The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Latest Episodes:

Natural Graph Networks with Taco Cohen - #440 Dec 21, 2020

Show notes

Today we kick off our NeurIPS 2020 series joined by Taco Cohen, a Machine Learning Researcher at Qualcomm Technologies.

In our conversation with Taco, we discuss his current research in equivariant networks and video compression using generative models, as well as his paper “Natural Graph Networks,” which explores the concept of “naturality, a generalization of equivariance” which suggests that weaker constraints will allow for a “wider class of architectures.”

We also discuss some of Taco’s recent research on neural compression and a very interesting visual demo for equivariance CNNs that Taco and the Qualcomm team released during the conference.

The complete show notes for this episode can be found at twimlai.com/go/440.

Productionizing Time-Series Workloads at Siemens Energy with Edgar Bahilo Rodriguez - #439 Dec 18, 2020

Show notes

Today we close out our re:Invent series joined by Edgar Bahilo Rodriguez, Lead Data Scientist in the industrial applications division of Siemens Energy.

Edgar spoke at this year's re:Invent conference about Productionizing R Workloads, and the resurrection of R for machine learning and productionalization. In our conversation with Edgar, we explore the fundamentals of building a strong machine learning infrastructure, and how they’re breaking down applications and using mixed technologies to build models.

We also discuss their industrial applications, including wind, power production management, managing systems intent on decreasing the environmental impact of pre-existing installations, and their extensive use of time-series forecasting across these use cases.

The complete show notes can be found at twimlai.com/go/439.

ML Feature Store at Intuit with Srivathsan Canchi - #438 Dec 16, 2020

Show notes

Today we continue our re:Invent series with Srivathsan Canchi, Head of Engineering for the Machine Learning Platform team at Intuit.

As we teased earlier this week, one of the major announcements coming from AWS at re:Invent was the release of the SageMaker Feature Store. To our pleasant surprise, we came to learn that our friends at Intuit are the original architects of this offering and partnered with AWS to productize it at a much broader scale. In our conversation with Srivathsan, we explore the focus areas that are supported by the Intuit machine learning platform across various teams, including QuickBooks and Mint, Turbotax, and Credit Karma, and his thoughts on why companies should be investing in feature stores.

We also discuss why the concept of “feature store” has seemingly exploded in the last year, and how you know when your organization is ready to deploy one. Finally, we dig into the specifics of the feature store, including the popularity of graphQL and why they chose to include it in their pipelines, the similarities (and differences) between the two versions of the store, and much more!

The complete show notes for this episode can be found at twimlai.com/go/438.

re:Invent Roundup 2020 with Swami Sivasubramanian - #437 Dec 14, 2020

Show notes

Today we’re kicking off our annual re:invent series joined by Swami Sivasubramanian, VP of Artificial Intelligence, at AWS.

During re:Invent last week, Amazon made a ton of announcements on the machine learning front, including quite a few advancements to SageMaker. In this roundup conversation, we discuss the motivation for hosting the first-ever machine learning keynote at the conference, a bunch of details surrounding tools like Pipelines for workflow management, Clarify for bias detection, and JumpStart for easy to use algorithms and notebooks, and many more.

We also discuss the emphasis placed on DevOps and MLOps tools in these announcements, and how the tools are all interconnected. Finally, we briefly touch on the announcement of the AWS feature store, but be sure to check back later this week for a more in-depth discussion on that particular release!

The complete show notes for this episode can be found at twimlai.com/go/437.

Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436 Dec 11, 2020

Show notes

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.

Scaling Video AI at RTL with Daan Odijk - #435 Dec 09, 2020

Show notes

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.

Benchmarking ML with MLCommons w/ Peter Mattson - #434 Dec 07, 2020

Show notes

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.

Deep Learning for NLP: From the Trenches with Charlene Chambliss - #433 Dec 03, 2020

Show notes

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.

Feature Stores for Accelerating AI Development - #432 Nov 30, 2020

Show notes

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.

An Exploration of Coded Bias with Shalini Kantayya, Deb Raji and Meredith Broussard - #431 Nov 27, 2020

Show notes

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

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