the University of Arizona and his MS in Computer Science at the University of Washington. Tal is also a chess grandmaster and won the World Junior Chess Championship in 1997.

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SUMMARY

Each of us has a different answer for Òwhy is machine learning so hard.Ó And how long you have been working on ML will drastically influence your answer.

I'll share what I learned over the past 20 years, implementing everything from scratch for 1 model in web search ranking, 100s of models for Sybil and 1000s of models for TFX. YouÕll see why I'm convinced that data and software engineering are critical for successful data science - more so than models. Regardless of your experience, IÕll share some tips that will help you overcome the hard parts of machine learning.

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Posted Apr 12 | Views 22
# Data engineering
# Explainability and Observability
# Feature Stores
# Research
# Systems and Architecture