Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436
Today we’re joined by Subarna Sinha, Machine Learning Engineering Leader at 23andMe. 23andMe handles a massive amount of genomic data every year from its core ancestry business but also uses that data for disease prediction, which is the core use case we discuss in our conversation. Subarna talks us through an initial use case of creating an evaluation of polygenic scores, and how that led them to build an ML pipeline and platform. We talk through the tools and tech stack used for the operationalization of their platform, the use of synthetic data, the internal pushback that came along with the changes that were being made, and what’s next for her team and the platform. The complete show notes for this episode can be found at twimlai.com/go/436.
11 Joulu 202039min
Scaling Video AI at RTL with Daan Odijk - #435
Today we’re joined by Daan Odijk, Data Science Manager at RTL. In our conversation with Daan, we explore the RTL MLOps journey, and their need to put platform infrastructure in place for ad optimization and forecasting, personalization, and content understanding use cases. Daan walks us through some of the challenges on both the modeling and engineering sides of building the platform, as well as the inherent challenges of video applications. Finally, we discuss the current state of their platform, and the benefits they’ve seen from having this infrastructure in place, and why using building a custom platform was worth the investment. The complete show notes for this episode can be found at twimlai.com/go/435.
9 Joulu 202040min
Benchmarking ML with MLCommons w/ Peter Mattson - #434
Today we’re joined by Peter Mattson, General Chair at MLPerf, a Staff Engineer at Google, and President of MLCommons. In our conversation with Peter, we discuss MLCommons and MLPerf, the former an open engineering group with the goal of accelerating machine learning innovation, and the latter a set of standardized Machine Learning speed benchmarks used to measure things like model training speed, throughput speed for inference. We explore the target user for the MLPerf benchmarks, the need for benchmarks in the ethics, bias, fairness space, and how they’re approaching this through the "People’s Speech" datasets. We also walk through the MLCommons best practices of getting a model into production, why it's so difficult, and how MLCube can make the process easier for researchers and developers. The complete show notes page for this episode can be found at twimlai.com/go/434.
7 Joulu 202046min
Deep Learning for NLP: From the Trenches with Charlene Chambliss - #433
Today we’re joined by Charlene Chambliss, Machine Learning Engineer at Primer AI. Charlene, who we also had the pleasure of hosting at NLP Office Hours during TWIMLfest, is back to share some of the work she’s been doing with NLP. In our conversation, we explore her experiences working with newer NLP models and tools like BERT and HuggingFace, as well as whats she’s learned along the way with word embeddings, labeling tasks, debugging, and more. We also focus on a few of her projects, like her popular multi-lingual BERT project, and a COVID-19 classifier. Finally, Charlene shares her experience getting into data science and machine learning coming from a non-technical background, and what the transition was like, and tips for people looking to make a similar shift.
3 Joulu 202045min
Feature Stores for Accelerating AI Development - #432
In this special episode of the podcast, we're joined by Kevin Stumpf, Co-Founder and CTO of Tecton, Willem Pienaar, an engineering lead at Gojek and founder of the Feast Project, and Maxime Beauchemin, Founder & CEO of Preset, for a discussion on Feature Stores for Accelerating AI Development. In this panel discussion, Sam and our guests explored how organizations can increase value and decrease time-to-market for machine learning using feature stores, MLOps, and open source. We also discuss the main data challenges of AI/ML, and the role of the feature store in solving those challenges. The complete show notes for this episode can be found at twimlai.com/go/432.
30 Marras 202056min
An Exploration of Coded Bias with Shalini Kantayya, Deb Raji and Meredith Broussard - #431
In this special edition of the podcast, we're joined by Shalini Kantayya, the director of Coded Bias, and Deb Raji and Meredith Broussard, who both contributed to the film. In this panel discussion, Sam and our guests explored the societal implications of the biases embedded within AI algorithms. The conversation discussed examples of AI systems with disparate impact across industries and communities, what can be done to mitigate this disparity, and opportunities to get involved. Our panelists Shalini, Meredith, and Deb each share insight into their experience working on and researching bias in AI systems and the oppressive and dehumanizing impact they can have on people in the real world. The complete show notes for this film can be found at twimlai.com/go/431
27 Marras 20201h 24min
Common Sense as an Algorithmic Framework with Dileep George - #430
Today we’re joined by Dileep George, Founder and the CTO of Vicarious. Dileep, who was also a co-founder of Numenta, works at the intersection of AI research and neuroscience, and famously pioneered the hierarchical temporal memory. In our conversation, we explore the importance of mimicking the brain when looking to achieve artificial general intelligence, the nuance of “language understanding” and how all the tasks that fall underneath it are all interconnected, with or without language. We also discuss his work with Recursive Cortical Networks, Schema Networks, and what’s next on the path towards AGI!
23 Marras 202047min
Scaling Enterprise ML in 2020: Still Hard! with Sushil Thomas - #429
Today we’re joined by Sushil Thomas, VP of Engineering for Machine Learning at Cloudera. Over the summer, I had the pleasure of hosting Sushil and a handful of business leaders across industries at the Cloudera Virtual Roundtable. In this conversation with Sushil, we recap the roundtable, exploring some of the topics discussed and insights gained from those conversations. Sushil gives us a look at how COVID19 has impacted business throughout the year, and how the pandemic is shaping enterprise decision making moving forward. We also discuss some of the key trends he’s seeing as organizations try to scale their machine learning and AI efforts, including understanding best practices, and learning how to hybridize the engineering side of ML with the scientific exploration of the tasks. Finally, we explore if organizational models like hub vs centralized are still organization-specific or if that’s changed in recent years, as well as how to get and retain good ML talent with giant companies like Google and Microsoft looming large. The complete show notes for this episode can be found at https://twimlai.com/go/429.
19 Marras 202046min
