Introducing DataRobot Pathfinder The potential impact AI will have on the global economy has been estimated to be huge -- about $13 trillion by 2030 (McKinsey). However, even with all the research and media around AI, many executives are still perplexed as to how AI can improve the profitability of their individual organizations. From the difficulties of choosing the right use case to the intricacies of adopting predictions into existing workflows, AI comes with a wide array of challenges that prevent organizations from maximizing the returns on their AI investments.

Check out all of the blog series: part 1, part 3, part 4, part 5. “I define nothing. Not beauty, not patriotism. I take each thing as it is, without prior rules about what it should be.” — Bob Dylan It’s entirely possible that someone, somewhere, has successfully built one of the amazing market predicting magic boxes from the previous…

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.

AI is Hard Investments in AI are growing every year, even during economic uncertainty. However, there are plenty of obstacles to successfully implementing artificial intelligence projects and creating organizational change that’s needed to drive them.  In such relatively hostile or apathetic climates, AI projects often succeed only because there’s someone who can see the forest for the trees; a person…

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…

Epigenetics, the study of how genes express themselves, is a growing field rich with potential. A common metaphor to understand how epigenetics differs from genetics is to think of a human genome as a family recipe book, where your DNA is the recipe. Epigenetics are all of the notes written in the margins making tweaks to the recipe, changing 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…