New Tasks as a data science
As a Data Science Consultant with AWS, I am used to helping customers solve problems using machine learning. So when we started our discussion with Haufe about the AI Foundations (AIF) Program, I was immediately intrigued. On the one hand, I was excited by the motivation and technical competence of the teams at Haufe. But on the other, I wasn’t used to transferring knowledge in a classroom setting. I’m used to designing algorithms not curriculums. Preparing lectures and exercises is certainly work that I’ve never done before. Furthermore, I knew that anything we delivered needed to meet Haufe’s high bar of technical excellence.
Preparing to teach in general
In preparing AI Foundations, we at AWS began by looking at our past projects to extract those patterns, best practices, and approaches that we’ve found particularly useful elsewhere. We knew that for a curriculum to be meaningful it would need to contain insights that were directly applicable to real world problems. Our second consideration was how best to make the material relevant for Haufe. We decided that by tying the program to the business challenges that Haufe was already facing we could accomplish two goals simultaneously. First, we could transfer knowledge of general ML concepts through real-world use-cases and, second, we could create a staging ground for usable future solutions.
Preparing to teach for Haufe - co-creation
However, getting to this point took a considerable amount of planning and collaboration. The AWS and Haufe teams started by scheduling a series of deep dives and follow ups with a variety of different business leaders across the company. Through this collaboration, we were able to pinpoint exactly what initiatives were most relevant and would provide the most value. The Haufe team was instrumental in defining the scope and direction of these focuses. In the end, AIF was a co-creation. The main strength of that co-creation was the varied and thoughtful perspectives of each person on the team.
Training done right produces successful results
From my perspective, AI Foundations was successful in many ways: it allowed engineers to branch out into a new area of technology, it fostered better communication around ML’s role in products, and it jump-started several ML initiatives across Haufe. However, in retrospect, the piece of AI Foundations that meant the most to me was the chance to collaborate with Haufe’s engineers to solve problems ranging from recommendation engines to ML driven search.
The topic of machine learning was a good selection and made a lot of sense. On the one hand ML provides us a completely new way to create products and features by leveraging ever-more-powerful, constantly-developing technology, on the other, ML has the capacity to free up resources, to streamline work and to enable engineering data-centric solutions for people, who don’t have specific domain knowledge in math and statistics. Maybe this is why machine learning has been added as a major building block to our technology strategy and why mastering machine learning is seen as a competitive must by top management.
The win-win of AI Foundations
Not only was this collaboration personally exciting, it has benefits for us at AWS as well. At AWS, we are customer obsessed. We are always trying to find ways to help customers be more effective and solve problems more easily. All our products and services are directly driven by customer feedback. Being able to understand and dive deep into the business problems at Haufe provided insight that we can use to more productively evolve the direction of these offerings.
The teachable moment - AI is accessible to many
There are many ML practitioners that like to think of machine learning as a gated community inaccessible to those that have not undergone years of rigorous training. If there is one thing that I took away from the AI Foundations initiative, it’s that this opinion couldn’t be farther from the truth. Machine learning is a tool available to anyone with the right motivation and dedication to learn to use it. This is a proposition that Haufe is proving every day as they move forward in their ML journey.
Will Mcgehee is a data scientist at AWS
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