ML/DL for Non-Stationary Time Series Analysis in Financial Markets and Beyond with Stuart Reid - TWiML Talk #203
Today, we’re joined by Stuart Reid, Chief Scientist at NMRQL Research. NMRQL is an investment management firm that uses ML algorithms to make adaptive, unbiased, scalable, and testable trading decisions for its funds. In our conversation, Stuart and I dig into the way NMRQL uses ML and DL models to support the firm’s investment decisions. We focus on techniques for modeling non-stationary time-series, stationary vs non-stationary time-series, and challenges of building models using financial data.
26 Nov 201858min
Industrializing Machine Learning at Shell with Daniel Jeavons - TWiML Talk #202
In this episode of our AI Platforms series, we’re joined by Daniel Jeavons, General Manager of Data Science at Shell. In our conversation, we explore the evolution of analytics and data science at Shell, discussing IoT-related applications and issues, such as inference at the edge, federated ML, and digital twins, all key considerations for the way they apply ML. We also talk about the data science process at Shell and the importance of platform technologies to the company as a whole.
21 Nov 201845min
Resurrecting a Recommendations Platform at Comcast with Leemay Nassery - TWiML Talk #201
In this episode of our AI Platforms series, we’re joined by Leemay Nassery, Senior Engineering Manager and head of the recommendations team at Comcast. In our conversation, Leemay and I discuss just how she and her team resurrected the Xfinity X1 recommendations platform, including the rebuilding the data pipeline, the machine learning process, and the deployment and training of their updated models. We also touch on the importance of A-B testing and maintaining their rebuilt infrastructure.
19 Nov 201847min
Productive Machine Learning at LinkedIn with Bee-Chung Chen - TWiML Talk #200
In this episode of our AI Platforms series, we’re joined by Bee-Chung Chen, Principal Staff Engineer and Applied Researcher at LinkedIn. Bee-Chung and I caught up to discuss LinkedIn’s internal AI automation platform, Pro-ML. Bee-Chung breaks down some of the major pieces of the pipeline, LinkedIn’s experience bringing Pro-ML to the company's developers and the role the LinkedIn AI Academy plays in helping them get up to speed. For the complete show notes, visit https://twimlai.com/talk/200.
15 Nov 201847min
Scaling Deep Learning on Kubernetes at OpenAI with Christopher Berner - TWiML Talk #199
In this episode of our AI Platforms series we’re joined by OpenAI’s Head of Infrastructure, Christopher Berner. In our conversation, we discuss the evolution of OpenAI’s deep learning platform, the core principles which have guided that evolution, and its current architecture. We dig deep into their use of Kubernetes and discuss various ecosystem players and projects that support running deep learning at scale on the open source project.
12 Nov 201849min
Bighead: Airbnb's Machine Learning Platform with Atul Kale - TWiML Talk #198
In this episode of our AI Platforms series, we’re joined by Atul Kale, Engineering Manager on the machine learning infrastructure team at Airbnb. In our conversation, we discuss Airbnb’s internal machine learning platform, Bighead. Atul outlines the ML lifecycle at Airbnb and how the various components of Bighead support it. We then dig into the major components of Bighead, some of Atul’s best practices for scaling machine learning, and a special announcement that Atul and his team made at Strata.
8 Nov 201849min
Facebook's FBLearner Platform with Aditya Kalro - TWiML Talk #197
In the kickoff episode of our AI Platforms series, we’re joined by Aditya Kalro, Engineering Manager at Facebook, to discuss their internal machine learning platform FBLearner Flow. FBLearner Flow is the workflow management platform at the heart of the Facebook ML engineering ecosystem. We discuss the history and development of the platform, as well as its functionality and its evolution from an initial focus on model training to supporting the entire ML lifecycle at Facebook.
6 Nov 201838min
Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane - TWiML Talk #196
In this episode we’re joined by Nina Miolane, researcher and lecturer at Stanford University. Nina and I spoke about her work in the field of geometric statistics in ML, specifically the application of Riemannian geometry, which is the study of curved surfaces, to ML. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric structures.
1 Nov 201843min
