Machine Learning Ecosystem 101

With global spending on AI and machine learning forecast to eclipse $85 billion this year and $204 billion in 2025, enterprises are investing in ML infrastructure with urgency as AI becomes more critical to their organizations. Unfortunately, the burgeoning ML infrastructure market can be confusing, crowded and complex — a dizzying array of platforms and tools that even sophisticated ML teams struggle to keep straight. 

In this comprehensive ebook, Arize AI breaks down the model building workflow to provide a comprehensive crash course on the major categories of solutions and why a team might need each. Designed to be useful to both business readers and dev teams broadly, this ebook details the goals and challenges in each stage of the machine learning workflow and the array of companies vying to help across:

  • Data Preparation 
  • Model Building & Development Tools 
  • Model Validation
  • Model Serving
  • Observability

Read the whitepaper

About the authors

Aparna Dhinakaran
Co-founder & Chief Product Officer

Aparna Dhinakaran is Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and Tubemogul (acquired by Adobe). During her time at Uber, she built a number of core ML Infrastructure platforms including Michaelangelo. She has a bachelor's from Berkeley's Electrical Engineering and Computer Science program where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

Jason Lopatecki
Arize Founder

A respected executive and software engineer with a history of building and scaling data-centric businesses and products, Jason was most recently was co-founder and chief innovation officer at TubeMogul where he saw the company through its successful IPO and acquisition by Adobe.

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