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 best practices that set apart machine learning techniques from the traditional quantitative toolbox, and automated machine learning has a compelling proposition for modern quantitative traders and investors.
Join Peter Simon, DataRobot’s lead data scientist for financial markets, for a live webinar as we explore how to harness the power of automated machine learning to make quants and data scientists massively more efficient and productive.
- How to ensure that your expertise is put to good use
- How automated machine learning allows your team to build sophisticated models without requiring a technical data science background
- Common misconceptions and pitfalls in financial machine learning and how to avoid them
- How to ensure that your machine learning models don’t just look great on paper, but also make money in production
- Why ‘black boxes’ are no longer a thing
- And much more.
Managing Director, Financial Markets Data Science
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.
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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