Machine learning techniques cannot magically make “signal” appear out of thin air, nor can they make unstable factors more stable. Data science typically succeeds where there are complex behaviors that can be described in data and where consistent inputs lead to consistent, predictable outcomes.
Nevertheless, there are a number of useful aspects that set apart machine learning techniques from the traditional quantitative toolbox, and modern automated machine learning is a compelling proposition for modern quantitative traders, analysts, and investors.
Join Peter Simon, DataRobot’s lead data scientist for financial markets, on part two of the podcast, AI in Financial Markets.
What can be automated using an enterprise AI platform to address two very common classes of AI problems, and how this automation can be used to exponentially increase the problem space to be searched
How advanced users in front office securities industry roles can achieve great increases in productivity and effectiveness with AutoML, and what they can do with this new found power
Best practices to ensure that your machine learning models don’t just look great on paper, but also make money in production
Lead Data Scientist, DataRobot
DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
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