Big companies often see innovations as a gold mine. Many entrepreneurs think, Have you heard about this new artificial intelligence start-up? Just imagine what we can achieve in this field. Our resources are vast compared to other companies. However, history dictates otherwise, since emerging innovative technologies are often born inside small start-ups rather than in big, stable businesses. This is counterintuitive. Large companies have more resources, people, time, and risk immunity, while start-ups have almost none. Then why do big corporations fail at innovation? The Innovator's Dilemma, by Clayton Christensen (https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244), and Crossing the Chasm, by Geoffrey Moore (https://www.amazon.com/Crossing-Chasm-3rd-Disruptive-Mainstream/dp/0062292986), tell a convincing...
Managing Data Science
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Managing Data Science
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Overview of this book
Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way.
After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps.
By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.
Table of Contents (18 chapters)
What You Can Do with Data Science
Testing Your Models
Understanding AI
Section 2: Building and Sustaining a Team
An Ideal Data Science Team
Conducting Data Science Interviews
Building Your Data Science Team
Section 3: Managing Various Data Science Projects
Managing Innovation
Managing Data Science Projects
Common Pitfalls of Data Science Projects
Creating Products and Improving Reusability
Section 4: Creating a Development Infrastructure
Implementing ModelOps
Building Your Technology Stack
Conclusion
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