51 episodes

Brought to you by the folks at Weights & Biases, Gradient Dissent is a weekly machine learning podcast that takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at Facebook, Google, Lyft, OpenAI, Salesforce, iRobot, Stanford and more.

Gradient Dissent - A Machine Learning Podcast by W&B Lukas Biewald

    • Technology
    • 4.7 • 26 Ratings

Brought to you by the folks at Weights & Biases, Gradient Dissent is a weekly machine learning podcast that takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at Facebook, Google, Lyft, OpenAI, Salesforce, iRobot, Stanford and more.

    Building AI-powered primary care with Curai's CTO, Xavier Amatriain

    Building AI-powered primary care with Curai's CTO, Xavier Amatriain

    Xavier shares his experience deploying healthcare models, augmenting primary care with AI, the challenges of "ground truth" in medicine, and robustness in ML.

    ---

    Xavier Amatriain is co-founder and CTO of Curai, an ML-based primary care chat system. Previously, he was VP of Engineering at Quora, and Research/Engineering Director at Neflix, where he started and led the Algorithms team responsible for Netflix's recommendation systems.

    ---

    ⏳ Timestamps:
    0:00 Sneak peak, intro
    0:49 What is Curai?
    5:48 The role of AI within Curai
    8:44 Why Curai keeps humans in the loop
    15:00 Measuring diagnostic accuracy
    18:53 Patient safety
    22:39 Different types of models at Curai
    25:42 Using GPT-3 to generate training data
    32:13 How Curai monitors and debugs models
    35:19 Model explainability
    39:27 Robustness in ML
    45:52 Connecting metrics to impact
    49:32 Outro

    🌟 Show notes:
    - http://wandb.me/gd-xavier-amatriain
    - Transcription of the episode
    - Links to papers, projects, and people

    ---

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    Get our podcast on these platforms:
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    👉 Soundcloud: http://wandb.me/soundcloud​

    • 50 min
    Enterprise-scale machine translation with Spence Green, CEO of Lilt

    Enterprise-scale machine translation with Spence Green, CEO of Lilt

    Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.

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    Spence Green is co-founder and CEO of Lilt, an AI-powered language translation platform. Lilt combines human translators and machine translation in order to produce high-quality translations more efficiently.

    ---

    🌟 Show notes:
    - http://wandb.me/gd-spence-green
    - Transcription of the episode
    - Links to papers, projects, and people

    ⏳ Timestamps:
    0:00 Sneak peak, intro
    0:45 The story behind Lilt
    3:08 Statistical MT vs neural MT
    6:30 Domain adaptation and personalized models
    8:00 The emergence of neural MT and development of Lilt
    13:09 What success looks like for Lilt
    18:20 Models that self-correct for gender bias
    19:39 How Lilt runs its models in production
    26:33 How far can MT go?
    29:55 Why Lilt cares about human-computer interaction
    35:04 Bilingual grammatical error correction
    37:18 Human parity in MT
    39:41 The unexpected challenges of prototype to production


    ---

    Get our podcast on these platforms:
    👉 Apple Podcasts: http://wandb.me/apple-podcasts​​
    👉 Spotify: http://wandb.me/spotify​
    👉 Google Podcasts: http://wandb.me/google-podcasts​​
    👉 YouTube: http://wandb.me/youtube​​
    👉 Soundcloud: http://wandb.me/soundcloud​

    Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
    http://wandb.me/slack​​

    Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
    https://wandb.ai/fully-connected

    • 43 min
    The rise of big data and responding to COVID-19 with Roger and DJ

    The rise of big data and responding to COVID-19 with Roger and DJ

    Roger and DJ share some of the history behind data science as we know it today, and reflect on their experiences working on California's COVID-19 response.

    ---

    Roger Magoulas is Senior Director of Data Strategy at Astronomer, where he works on data infrastructure, analytics, and community development. Previously, he was VP of Research at O'Reilly and co-chair of O'Reilly's Strata Data and AI Conference.

    DJ Patil is a board member and former CTO of Devoted Health, a healthcare company for seniors. He was also Chief Data Scientist under the Obama administration and the Head of Data Science at LinkedIn.

    Roger and DJ recently volunteered for the California COVID-19 response, and worked with data to understand case counts, bed capacities and the impact of intervention.

