Gradient Dissent: Conversations on AI

In this episode of Gradient Dissent, Isomorphic Labs Chief AI Officer Max Jaderberg, and Chief Technology Officer Sergei Yakneen join our host Lukas Biewald to discuss the advancements in biotech and drug discovery being unlocked with machine learning.With backgrounds in advanced AI research at DeepMind, Max and Sergei offer their unique insights into the challenges and successes of applying AI in a complex field like biotechnology. They share their journey at Isomorphic Labs, a company dedicated to revolutionizing drug discovery with AI. In this episode, they discuss the transformative impact of deep learning on the drug development process and Isomorphic Labs' strategy to innovate from molecular design to clinical trials.You’ll come away with valuable insights into the challenges of applying AI in biotech, the role of AI in streamlining the drug discovery pipeline, and peer into the  future of AI-driven solutions in healthcare.Connect with Sergei Yakneen & Max Jaderberg:https://www.linkedin.com/in/maxjaderberg/ https://www.linkedin.com/in/yakneensergei/ https://twitter.com/SergeiIakhnin https://twitter.com/maxjaderberg Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb 

🚀 Discover the cutting-edge AI hardware development for enterprises in this episode of Gradient Dissent, featuring Rodrigo Liang, CEO of SambaNova Systems. Rodrigo Liang’s journey from Oracle to founding SambaNova is a tale of innovation and determination. In this episode, Rodrigo discusses the importance of specialized hardware in unlocking AI's potential for Enterprise businesses and SambaNova's mission to deliver comprehensive AI solutions from chips to models. Explore the critical insights on navigating the challenges of introducing AI to executives and the evolution of AI applications within large enterprises, and get a glimpse into the future of AI in the business world.🎙 Get our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtubeConnect with Rodrigo Liang:https://www.linkedin.com/in/rodrigo-liang/https://twitter.com/RodrigoLiang  Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3

🚀 This episode of Gradient Dissent welcomes Edo Liberty, the mind behind Pinecone's revolutionary vector database technology.As a former leader at Amazon AI Labs and Yahoo's New York lab, Edo Liberty's extensive background in AI research and development showcases the complexities behind vector databases and their essential role in enhancing AI's capabilities.Discover the pivotal moments and key decisions that have defined Pinecone's journey, learn about the different embedding strategies that are reshaping AI applications, and understand how Pinecone's success has had a profound impact on the technology landscape.Connect with Edo Liberty:https://www.linkedin.com/in/edo-liberty-4380164/ https://twitter.com/EdoLiberty Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3

In this episode of Gradient Dissent, we explore the revolutionary impact of AI across industries with Clara Shih, CEO of Salesforce AI and Founder of Hearsay Systems. Dive into Salesforce AI's cutting-edge approach to customer service through AI, the importance of a trust-first strategy, and the future of AI policies and education. Learn how Salesforce empowers businesses and shapes the future with AI innovations like Prompt Builder and Copilot Studio. Whether you're an AI enthusiast, a business leader, or someone curious about the future of technology, this discussion offers valuable insights into navigating the rapidly evolving world of AI.Subscribe to Weights & Biases YouTube →  https://bit.ly/45BCkYzGet our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleConnect with Clara:https://www.linkedin.com/in/clarashih/https://x.com/clarashih?s=20  Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb 

In the newest episode of Gradient Dissent, we explore the intersecting worlds of AI and Healthcare with John Halamka, President of the Mayo Clinic Platform.Journey with us down John Halamka's remarkable path from his early tech startup days to leading innovations as the President of the Mayo Clinic Platform, one of the world's most esteemed healthcare institutions. This deep dive into AI's role in modern medicine covers the technology's evolution, its potential to redefine patient care, and the visionary work of Mayo Clinic Platform in harnessing AI responsibly.Explore the misconceptions surrounding AI in healthcare and discover the ethical and regulatory frameworks guiding its application. Glimpse into the future with Halamka's visionary perspective on AI's potential to democratize and revolutionize healthcare across the globe. Join us for an enlightening discussion on the challenges, triumphs, and the horizon of AI in healthcare through the lens of John Halamka's pioneering experiences.🎙 Get our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtube✅ Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb 

