Understanding the role of DL in our daily lives

By exploiting the flexibility of DL, researchers have made remarkable progress in the domains in which traditional ML techniques have shown limited performance (see Figure 1.3). The first flag has been planted in the field of computer vision (CV) for digit recognition and object detection tasks. Then, DL has been adopted for natural language processing (NLP), showing meaningful progress in translation and speech recognition tasks. It also demonstrates its effectiveness in reinforcement learning (RL) as well as generative modeling.

The list of papers linked in the Further reading section in this chapter summarizes popular use cases of DL.

Following diagram shows various applications of DL:

Figure 1.3 – Applications of DL

However, integrating DL into an existing technology infrastructure is not an easy task; difficulties can arise from various aspects, including but not limited to budget, time, as well as talent. Therefore, a thorough understanding of DL has become an essential skill for those who manage DL projects: project managers, technology leads, as well as C-suite executives. Furthermore, the knowledge in this fast-growing field will allow them to find future opportunities in their specific verticals and across the organization, as people working on AI projects actively gather new knowledge to derive innovative and competitive advantages. Overall, an in-depth understanding of DL pipelines and developing production-ready outputs allows managers to execute projects better by effectively avoiding commonly known pitfalls.

Unfortunately, DL projects are not yet in a plug-and-play state. In many cases, they involve extensive research and adjustment phases, which can quickly drain the available resources. Above all, we have noticed that many companies struggle to move from proof of concept to production because critical decisions are made by the few who only have a limited understanding of the project requirements and DL pipelines. With that being said, our book aims to provide a complete picture of a DL project; we will start with project planning, and then discuss how to develop MVPs and FFPs, how to utilize cloud services to scale up, and finally, how to deliver the product to targeted users.

Things to remember

a. DL has been applied to many problems in various fields, including but not limited to CV, NLP, RL, and generative modeling.

b. An in-depth understanding of DL is crucial for those leading DL projects, regardless of their position or background.

c. This book provides a complete picture of a DL project by describing how DL projects are carried out from project planning to deployment.

Next, we will take a closer look at how DL projects are structured.