What Skills Do You Need on an AI Project?
The Basics If you are starting an Artificial Intelligence (AI) initiative, one of the first things you need to do is to identify the skills you need to be successful. Create a common understanding of what AI will mean for you with this 140 character explanation: “Simply put, artificial intelligence covers a broad range of evolving technologies that combine stats, data, and modern computing power to transform human potential.” You […]
If you are starting an Artificial Intelligence (AI) initiative, one of the first things you need to do is to identify the skills you need to be successful. Create a common understanding of what AI will mean for you with this 140 character explanation:
“Simply put, artificial intelligence covers a broad range of evolving technologies that combine stats, data, and modern computing power to transform human potential.”
You can augment that with some possible uses of AI and a few myths about it. Once you know what AI means for you, you can identify the skills you need.
Start by defining the business problem you want to solve and whether machine learning algorithms are the best way to solve it. Your team needs discovery skills – to understand the end-users, refine the business problem and determine the desired outcome. They’ll also have to conduct exploratory data analysis (EDA). Once the team goes through a process of discovery, they can define a minimal viable product and aim for delivery in a short time frame. One to two months is a good target depending on the data available, the quality of that data and how well it aligns with the business problem.
An example “dream team” for this type of work would include:
- Machine Learning Engineers or Data Scientists to build the necessary algorithms. Have at least two on your team, so they can collaborate and check each other’s work. They should have a deep understanding of the mathematical concepts behind machine learning along with programming skills.
- Data Integration Engineers with expertise in data pipelines and ETL processes. Since data is always messier than anticipated, you’ll need at least two. Excella’s Data Engineers bring expertise in DataOps to build reliable, resilient, secure data pipelines that automate the processes of bringing data into our AI solutions.
- Software Developers and DevOps Engineers who can help choose the right technology platform, develop APIs, and integrate the models into scalable, secure applications. Ideally, you’ll want a couple of people who can do this work. They should implement best practices like test-driven development, performance testing and built-in resiliency.
- Analysts, user experience designers and/or data visualization developers who are experts at delivering solutions that create value. AI solutions are usually intended to augment human decision-making so you’ll need experts who can understand the users’ needs, analyze the business problem and present results in accessible ways.
- ScrumMaster, Service Delivery Manager (for Kanban teams) or an Agile Coach will ensure the team comes together and continually improve with iterative, incremental deliveries and fast feedback loops.
This dream team will bring all the necessary skills and expertise to deliver high quality, scalable AI solutions that rapidly deliver value.
Needs and Skills Vary
Of course, no two projects are exactly the same and your needs and specific team composition will vary depending on your context. Expect those needs to evolve over time, and your ideal team composition will change as a result. The “dream team” outlined above has been a successful starting point for Excella in a variety of engagements with many important clients. It should give you a good idea of where to start.
Are you ready to build your dream team? We can help!
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