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Building Internal AI Capabilities

Building Internal AI Capabilities: How to ensure you have the right infrastructure & expertise

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Companies typically have a culture of valuing data and use diagnostic and descriptive analytics to understand it. Investing in company-wide analytics or business intelligence initiatives gives companies operations and cultural advantages.

Analytics, however, is not enough. You must build a team of data scientists and machine learning engineers who understand the value of different data, how to capture new data, and can analyze patterns within big data. Here’s an excerpt from Hackernoon on key roles in artificial intelligence and data science:


If you’re dealing with small datasets, data engineering is essentially entering some numbers into a spreadsheet. When you operate at a more impressive scale, data engineering becomes a sophisticated discipline in its own right. Someone on your team will need to take responsibility for dealing with the tricky engineering aspects of delivering data that the rest of your staff can work with.


This person can look at more data faster. The game here is speed, exploration, and discovery (another term for analytics is data mining.) This is not the role concerned with rigor and careful conclusions. Instead, this is the person who helps your team get eyes on as much of your data as possible so that your decision-maker can get a sense of what’s worth pursuing with more care.

The job here is speed, encountering potential insights as quickly as possible. Unfortunately, those who obsess over code quality may find it too difficult to zoom through the data fast enough to be useful in this role. Don’t staff this role with your most reliable engineers who write gorgeous, robust code.


Now that we’ve got all these folks zealously exploring data, we’d better have someone around to put a damper on the feeding frenzy. It might be a good idea to have someone around who can prevent the team from making unwarranted conclusions. For example, if your machine learning system worked in one dataset, all you can safely conclude is that it worked in that dataset. Will it work when it’s running in production? Should you launch it?

If you’re wanting to make serious decisions where you don’t have perfect facts, slow down and take a careful approach. Statisticians help decision-makers come to conclusions safely beyond the data.


A machine learning engineer’s best attribute is not an understanding of how algorithms work. Their job is to use them, not build them (that’s what researchers do). Expertise at wrangling code that gets existing algorithms to accept and churn through your datasets is what you’re looking for.

A huge part of the job is dabbling blindly, and it takes a certain kind of personality to enjoy that. Perfectionists tend to struggle as ML engineers. Although there’s a lot of tinkering, it’s important for the machine learning engineer to have a deep respect for the part of the process where rigor is vital: assessment.

The strongest applied ML engineers have a very good sense of how long it takes to apply various approaches. When a potential ML hire can rank options by the time it takes to try them on various kinds of datasets, be impressed.


A data scientist is someone who is a full expert in analytics, statistics, and machine learning. Not everyone feels this way though. You’ll see job applications out there with people calling themselves a “data scientist,” but they’ve really only mastered one of the three focus areas. We advise you to thoroughly probe an individual’s experience when evaluating hires for this role.


This hybrid role acts as a force-multiplier, ensuring that your data science team isn’t off in the weeds instead of adding value to your business.

Unfortunately, these individuals are rare and hard to hire. They’re kept awake at night by questions like, “How do we design the right questions? How do we make decisions? How do we best allocate our experts? What’s worth doing? Will the skills and data match the requirements? How do we ensure good input data?” If you’re lucky enough to hire one of these, hold on to them and never let them go.


The final critical role is the Software Engineer. Software engineers are full-stack developers who can take a model and build a solution around it. Models by themselves are not all that useful. Development chops are critical to bottle up the intelligence produced by a model and integrate it into existing systems or build a new one.

Want more insights into how you can staff up your internal AI team? Download our free whitepaper here.

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