Overview of DL projects
While DL and other software engineering projects have a lot in common, DL projects emphasize planning, due to the extensive need for resources, mainly coming from the complexity of the models and the high volume of data involved. In general, DL projects can be split into the following phases:
- Project planning
- Building MVPs
- Building FFPs
- Deployment and maintenance
- Project evaluation
In this section, we provide high-level overviews of these phases. The following sections cover each phase in detail.
Project planning
As the first step, the project lead must clearly define what needs to be achieved by the project and understand groups that can affect or be affected by the project. The evaluation metrics need to be defined and agreed upon, as they will be revisited during project evaluation. Then, the team members group together to discuss individual responsibilities and achieve business objectives using available resources. This process naturally leads to a timeline, an estimate of how long the project would take. Overall, project planning should result in the generation of a document called a playbook, which includes a thorough description of how the project will be carried out and evaluated.
Building minimum viable products
Once the direction is clear for everyone, the next step is to build an MVP, a simplistic version of the target deliverable that showcases the project’s value. Another important aspect of this phase is to understand the project’s difficulties and reject paths with greater risks or less promising outcomes by following the fail fast, fail often philosophy. Therefore, data scientists and engineers typically work with partially sampled datasets in development settings and ignore insignificant optimizations.
Building fully featured products
Once the feasibility of the project has been confirmed by the MVP, it must be packaged into an FFP. This phase aims to polish up the MVP to build a production-ready deliverable with various optimizations. In the case of DL projects, additional data preparation techniques are introduced to improve the quality of input data, or the model pipeline gets augmented slightly for greater model performance. Additionally, the data preparation pipeline and model training pipeline may be migrated to the cloud, exploiting various web services for higher throughput and quality. In this case, the whole pipeline often gets reimplemented using different tools and services. This book focuses on Amazon Web Services (AWS), the most popular web service for handling high volumes of data and expensive computations.
Deployment and maintenance
In many cases, the deployment settings are different from the development settings. Therefore, different sets of tools are often involved when moving an FFP to production. Furthermore, deployment may introduce problems that weren’t visible during development, which mainly arise as a result of limited computational resources. Consequently, many engineers and scientists spend additional time improving the user experience during this phase. Most people believe that deployment is the last step. However, there is one more step: maintenance. The quality of data and model performance needs to be monitored consistently to provide stable services to targeted users.
Project evaluation
In the last phase, project evaluation, the team should revisit the discussions made during project planning to evaluate whether the project has been carried out successfully or not. Furthermore, the details of the project need to be recorded, and potential improvements must be discussed so that the next projects can be achieved more efficiently.
Things to remember
a. The phases within DL projects are split into project planning, building MVPs, building FFPs, deployment and maintenance, and project evaluation.
b. During the project planning phase, the project goal and evaluation metrics are defined, and the team discusses an individual's responsibility, available resources, and the timeline for the project.
c. The purpose of building an MVP is to understand the difficulties of the project and reject paths that pose greater risks or offer less promising outcomes.
d. The FFP is a production-ready deliverable that is an optimized version of the MVP. The data preparation pipeline and model training pipeline may be migrated to the cloud, exploiting various web services for higher throughput and quality.
e. Deployment settings often provide limited computational resources. In this case, the system needs to be tuned to provide stable services to target users.
f. Upon the completion of the project, the team needs to revisit the timeline, assigned responsibilities, and business requirements to evaluate the success of the project.
In the following section, we will walk you through how to plan a DL project properly.