    Connect with Roger and DJ:
    📍 Roger's Twitter: https://twitter.com/rogerm
    📍 DJ's Twitter: https://twitter.com/dpatil

    ---

    🌟 Transcript: http://wandb.me/gd-roger-and-dj 🌟

    ⏳ Timestamps:
    0:00 Sneak peek, intro
    1:03 Coining the terms "big data" and "data scientist"
    7:12 The rise of data science teams
    15:28 Big Data, Hadoop, and Spark
    23:10 The importance of using the right tools
    29:20 BLUF: Bottom Line Up Front
    34:44 California's COVID response
    41:21 The human aspects of responding to COVID
    48:33 Reflecting on the impact of COVID interventions
    57:06 Advice on doing meaningful data science work
    1:04:18 Outro

    🍀 Links:
    1. "MapReduce: Simplified Data Processing on Large Clusters" (Dean and Ghemawat, 2004): https://research.google/pubs/pub62/
    2. "Big Data: Technologies and Techniques for Large-Scale Data" (Magoulas and Lorica, 2009): https://academics.uccs.edu/~ooluwada/courses/datamining/ExtraReading/BigData
    3. The O'RLY book covers: https://www.businessinsider.com/these-hilarious-memes-perfectly-capture-what-its-like-to-work-in-tech-2016-4
    4. "The Premonition" (Lewis, 2021): https://www.npr.org/2021/05/03/991570372/michael-lewis-the-premonition-is-a-sweeping-indictment-of-the-cdc
    5. Why California's beaches are glowing with bioluminescence: https://www.youtube.com/watch?v=AVYSr19ReOs
    6.
    7. Sturgis Motorcyle Rally: https://en.wikipedia.org/wiki/Sturgis_Motorcycle_Rally

    ---

    Get our podcast on these platforms:
    👉 Apple Podcasts: http://wandb.me/apple-podcasts​​
    👉 Spotify: http://wandb.me/spotify​
    👉 Google Podcasts: http://wandb.me/google-podcasts​​
    👉 YouTube: http://wandb.me/youtube​​
    👉 Soundcloud: http://wandb.me/soundcloud​

    Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
    http://wandb.me/slack​​

    Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
    https://wandb.ai/fully-connected

    • 1 hr 4 min
    How Pandora deploys machine learning models into production with Amelia and Filip

    How Pandora deploys machine learning models into production with Amelia and Filip

    Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in production.

    ---

    Amelia Nybakke is a Software Engineer at Pandora. Her team is responsible for the production system that serves models to listeners.
    Filip Korzeniowski is a Senior Scientist at Pandora working on recommender systems. Before that, he was a PhD student working on deep neural networks for acoustic and language modeling applied to musical audio recordings.

    Connect with Amelia and Filip:
    📍 Amelia's LinkedIn: https://www.linkedin.com/in/amelia-nybakke-60bba5107/
    📍 Filip's LinkedIn: https://www.linkedin.com/in/filip-korzeniowski-28b33815a/

    ---

    ⏳ Timestamps:
    0:00 Sneak peek, intro
    0:42 What type of ML models are at Pandora?
    3:39 What makes two songs similar or not similar?
    7:33 Improving models and A/B testing
    8:52 Chaining, retraining, versioning, and tracking models
    13:29 Useful development tools
    15:10 Debugging models
    18:28 Communicating progress
    20:33 Tuning and improving models
    23:08 How Pandora puts models into production
    29:45 Bias in ML models
    36:01 Repetition vs novelty in recommended songs
    38:01 The bottlenecks of deployment

    🌟 Transcript: http://wandb.me/gd-amelia-and-filip 🌟

    Links:
    📍 Amelia's "Women's History Month" playlist: https://www.pandora.com/playlist/PL:1407374934299927:100514833

    ---

    Get our podcast on these platforms:
    👉 Apple Podcasts: http://wandb.me/apple-podcasts​​
    👉 Spotify: http://wandb.me/spotify​
    👉 Google Podcasts: http://wandb.me/google-podcasts​​
    👉 YouTube: http://wandb.me/youtube​​
    👉 Soundcloud: http://wandb.me/soundcloud​

    Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
    http://wandb.me/slack​​

    Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
    https://wandb.ai/fully-connected

    • 40 min
    OctoML CEO Luis Ceze on accelerating machine learning systems

    OctoML CEO Luis Ceze on accelerating machine learning systems

    From Apache TVM to OctoML, Luis gives direct insight into the world of ML hardware optimization, and where systems optimization is heading.

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    Luis Ceze is co-founder and CEO of OctoML, co-author of the Apache TVM Project, and Professor of Computer Science and Engineering at the University of Washington. His research focuses on the intersection of computer architecture, programming languages, machine learning, and molecular biology.

