• The problem can be internal (such as with improving operations) or external (helping to provide better customer experiences). This chapter will look at cases where companies have been successful with this.

Chapter 3: Data Preparation

• This often does not get enough attention. But data preparation is absolutely essential and full of mine fields. There will be a look at how to identify/clean the data, such a with various tools and techniques. This chapter will also describe strategies for data ethics, governance, provenance and compliance.

Chapter 4: Building the AI Team

• This shows what skillsets are required and how to recruit the right people. There will also be a look at setting up the right incentives, roles and duties.

Chapter 5: Creating the Model

• This chapter will focus on what algorithms to use, how to select the parameters and how to test/train the models. There will also be coverage on the various types of tools to select and when to create in-house ones.

Chapter 6: Deploy The Model

• Here there is a look at strategies for having limited releases and rollouts. There will also be a look at different approaches for the design of the UI so as to get better adoption.

Chapter 7: Monitoring

• This chapter will show how to keep track of the model and know when to make changes/upgrades.

Chapter 8: Scaling AI

• This has proven to be extremely difficult for organizations. So in this chapter, there will be a look at strategies to show how AI can move the needle.

Chapter 9: The Future

• Again, there needs to be a different mindset. Thus, for a successful AI implementation, it’s important to look at change management strategies.

Chapter 10: The Future

• This will be a recap of the main takeaways of the book and also a look at major trends with AI.

Appendix A: Resources like blogs, videos and websites

Appendix B: AI Tools (TensorFlow, DataRobot, Microsoft AI Builder, etc)

Appendix C: AI Glossary

Tom Taulli has been developing software since the 1980s. In college, he started his first company, which focused on the development of e-learning systems. He created other companies as well, including Hypermart.net that was sold to InfoSpace in 1996. Along the way, Tom has written columns for online publications such as BusinessWeek.com, TechWeb.com, and Bloomberg.com. He also writes posts on Artificial Intelligence for Forbes.com and is the adviser to various companies in the space. You can reach Tom on Twitter (@ttaulli) or through his website (Taulli.com) where he has an online course on AI.

AI is one of the fastest growing corners of the tech world. But there remains one big problem: many AI projects fail. The fact is that AI is unique among IT projects. The technology requires a different mindset, in terms of understanding probabilities, data structures and complex algorithms. There is also a need to deal with complex issues like ethics and privacy.

This is where Implementing AI Systems comes in. You'll learn the step-by-step process for successful implementations of AI, backed up with numerous case studies from top companies. This book puts everything you need to know into one place – that is, it’s the handbook you need for AI. You’ll focus primarily on understanding the core concepts for AI like NLP, Machine Learning, Deep Learning and so on.

This book will help you find the right areas to apply AI.

You will:

Put together an effective data strategy
Create models and how to successfully test them
Evaluate AI tools
Assemble the right team
Scale AI across an organization

Taulli, Tom Tom Taulli has his own firm, Taulli Research, whic... więcej >

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