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
You might also like
book
Advanced Analytics and Deep Learning Models
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts …
book
Data Mining and Machine Learning Applications
DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of …
book
Automated Deep Learning Using Neural Network Intelligence: Develop and Design PyTorch and TensorFlow Models Using Python
Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural …
book
Practical Machine Learning with H2O
Machine learning has finally come of age. With H2O software, you can perform machine learning and …