In the newest episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning.Discover her groundbreaking work, where two-armed robots learn to cook shrimp (messes included!), and discuss how robotic learning could transform student feedback in education.We'll dive into the challenges of developing humanoid and quadruped robots, explore the limitations of simulated environments and discuss why real-world experience is key for adaptable machines. Plus, Chelsea will offer a glimpse into the future of household robotics and why it may be a few years before a robot is making your bed.Whether you're an AI enthusiast, a robotics professional, or simply curious about the potential and future of the technology, this episode offers unique insights into the evolving world of robotics and where it's headed next.*Subscribe to Weights & Biases* → https://bit.ly/45BCkYz🎙 Get our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtubeConnect with Chelsea Finn:https://www.linkedin.com/in/cbfinn/ https://twitter.com/chelseabfinnFollow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3

In the latest episode of Gradient Dissent, Richard Socher, CEO of You.com, shares his insights on the power of AI in search. The episode focuses on how advanced language models like GPT-4 are transforming search engines and changing the way we interact with digital platforms. The discussion covers the practical applications and challenges of integrating AI into search functionality, as well as the ethical considerations and future implications of AI in our digital lives. Join us for an enlightening conversation on how AI and you.com are reshaping how we access and interact with information online.*Subscribe to Weights & Biases* →  https://bit.ly/45BCkYzTimestamps: 00:00 - Introduction to Gradient Dissent Podcast 00:48 - Richard Socher’s Journey: From Linguistic Computer Science to AI 06:42 - The Genesis and Evolution of MetaMind 13:30 - Exploring You.com's Approach to Enhanced Search 18:15 - Demonstrating You.com's AI in Mortgage Calculations 24:10 - The Power of AI in Search: A Deep Dive with You.com 30:25 - Security Measures in Running AI-Generated Code 35:50 - Building a Robust and Secure AI Tech Stack 42:33 - The Role of AI in Automating and Transforming Digital Work 48:50 - Discussing Ethical Considerations and the Societal Impact of AI 55:15 - Envisioning the Future of AI in Daily Life and Work 01:02:00 - Reflecting on the Evolution of AI and Its Future Prospects 01:05:00 - Closing Remarks and Podcast Wrap-Up🎙 Get our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtubeConnect with Richard Socher:https://www.linkedin.com/in/richardsocher/ https://twitter.com/RichardSocher Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3

Explore the Future of Investment & Impact in AI with Host Lukas Biewald and Guests Elad Gill and Sarah Guo of the No Priors podcast.Sarah is the founder of Conviction VC, an AI-centric $100 million venture fund. Elad, a seasoned entrepreneur and startup investor, boasts an impressive portfolio in over 40 companies, each valued at $1 billion or more, and wrote the influential "High Growth Handbook."Join us for a deep dive into the nuanced world of AI, where we'll explore its broader industry impact, focusing on how startups can seamlessly blend product-centric approaches with a balance of innovation and practical development.*Subscribe to Weights & Biases* → https://bit.ly/45BCkYzTimestamps:0:00 - Introduction 5:15 - Exploring Fine-Tuning vs RAG in AI10:30 - Evaluating AI Research for Investment15:45 - Impact of AI Models on Product Development20:00 - AI's Role in Evolving Job Markets25:15 - The Balance Between AI Research and Product Development30:00 - Code Generation Technologies in Software Engineering35:00 - AI's Broader Industry Implications40:00 - Importance of Product-Driven Approaches in AI Startups45:00 - AI in Various Sectors: Beyond Software Engineering50:00 - Open Source vs Proprietary AI Models55:00 - AI's Impact on Traditional Roles and Industries1:00:00 - Closing Thoughts Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Follow Weights & Biases:YouTube: http://wandb.me/youtubeTwitter: https://twitter.com/weights_biases LinkedIn: https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3#OCR #DeepLearning #AI #Modeling #ML

