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 the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. 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

Jason Lopatecki is co-founder and CEO of Arize AI, a machine learning observability company. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Jason was co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company and eventual acquisition by Adobe. Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Jason tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley - Go Bears!

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