Book description
Enterprises in traditional and emerging industries alike are increasingly turning to machine learning (ML) to maximize the value of their business data. But many of these teams are likely to experience significant hurdles and setbacks throughout the journey. In this practical ebook, data scientists and machine learning engineers explore six common challenges that teams face every day when creating, managing, and scaling ML applications.
For each problem, you’ll get hard-earned advice from Hussein Mehanna, AI engineering director for Google Cloud; Nakul Arora, VP of product management and marketing at Infosys; Patrick Hall, senior director for data science products at H2O; Matt Harrison, consultant and corporate trainer at MetaSnake; Joao Natali, data science director at Neustar; and Jerry Overton, data scientist and technology fellow at DXC.
Accomplished data scientist Piero Cinquegrana and Matheen Raza of Qubole examine ways to overcome challenges that include:
- Reconciling disparate interfaces
- Resolving environment dependencies
- Ensuring close collaboration among all ML stakeholders
- Building or renting adequate ML infrastructure
- Meeting the scalability needs of your application
- Enabling smooth deployment of ML projects
Table of contents
-
Machine Learning at Enterprise Scale
- Introduction
- Problem 1: Reconciling Disparate Interfaces
- Problem 2: Resolving Environment Dependencies
- Problem 3: Ensuring Close Collaboration Among All Machine Learning Stakeholders
- Problem 4: Building (or Renting) Adequate Machine Learning Infrastructure
- Problem 5: Scaling to Meet Machine Learning Requirements
- Problem 6: Enabling Smooth Deployment of Machine Learning Projects
- Conclusion
Product information
- Title: Machine Learning at Enterprise Scale
- Author(s):
- Release date: July 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492050803
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