0%

Book Description

As more companies work to adopt AI for business processes, project costs and failure rates are on the rise. Why? No standard practice exists for implementing AI in business applications, and many organizations don’t have the skills, processes, and tools to mitigate risk. With this practical report, industry experts Carlo Appugliese, Paco Nathan, and William S. Roberts teach you Agile AI to help you innovate, reduce required investments, and decrease failure risk.

Written for technical leaders as well as tech-savvy business cohorts with an understanding of analytics, software engineering, and data science, this report from IBM is useful for anyone interested in an Agile approach to AI and machine learning at the enterprise level. You’ll quickly learn how to choose the approach that works best for your company.

  • Fundamentals: Explore data science and AI tools, including the trends in and risks of machine learning
  • AI skills: Examine core skills in data science, as well as effective practices for building a data science team and nurturing a supportive culture
  • Agile approach: Focus on the right team mind-set of flexibility, the right set of tools, and the right set of team skills

Table of Contents

  1. 1. Introduction: Agile AI Processes and Outcomes
    1. The Agile Approach
    2. AI Processes in Businesses Today
    3. Beyond R&D
    4. Organizing for AI
      1. Data Scientists
      2. Data Engineers
      3. Business Analysts
    5. Why Agile for AI?
    6. Summary
  2. 2. Understanding AI Tools
    1. Contrasting Machine Learning and AI
    2. The Role of Open Source in Innovation
    3. Tooling
    4. The Fundamentals of Machine Learning Projects
      1. Supervised Learning
      2. Unsupervised Learning
      3. Deep Learning
    5. The Machine Learning Life Cycle
      1. Training
      2. Deployment
      3. Running
    6. Distributed Workloads and Hybrid Environments
    7. Summary
  3. 3. AI Skills
    1. Understanding the Skills and Culture
    2. Team Skills Assessment
    3. Core Skills
      1. Data Preparation
      2. Coding Chops
      3. Exploring and Visualizing Data
      4. Platform Engineering
      5. Feature Engineering
      6. Modeling
      7. Model Integration and Deployment
      8. Troubleshooting in Production
    4. Building Teams
      1. Team Culture
      2. Finding the Best People
  4. 4. Summary
    1. Use Cases
    2. Data
    3. Tools and Process
    4. Mindset
    5. Integration and Trust
    6. Conclusion
18.117.186.92