The AI Team

This part of section 4 will lead you through the process of recruiting a team with the right set of skills and experience needed to implement an AI project. There are three main categories of skills you will need in an AI team:

• Software engineering—Extracts data from various sources, integrate third-party components and AI, and manages the infrastructure
• Machine learning and data science—Choosing the right algorithm for a specific problem, tuning it, and evaluating the accuracy
• Advanced mathematics—Developing cutting-edge deep learning algorithms

You may be tempted to find a person who has all these skills. This person does not exist! However, you can find different people that excel in one or more of these tasks. Also, depending on the AI model you are trying to build, you may or may not need some of these skills.

Figure 4.5 helps you visualize how each of these roles has a different composition of the three core AI skills: software engineering (SW), machine learning/data science (ML), and advanced math.

Figure 4.5 | The typical software engineer, ML engineer, and researcher have different levels of skills across software engineering (SW), machine learning (ML), and math.
Attribution: Zero to AI, Figure 8.8. Nicolò Valigi and Gianluca Mauro. Link to source. All rights reserved.

To elaborate more on how each of these figures fits in the data/model/infrastructure breakdown of AI projects, Software engineers mostly work on the infrastructure side. Setting up the system that fetches data and integrates the ML into the existing product or service.

A machine learning engineer has specialized knowledge of AI and machine learning. Their job is to analyze the data and choose the most appropriate model for your project. They also write the code for your model and evaluate the performance. They will guide your efforts in collecting more and better data.

Suppose you are doing a data science project. In that case, you may have thought a data scientist is a jack-of-all-trades – someone who can solve any problem involving data, ranging from simple analysis to the design of complex ML models. On top of that, we often want the ideal data scientist to show business acumen too. The expectations of what a data scientist can do are so high in theory that they are often unmet in practice. As we discussed in the course, their job is to analyze the data and give you insight into what information you can get out of the raw data you have. The management can use the information to turn into insight and be utilized to refine the business.

To determine if you should opt in for a data science project versus an AI project, think of the following prompts as it applies to your organization:

• You are not yet sure about the direction that your organization will take. Having someone with a broad skill set can help you be flexible until you have a clearer direction.
• You do not have extremely complex needs on the technical side. If all you need is someone who can analyze medium-sized datasets (up to a few gigabytes) to give insight to the management to make business decisions and build some ML models, people who call themselves data scientists are usually a good fit.
• You are looking to refine the current processes in your organization, so you need some insight and not thinking of using AI within your processes yet.

Finally, the researcher is the most academic role of the three. They can use their in-depth research experience in a specific niche to find solutions to novel problems that are not well explored in the industry yet. Of the three components of the project, the researcher sits firmly in the model camp, where they can use their knowledge of state of the art to build AI algorithms that push forward the frontiers of what is possible. For your initial projects, you might not need someone to implement state of the art methods. You may only want to implement more basic models just to gain experience. Therefore, you may not need a researcher at the start.

Remember, you can always outsource these teams to outside resources. We talked about it in section 2, you can always partner with academic institutions to leverage the skills of students training in these areas of expertise. In this section, we explained the workflow of defining an AI problem and introduced the AI team. Next, we elaborate on the differences between the workflow of a machine learning and a data science project.