Summary

This chapter is an introduction to our journey. In the first two sections, we have described where DL sits within the wider picture of AI and how it continually shapes our daily lives. The key takeaways are the fact that DL is highly flexible due to its unique model architecture and the fact that DL has been actively adopted to the domain which traditional ML techniques have failed to demonstrate notable accomplishments.

Then, we have provided a high-level view of the DL project. In general, DL projects can be split into the following phases: project planning, building MVPs, building FFPs, development and maintenance, and project evaluation.

The main contents of this chapter covered the most important step of the DL project: project planning. In this phase, the purpose of the project needs to be clearly defined, along with the evaluation metrics, everyone must have a solid understanding of the stakeholders and their respective roles, and lastly, the tasks, milestones, and timeline need to be agreed upon by the participants. The outcome of this phase would be a well-formatted document called a playbook. In the next chapter, we will learn how to prepare data for DL projects.