In the latest episode of Gradient Dissent, we explore the innovative features and impact of LlamaIndex in AI data management with Jerry Liu, CEO of LlamaIndex. Jerry shares insights on how LlamaIndex integrates diverse data formats with advanced AI technologies, addressing challenges in data retrieval, analysis, and conversational memory. We also delve into the future of AI-driven systems and LlamaIndex's role in this rapidly evolving field. This episode is a must-watch for anyone interested in AI, data science, and the future of technology.Timestamps:0:00 - Introduction 4:46 - Differentiating  LlamaIndex in the AI framework ecosystem.9:00 - Discussing data analysis, search, and retrieval applications.14:17 - Exploring Retrieval Augmented Generation (RAG) and vector databases.19:33 - Implementing and optimizing One Bot in Discord.24:19 - Developing and evaluating datasets for AI systems.28:00 - Community contributions and the growth of LlamaIndex.34:34 - Discussing embedding models and the use of vector databases.39:33 - Addressing AI model hallucinations and fine-tuning.44:51 - Text extraction applications and agent-based systems in AI.49:25 - Community contributions to LlamaIndex and managing refactors.52:00 - Interactions with big tech's corpus and AI context length.54:59 - Final thoughts on underrated aspects of ML and challenges in AI.Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Connect with Jerry:https://twitter.com/jerryjliu0https://www.linkedin.com/in/jerry-liu-64390071/Follow Weights & Biases:YouTube: http://wandb.me/youtubeTwitter: https://twitter.com/weights_biases LinkedIn: https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3#OCR #DeepLearning #AI #Modeling #ML

In the latest episode of Gradient Dissent, we hear from Joseph Spisak, Product Director, Generative AI @Meta, to explore the boundless impacts of AI and its expansive role in reshaping various sectors. We delve into the intricacies of models like GPT and Llama2, their influence on user experiences, and AI's groundbreaking contributions to fields like biology, material science, and green hydrogen production through the Open Catalyst Project. The episode also examines AI's practical business applications, from document summarization to intelligent note-taking, addressing the ethical complexities of AI deployment. We wrap up with a discussion on the significance of open-source AI development, community collaboration, and AI democratization. Tune in for valuable insights into the expansive world of AI, relevant to developers, business leaders, and tech enthusiasts.We discuss:0:00 Intro0:32 Joe is Back at Meta3:28 What Does Meta Get Out Of Putting Out LLMs?8:24 Measuring The Quality Of LLMs10:55 How Do You Pick The Sizes Of Models16:45 Advice On Choosing Which Model To Start With24:57 The Secret Sauce In The Training26:17 What Is Being Worked On Now33:00 The Safety Mechanisms In Llama 237:00 The Datasets Llama 2 Is Trained On38:00 On Multilingual Capabilities & Tone43:30 On The Biggest Applications Of Llama 247:25 On Why The Best Teams Are Built By Users54:01 The Culture Differences Of Meta vs Open Source57:39 The AI Learning Alliance1:01:34 Where To Learn About Machine Learning1:05:10 Why AI For Science Is Under-rated1:11:36 What Are The Biggest Issues With Real-World ApplicationsThanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

