With the overall resurgence of AI and the new generative AI capabilities that we have seen coming out primarily of OpenAI and their ChatGPT product, we thought providing a refresher on how we view the AI (and data) talent stack could be useful.
AI, specifically machine learning (ML), are sophisticated statistical techniques that help with dynamic decision-making around optimizations, recommendations, forecasts, etc. Different from ML, basic statistical techniques that can be explained by a human are sometimes enough to implement in processes or software products (e.g., linear regressions). In other instances, more complex, black-box techniques merit inclusion due to the nature or volume of the data or time sensitivity for decision-making.
In summary, machine learning algorithms (bundled inside the concept of artificial intelligence/AI) are trained with data. The data component is key here. For ML algorithms to work as trained, they need troves of data, which connects the AI stack with the data stack.
A company needs to first source data, cleanse it/transform it, load it, etc., to analyze it. These tasks are typically performed by engineering roles: typically a Data Engineer and, for more senior roles, a Data Architect.
Once data is accessible in a data warehouse or data lake, Data Analysts or Data Scientists can start analyzing it, coming up with insights, and trying to find patterns. These professionals will use various techniques to help answer the company's business questions.
As the team defines suitable models capable of learning and making predictions, they must operationalize these outside of R&D and into production. That’s when Machine Learning Engineers start participating in the project by helping create the needed infrastructure to perform as the business expects. Machine Learning (Deep Learning) Engineers could also be considered senior Data Scientists with strong software development and engineering training/skills.
In many cases, there are no strong boundaries among these roles, but a data team with different professionals from diverse backgrounds collaborating with overlapped responsibilities looks to achieve the organization’s business goals.
At Alten Capital we enjoy the AI and data space. Please reach out to explore how we can collaborate.