Machine Learning Project Life Cycle and Challenges
Today, machine learning (ML) and artificial intelligence (AI) are integral parts of business strategy for many organizations, and more organizations are using them every year. The major reason for this adoption is the power of ML and AI solutions to garner more revenue, brand value, and cost savings. This increase in the adoption of AI and ML demands more skilled data and ML specialists and technical leaders. If you are an ML practitioner or beginner, this book will help you become a confident ML engineer or data scientist with knowledge of Google’s best practices. In this chapter, we will discuss the basics of the life cycle and the challenges and limitations of ML when developing real-world applications.
ML projects often involve a defined set of steps from problem statements to deployments. It is essential to understand the importance and common challenges involved with these steps to complete a successful and impactful project. In this chapter, we will discuss the importance of understanding the business problem, the common steps involved in a typical ML project life cycle, and the challenges and limitations of ML in detail. This will help new ML practitioners understand the basic project flow; plus, it will help create a foundation for forthcoming chapters in this book.
This chapter covers the following topics:
- ML project life cycle
- Common challenges in developing real-world ML solutions
- Limitations of ML