In the premiere episode of Gradient Dissent Business, we're joined by Weights & Biases co-founder Chris Van Pelt for a deep dive into the world of large language models like GPT-3.5 and GPT-4. Chris bridges his expertise as both a tech founder and AI expert, offering key strategies for startups seeking to connect with early users, and for enterprises experimenting with AI. He highlights the melding of AI and traditional web development, sharing his insights on product evolution, leadership, and the power of customer conversations—even for the most introverted founders. He shares how personal development and authentic co-founder relationships enrich business dynamics. Join us for a compelling episode brimming with actionable advice for those looking to innovate with language models, all while managing the inherent complexities. Don't miss Chris Van Pelt's invaluable take on the future of AI in this thought-provoking installment of Gradient Dissent Business.We discuss:0:00 - Intro5:59 - Impactful relationships in Chris's life13:15 - Advice for finding co-founders16:25 - Chris's fascination with challenging problems22:30 - Tech stack for AI labs30:50 - Impactful capabilities of AI models36:24 - How this AI era is different47:36 - Advising large enterprises on language model integration51:18 - Using language models for business intelligence and automation52:13 - Closing thoughts and appreciationThanks for listening to the Gradient Dissent Business podcast, with hosts Lavanya Shukla and Caryn Marooney, brought to you by Weights & Biases. Be sure to click the subscribe button below, to keep your finger on the pulse of this fast-moving space and hear from other amazing guests#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Brandon Duderstadt, Co-Founder and CEO of Nomic AI. Both of Nomic AI’s products, Atlas and GPT4All, aim to improve the explainability and accessibility of AI.We discuss:- (0:55) What GPT4All is and its value proposition.- (6:56) The advantages of using smaller LLMs for specific tasks. - (9:42) Brandon’s thoughts on the cost of training LLMs. - (10:50) Details about the current state of fine-tuning LLMs. - (12:20) What quantization is and what it does. - (21:16) What Atlas is and what it allows you to do.- (27:30) Training code models versus language models.- (32:19) Details around evaluating different models.- (38:34) The opportunity for smaller companies to build open-source models. - (42:00) Prompt chaining versus fine-tuning models.Resources mentioned:Brandon Duderstadt - https://www.linkedin.com/in/brandon-duderstadt-a3269112a/Nomic AI - https://www.linkedin.com/company/nomic-ai/Nomic AI Website - https://home.nomic.ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Soumith Chintala, VP/Fellow of Meta and Co-Creator of PyTorch. Soumith and his colleagues’ open-source framework impacted both the development process and the end-user experience of what would become PyTorch.We discuss:- The history of PyTorch’s development and TensorFlow’s impact on development decisions.- How a symbolic execution model affects the implementation speed of an ML compiler.- The strengths of different programming languages in various development stages.- The importance of customer engagement as a measure of success instead of hard metrics.- Why community-guided innovation offers an effective development roadmap.- How PyTorch’s open-source nature cultivates an efficient development ecosystem.- The role of community building in consolidating assets for more creative innovation.- How to protect community values in an open-source development environment.- The value of an intrinsic organizational motivation structure.- The ongoing debate between open-source and closed-source products, especially as it relates to AI and machine learning.Resources:- Soumith Chintalahttps://www.linkedin.com/in/soumith/- Meta | LinkedInhttps://www.linkedin.com/company/meta/- Meta | Websitehttps://about.meta.com/- Pytorchhttps://pytorch.org/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Andrew Feldman, Founder and CEO of Cerebras Systems. Andrew and the Cerebras team are responsible for building the largest-ever computer chip and the fastest AI-specific processor in the industry.We discuss:- The advantages of using large chips for AI work.- Cerebras Systems’ process for building chips optimized for AI.- Why traditional GPUs aren’t the optimal machines for AI work.- Why efficiently distributing computing resources is a significant challenge for AI work.- How much faster Cerebras Systems’ machines are than other processors on the market.- Reasons why some ML-specific chip companies fail and what Cerebras does differently.- Unique challenges for chip makers and hardware companies.- Cooling and heat-transfer techniques for Cerebras machines.- How Cerebras approaches building chips that will fit the needs of customers for years to come.- Why the strategic vision for what data to collect for ML needs more discussion.Resources:Andrew Feldman - https://www.linkedin.com/in/andrewdfeldman/Cerebras Systems - https://www.linkedin.com/company/cerebras-systems/Cerebras Systems | Website - https://www.cerebras.net/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Harrison Chase, Co-Founder and CEO of LangChain. Harrison and his team at LangChain are on a mission to make the process of creating applications powered by LLMs as easy as possible.We discuss:- What LangChain is and examples of how it works. - Why LangChain has gained so much attention. - When LangChain started and what sparked its growth. - Harrison’s approach to community-building around LangChain. - Real-world use cases for LangChain.