Building the Better, More Scalable Algorithms with SigOpt’s Scott Clark

An A.I. the model is similar to a boat in that it needs constant maintenance to perform. The reality is  A.I. models need adjusted boundaries and guidelines to remain efficient.  And when you live in a world where everyone is trying to get bigger and faster and have a certain edge, Scott Clark is helping make that possible with his finely-tuned A.I. modeling techniques.

“As you're building up these rules and constructs for how that system will even learn itself, there's a lot of parameters that you need to set and tune. There's all these magical numbers that go into these systems. If you don't have a system of record for this, if you're just throwing things against the wall and seeing what sticks, and then only checking the best one, and you don't have a system of what you tried, what the trade-offs were, which parameters were the most important, and how it traded off different metrics it can seem like a very opaque process. At least that hyper parameter optimization and neural architecture search and kind of tuning part of the process can be a little bit more explainable, a little bit more repeatable and a little bit more optimal.”

More explainable, and more optimal, but most importantly scaleable and reproducible. On this episode of IT Visionaries, Scott, the CEO and Co-founder of SigOpt, a company that’s on a mission to empower modeling systems to reach their fullest potential, explains the basic steps that go into successful models, how his team tweaks and optimizes those models to build more efficient processes. Plus, Scott touches on the future of algorithmic models — including how they will improve and where they struggle. Enjoy this episode.

Main Takeaways

• Bad Data, Bad!: When you’re building algorithm models you have to not only focus on the data you are putting into those models, but you have to know where that data is coming from and if that data is trustworthy. When you have untrustworthy data — either its coming from an unknown source or is bias in any way — this can lead to models that deliver poor results.
• Delivering Consistency: While every algorithm needs to be tweaked and tuned at the start, the best way to deliver consistent, scalable algorithmic models is to make sure you are able to define hyper-specific patterns that the algorithm can abide by. When algorithms know what rules they are looking for (such as this person only likes medium sized shirts with stripes) it has a set of hyper-specific boundaries it can operate off of in order to deliver the best results.
• Where is the Band Conductor?: Algorithms will continue to infiltrate our everyday lives, but the truth is they still need humans to effectively run them, to tune them, and to make sure that the decisions they are making are the right ones. 

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