Capitalizing on machine learning with collaborative, structured enterprise tooling teams


Having centralized enterprise MLOps and engineering teams ask these questions can free up the business to solve customer problems, and to consider how technology can continue to support the evolution of new solutions and experiences.

Don’t simply hire unicorns, build them

There’s no question that delivering for the needs of business partners in the modern enterprise takes significant amounts of MLOps expertise. It requires both software engineering and ML engineering experience, and—especially as AI/ML capabilities evolve—people with deeply specialized skill sets, such as those with deep graphics processing (GPU) expertise.

Instead of hiring a “unicorn” individual, companies should focus on building a unicorn team with the best of both worlds. This means having deep subject matter experts in science, engineering, statistics, product management, DevOps, and other disciplines. These are all complementary skill sets that add up to a more powerful collective. Together, individuals who can work effectively as a team, show a curiosity for learning, and an ability to empathize with the problems you’re solving are just as important as their unique domain skills.

Develop a product mindset to produce better tools

Last but not least, it’s important to take a product-backed mindset when building new AI and ML tools for internal customers and business partners. It requires not just thinking about what you build as just a task or project to be checked off the list, but understanding the customer you’re building for and taking a holistic approach that works back from their needs.

Often, the products MLOps teams build—whether it’s a new feature library or an explainability tool—look different than what traditional product managers deliver, but the process for creating great products should be the same. Focusing on the customer needs and pain points helps everyone deliver better products; it’s a muscle that many data science and engineering experts have to build, but ultimately helps us all create better tooling and deliver more value for the customer.

The bottom line is that today, the most effective MLOps strategies are not just about technical capabilities, but also involve intentional and thoughtful culture, collaboration, and communication strategies. In large enterprises, it’s important to be cognizant that no one operates in a vacuum. As hard as it may be to see in the day-to-day, everything within the enterprise is ultimately connected, and the capabilities that AI/ML tooling and engineering teams bring to bear have important implications for the entire organization.

This content was produced by Capital One. It was not written by MIT Technology Review’s editorial staff.


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