Jupyter and the Evolution of ML Tooling with Brian Granger - #544
Today we conclude our AWS re:Invent coverage joined by Brian Granger, a senior principal technologist at Amazon Web Services, and a co-creator of Project Jupyter. In our conversion with Brian, we discuss the inception and early vision of Project Jupyter, including how the explosion of machine learning and deep learning shifted the landscape for the notebook, and how they balanced the needs of these new user bases vs their existing community of scientific computing users. We also explore AWS’s role with Jupyter and why they’ve decided to invest resources in the project, Brian's thoughts on the broader ML tooling space, and how they’ve applied (and the impact of) HCI principles to the building of these tools. Finally, we dig into the recent Sagemaker Canvas and Studio Lab releases and Brian’s perspective on the future of notebooks and the Jupyter community at large. The complete show notes for this episode can be found at twimlai.com/go/544
13 Dec 202157min
Creating a Data-Driven Culture at ADP with Jack Berkowitz - #543
Today we continue our 2021 re:Invent series joined by Jack Berkowitz, chief data officer at ADP. In our conversation with Jack, we explore the ever evolving role and growth of machine learning at the company, from the evolution of their ML platform, to the unique team structure. We discuss Jack’s perspective on data governance, the broad use cases for ML, how they approached the decision to move to the cloud, and the impact of scale in the way they deal with data. Finally, we touch on where innovation comes from at ADP, and the challenge of getting the talent it needs to innovate as a large “legacy” company. The complete show notes for this episode can be found at twimlai.com/go/543
9 Dec 202134min
re:Invent Roundup 2021 with Bratin Saha - #542
Today we’re joined by Bratin Saha, vice president and general manager at Amazon. In our conversation with Bratin, we discuss quite a few of the recent ML-focused announcements coming out of last weeks re:Invent conference, including new products like Canvas and Studio Lab, as well as upgrades to existing services like Ground Truth Plus. We explore what no-code environments like the aforementioned Canvas mean for the democratization of ML tooling, and some of the key challenges to delivering it as a consumable product. We also discuss industrialization as a subset of MLOps, and how customer patterns inform the creation of these tools, and much more! The complete show notes for this episode can be found at twimlai.com/go/542.
6 Dec 202141min
Multi-modal Deep Learning for Complex Document Understanding with Doug Burdick - #541
Today we’re joined by Doug Burdick, a principal research staff member at IBM Research. In a recent interview, Doug’s colleague Yunyao Li joined us to talk through some of the broader enterprise NLP problems she’s working on. One of those problems is making documents machine consumable, especially with the traditionally archival file type, the PDF. That’s where Doug and his team come in. In our conversation, we discuss the multimodal approach they’ve taken to identify, interpret, contextualize and extract things like tables from a document, the challenges they’ve faced when dealing with the tables and how they evaluate the performance of models on tables. We also explore how he’s handled generalizing across different formats, how fine-tuning has to be in order to be effective, the problems that appear on the NLP side of things, and how deep learning models are being leveraged within the group. The complete show notes for this episode can be found at twimlai.com/go/541
2 Dec 202145min
Predictive Maintenance Using Deep Learning and Reliability Engineering with Shayan Mortazavi - #540
Today we’re joined by Shayan Mortazavi, a data science manager at Accenture. In our conversation with Shayan, we discuss his talk from the recent SigOpt HPC & AI Summit, titled A Novel Framework Predictive Maintenance Using Dl and Reliability Engineering. In the talk, Shayan proposes a novel deep learning-based approach for prognosis prediction of oil and gas plant equipment in an effort to prevent critical damage or failure. We explore the evolution of reliability engineering, the decision to use a residual-based approach rather than traditional anomaly detection to determine when an anomaly was happening, the challenges of using LSTMs when building these models, the amount of human labeling required to build the models, and much more! The complete show notes for this episode can be found at twimlai.com/go/540
29 Nov 202149min
Building a Deep Tech Startup in NLP with Nasrin Mostafazadeh - #539
Today we’re joined by friend-of-the-show Nasrin Mostafazadeh, co-founder of Verneek. Though Verneek is still in stealth, Nasrin was gracious enough to share a bit about the company, including their goal of enabling anyone to make data-informed decisions without the need for a technical background, through the use of innovative human-machine interfaces. In our conversation, we explore the state of AI research in the domains relevant to the problem they’re trying to solve and how they use those insights to inform and prioritize their research agenda. We also discuss what advice Nasrin would give to someone thinking about starting a deep tech startup or going from research to product development. The complete show notes for today’s show can be found at twimlai.com/go/539.
24 Nov 202151min
Models for Human-Robot Collaboration with Julie Shah - #538
Today we’re joined by Julie Shah, a professor at the Massachusetts Institute of Technology (MIT). Julie’s work lies at the intersection of aeronautics, astronautics, and robotics, with a specific focus on collaborative and interactive robotics. In our conversation, we explore how robots would achieve the ability to predict what their human collaborators are thinking, what the process of building knowledge into these systems looks like, and her big picture idea of developing a field robot that doesn’t “require a human to be a robot” to work with it. We also discuss work Julie has done on cross-training between humans and robots with the focus on getting them to co-learn how to work together, as well as future projects that she’s excited about. The complete show notes for this episode can be found at twimlai.com/go/538.
22 Nov 202142min
Four Key Tools for Robust Enterprise NLP with Yunyao Li - #537
Today we’re joined by Yunyao Li, a senior research manager at IBM Research. Yunyao is in a somewhat unique position at IBM, addressing the challenges of enterprise NLP in a traditional research environment, while also having customer engagement responsibilities. In our conversation with Yunyao, we explore the challenges associated with productizing NLP in the enterprise, and if she focuses on solving these problems independent of one another, or through a more unified approach. We then ground the conversation with real-world examples of these enterprise challenges, including enabling level document discovery at scale using combinations of techniques like deep neural networks and supervised and/or unsupervised learning, and entity extraction and semantic parsing to identify text. Finally, we talk through data augmentation in the context of NLP, and how we enable the humans in-the-loop to generate high-quality data. The complete show notes for this episode can be found at twimlai.com/go/537
18 Nov 202158min