- What parts of LangChain Harrison is proud of and which parts can be improved.- Details around evaluating effectiveness in the ML space.- Harrison's opinion on fine-tuning LLMs.- The importance of detailed prompt engineering.- Predictions for the future of LLM providers.Resources:Harrison Chase - https://www.linkedin.com/in/harrison-chase-961287118/LangChain | LinkedIn - https://www.linkedin.com/company/langchain/LangChain | Website - https://docs.langchain.com/docs/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Jean Marc Alkazzi, Applied AI at idealworks. Jean focuses his attention on applied AI, leveraging the use of autonomous mobile robots (AMRs) to improve efficiency within factories and more.We discuss:- Use cases for autonomous mobile robots (AMRs) and how to manage a fleet of them. - How AMRs interact with humans working in warehouses.- The challenges of building and deploying autonomous robots.- Computer vision vs. other types of localization technology for robots.- The purpose and types of simulation environments for robotic testing.- The importance of aligning a robotic fleet’s workflow with concrete business objectives.- What the update process looks like for robots.- The importance of avoiding your own biases when developing and testing AMRs.- The challenges associated with troubleshooting ML systems.Resources: Jean Marc Alkazzi - https://www.linkedin.com/in/jeanmarcjeanazzi/idealworks |LinkedIn - https://www.linkedin.com/company/idealworks-gmbh/idealworks | Website - https://idealworks.com/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Stella Biderman, Executive Director at EleutherAI and Lead Scientist - Mathematician at Booz Allen Hamilton.EleutherAI is a grassroots collective that enables open-source AI research and focuses on the development and interpretability of large language models (LLMs).We discuss:- How EleutherAI got its start and where it's headed.- The similarities and differences between various LLMs.- How to decide which model to use for your desired outcome.- The benefits and challenges of reinforcement learning from human feedback.- Details around pre-training and fine-tuning LLMs.- Which types of GPUs are best when training LLMs.- What separates EleutherAI from other companies training LLMs.- Details around mechanistic interpretability.- Why understanding what and how LLMs memorize is important.- The importance of giving researchers and the public access to LLMs.Stella Biderman - https://www.linkedin.com/in/stellabiderman/EleutherAI - https://www.linkedin.com/company/eleutherai/Resources:- https://www.eleuther.ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Aidan Gomez, Co-Founder and CEO at Cohere. Cohere develops and releases a range of innovative AI-powered tools and solutions for a variety of NLP use cases.We discuss:- What “attention” means in the context of ML.- Aidan’s role in the “Attention Is All You Need” paper.- What state-space models (SSMs) are, and how they could be an alternative to transformers. - What it means for an ML architecture to saturate compute.- Details around data constraints for when LLMs scale.- Challenges of measuring LLM performance.- How Cohere is positioned within the LLM development space.- Insights around scaling down an LLM into a more domain-specific one.- Concerns around synthetic content and AI changing public discourse.- The importance of raising money at healthy milestones for AI development.Aidan Gomez - https://www.linkedin.com/in/aidangomez/Cohere - https://www.linkedin.com/company/cohere-ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Resources:- https://cohere.ai/- “Attention Is All You Need”#OCR #DeepLearning #AI #Modeling #ML

Jonathan Frankle, Chief Scientist at MosaicML and Assistant Professor of Computer Science at Harvard University, joins us on this episode. With comprehensive infrastructure and software tools, MosaicML aims to help businesses train complex machine-learning models using their own proprietary data.We discuss:- Details of Jonathan’s Ph.D. dissertation which explores his “Lottery Ticket Hypothesis.”- The role of neural network pruning and how it impacts the performance of ML models.- Why transformers will be the go-to way to train NLP models for the foreseeable future.- Why the process of speeding up neural net learning is both scientific and artisanal. - What MosaicML does, and how it approaches working with clients.- The challenges for developing AGI.- Details around ML training policy and ethics.- Why data brings the magic to customized ML models.- The many use cases for companies looking to build customized AI models.Jonathan Frankle - https://www.linkedin.com/in/jfrankle/Resources:- https://mosaicml.com/- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural NetworksThanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

About This EpisodeShreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.Show notes (transcript and links): http://wandb.me/gd-shreya---💬 *Host:* Lukas Biewald---*Subscribe and listen to Gradient Dissent today!*👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​