Effective Machine Learning Teams: Best Practices for ML Practitioners - Image 1

£39.59

O'Reilly Effective Machine Learning Teams: Best Practices for ML Practitioners

Name: Effective Machine Learning Teams: Best Practices for ML Practitioners
Brand: O'Reilly
Price: 39.59 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 2 days • 2 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 2 days • 1 price levels

Days at Price

Price Analysis

Most common price: £40 (2 days, 100.0%)

Price range: £40 - £40

Price levels: 1 different prices over 2 days

Description

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.

Key Features

New Store Stock

Product Specifications

Brand: O'Reilly
Format: paperback
ASIN: 1098144635
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 31 March 2024
Listed Since: 03 August 2023

Barcode

No barcode data available

O'Reilly Effective Machine Learning Teams: Best Practices for ML Practitioners

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Reliable Machine Learning: Applying SRE Principles to ML in Production

Practical MLOps: Operationalizing Machine Learning Models

Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines

O'Reilly Building Machine Learning Powered Applications Book

Implementing MLOps in the Enterprise: A Production-First Approach

Software Engineering for Data Scientists: From Notebooks to Scalable Systems

Lean Software Systems Engineering for Developers: Managing Requirements, Complexity, Teams, and Change Like a Champ

Introducing MLOps: How to Scale Machine Learning in the Enterprise

MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

Machine Learning Engineering in Action

Introduction to Machine Learning with Python: A Guide for Data Scientists

The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Machine Learning in Production: From Models to Products

Machine Learning for Oracle Database Professionals: Deploying Model-Driven Applications and Automation Pipelines

Building an Effective Data Science Practice: A Framework to Bootstrap and Manage a Successful Data Science Practice

Machine Learning Interviews: Kickstart Your Machine Learning Career

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

Thinking Data Science: A Data Science Practitioner’s Guide (The Springer Series in Applied Machine Learning)

Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions

Deep Learning at Scale: At the Intersection of Hardware, Software, and Data

Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

Low-Code AI: A Practical Project-Driven Introduction to Machine Learning

Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow

Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems