Succeeding with AI
- Paperback: 288 pages
- Publisher: WOW! eBook; 1st edition (March 31, 2020)
- Language: English
- ISBN-10: 1617296937
- ISBN-13: 978-1617296932
Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Succeeding with AI sets out a blueprint for Artificial Intelligence (AI) projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals.
The big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. Artificial Intelligence (AI) systems with great funding and top talent will still fail if they aren’t answering questions that will drive real business value. As the leader of an AI team, it’s your job to make sure you’re directing your team toward the right goals and implementing a process that will deliver results on time and on budget.
- Selecting the right AI project to meet specific business goals
- Economizing resources to deliver the best value for money
- How to measure the success of your AI efforts in the business terms
- Predict if you are you on the right track to deliver your intended business results
In Succeeding with AI, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage startups, and other businesses across multiple industries. Veljko first lays out a framework for determining the right questions to answer for your business. Then, he teaches you a repeatable process for properly organizing an AI project to maximize the value of limited sources, such as the time of your data scientists. You’ll learn to establish metrics that let you judge the effectiveness of your machine learning against business needs and how to assess whether your AI project is on the right track early on in its lifecycle. With exercises based on the kind of business dilemmas you’ll encounter in the real world, you’ll learn how to manage an ML pipeline and keep it from change-resistant calcification. When you’re done, you’ll be ready to start investing wisely in data science to deliver concrete, reliable, and profitable results for your business.