    Connect with Luis:
    📍 Twitter: https://twitter.com/luisceze
    📍 University of Washington profile: https://homes.cs.washington.edu/~luisceze/

    ---

    ⏳ Timestamps:
    0:00 Intro and sneak peek
    0:59 What is TVM?
    8:57 Freedom of choice in software and hardware stacks
    15:53 How new libraries can improve system performance
    20:10 Trade-offs between efficiency and complexity
    24:35 Specialized instructions
    26:34 The future of hardware design and research
    30:03 Where does architecture and research go from here?
    30:56 The environmental impact of efficiency
    32:49 Optimizing and trade-offs
    37:54 What is OctoML and the Octomizer?
    42:31 Automating systems design with and for ML
    44:18 ML and molecular biology
    46:09 The challenges of deployment and post-deployment

    🌟 Transcript: http://wandb.me/gd-luis-ceze 🌟

    Links:
    1. OctoML: https://octoml.ai/
    2. Apache TVM: https://tvm.apache.org/
    3. "Scalable and Intelligent Learning Systems" (Chen, 2019): https://digital.lib.washington.edu/researchworks/handle/1773/44766
    4. "Principled Optimization Of Dynamic Neural Networks" (Roesch, 2020): https://digital.lib.washington.edu/researchworks/handle/1773/46765
    5. "Cross-Stack Co-Design for Efficient and Adaptable Hardware Acceleration" (Moreau, 2018): https://digital.lib.washington.edu/researchworks/handle/1773/43349
    6. "TVM: An Automated End-to-End Optimizing Compiler for Deep Learning" (Chen et al., 2018): https://www.usenix.org/system/files/osdi18-chen.pdf
    7. Porcupine is a molecular tagging system introduced in "Rapid and robust assembly and decoding of molecular tags with DNA-based nanopore signatures" (Doroschak et al., 2020): https://www.nature.com/articles/s41467-020-19151-8

    ---

    Get our podcast on these platforms:
    👉 Apple Podcasts: http://wandb.me/apple-podcasts​​
    👉 Spotify: http://wandb.me/spotify​
    👉 Google Podcasts: http://wandb.me/google-podcasts​​
    👉 YouTube: http://wandb.me/youtube​​
    👉 Soundcloud: http://wandb.me/soundcloud​

    Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
    http://wandb.me/slack​​

    Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
    https://wandb.ai/fully-connected

    • 48 min
    Bringing genetic insights to everyone with Invitae's Head of AI, Matthew Davis

    Bringing genetic insights to everyone with Invitae's Head of AI, Matthew Davis

    Matthew explains how combining machine learning and computational biology can provide mainstream medicine with better diagnostics and insights.

    ---

    Matthew Davis is Head of AI at Invitae, the largest and fastest growing genetic testing company in the world. His research includes bioinformatics, computational biology, NLP, reinforcement learning, and information retrieval. Matthew was previously at IBM Research AI, where he led a research team focused on improving AI systems.

    Connect with Matthew:
    📍 Personal website: https://www.linkedin.com/in/matthew-davis-51233386/
    📍 Twitter: https://twitter.com/deadsmiths

    ---

    ⏳ Timestamps:
    0:00 Sneak peek, intro
    1:02 What is Invitae?
    2:58 Why genetic testing can help everyone
    7:51 How Invitae uses ML techniques
    14:02 Modeling molecules and deciding which genes to look at
    22:22 NLP applications in bioinformatics
    27:10 Team structure at Invitae
    36:50 Why reasoning is an underrated topic in ML
    40:25 Why having a clear buy-in is important

    🌟 Transcript: http://wandb.me/gd-matthew-davis 🌟

    Links:
    📍 Invitae: https://www.invitae.com/en
    📍 Careers at Invitae: https://www.invitae.com/en/careers/

    ---

    Get our podcast on these platforms:
    👉 Apple Podcasts: http://wandb.me/apple-podcasts​​
    👉 Spotify: http://wandb.me/spotify​
    👉 Google Podcasts: http://wandb.me/google-podcasts​​
    👉 YouTube: http://wandb.me/youtube​​
    👉 Soundcloud: http://wandb.me/soundcloud​

    Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
    http://wandb.me/slack​​

    Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
    https://wandb.ai/fully-connected

    • 43 min

Customer Reviews

4.7 out of 5
26 Ratings

26 Ratings

humblylearning ,

Always learning new things

Where rubber meets the road. That is what this podcast is all about. It shares very notable and relevant ML/AI real life situations with many lessons learned.

bulbul ahmmed ,

Great host and guests

I like the way Lukas host his podcasts. Also, he invites guests who are best in the industry.

moonpiesandbox ,

Love it

Great guests and information about doing machine learning in the real world. I love that

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