This series of blog posts is based on a talk the author presented at ODSC Europe 2019 alongside Ayub Hanif, VP at JPMorgan’s Quantitative and Derivatives Strategy. We’ll look at how recent developments in automated machine learning and interpretability can help market participants build, test, and understand powerful AI models that support and enhance their investment processes. But first, some context is needed. Part 1: Beyond the Market-Predicting Magic Box In which we conclude that the existence of this article is strong evidence that we don’t know how to use AI to predict asset prices, either, and try to get real.

DataRobot Intelligent Value Estimator for 2020 MLB Team and Player Performance Recently, we predicted how the 60-game 2020 MLB season would unfold -- for teams and for individual players. Please see our original post for all of the details of how we build the models with DataRobot’s leading artificial intelligence platform. Who is projected to win each division in the…

In our previous post, we talked about how red AI means adding computational power to “buy” more accurate models in machine learning, and especially in deep learning. We also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is. We…

Over the past several years, Machine Learning Operations (commonly referred to as MLOps, or ModelOps) has gained a growing following. With the rise of AI, many organizations recognize the need for a dedicated set of people, processes and technologies to scale AI across the enterprise.  That’s where MLOps comes in. Like DevOps for software development, MLOps helps organizations realize the…

With the continued unfolding of the COVID-19 pandemic, the world’s economies and societies are going through an extended period of uncertainty. This ongoing volatility brings new challenges for organizations and teams managing predictive models.  It’s tricky to maintain a grip on production model management and monitoring under normal circumstances. The current turbulent times highlight this issue even further, with models…

Across the retail sector, companies face heightened challenges in the form of the empowered consumer. Consumers expect more information that is tailored to them about what to buy and where to buy it, as well as in-store safety during the pandemic, online shipment, and information on where the best deals are. With the ubiquity of smartphones and widespread access to…

As the effects of social distancing continue to impact every industry, the retail, sports, and gaming industries are no exception. Supply chains are being redefined. Demand forecasting is more volatile than ever. Companies are moving online, and there is triage in decisions from distribution center planning to product repricing. In parts one and two of this series, we discussed how…

We founded Zeff.ai with a mission to bring automated machine learning to teams working on the naturally complex datasets that arise from building software products and services, including datasets with images, audio, and unstructured data. Initially, we were just excited to see applied deep learning helping a variety of businesses. That excitement grew into an obsession to help as many…

As the world continues to face the global COVID-19 pandemic, AI technology is helping companies minimize disruption with AI-driven forecasting and strategy. In part 1 of this series, we discussed how the healthcare and insurance industries can become more resilient and adapt to their new reality. In this article, we cover how banking, FinTech, and financial markets can mitigate some…