MLOps.community – Details, episodes & analysis

The AI Dream Team: Strategies for ML Recruitment and Growth // MLOps Podcast #267 with Jelmer Borst, Analytics & Machine Learning Domain Lead, and Daniela Solis, Machine Learning Product Owner, of Picnic. // Abstract Like many companies, Picnic started out with a small, central data science team. As this grows larger, focussing on more complex models, it questions the skillsets & organisational set up. Use an ML platform, or build ourselves? A central team vs. embedded? Hire data scientists vs. ML engineers vs. MLOps engineers How to foster a team culture of end-to-end ownership How to balance short-term & long-term impact // Bio Jelmer Borst Jelmer leads the analytics & machine learning teams at Picnic, an app-only online groceries company based in The Netherlands. Whilst his background is in aerospace engineering, he was looking for something faster-paced and found that at Picnic. He loves the intersection of solving business challenges using technology & data. In his free time loves to cook food and tinker with the latest AI developments. Daniela Solis Morales As a Machine Learning Lead at Picnic, I am responsible for ensuring the success of end-to-end Machine Learning systems. My work involves bringing models into production across various domains, including Personalization, Fraud Detection, and Natural Language Processing. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jelmer on LinkedIn: https://www.linkedin.com/in/japborst Connect with Daniela on LinkedIn: https://www.linkedin.com/in/daniela-solis-morales/

Francisco Ingham, LLM consultant, NLP developer, and founder of Pampa Labs. Making Your Company LLM-native // MLOps Podcast #266 with Francisco Ingham, Founder of Pampa Labs. // Abstract Being an LLM-native is becoming one of the key differentiators among companies, in vastly different verticals. Everyone wants to use LLMs, and everyone wants to be on top of the current tech but - what does it really mean to be LLM-native? LLM-native involves two ends of a spectrum. On the one hand, we have the product or service that the company offers, which surely offers many automation opportunities. LLMs can be applied strategically to scale at a lower cost and offer a better experience for users. But being LLM-native not only involves the company's customers, it also involves each stakeholder involved in the company's operations. How can employees integrate LLMs into their daily workflows? How can we as developers leverage the advancements in the field not only as builders but as adopters? We will tackle these and other key questions for anyone looking to capitalize on the LLM wave, prioritizing real results over the hype. // Bio Currently working at Pampa Labs, where we help companies become AI-native and build AI-native products. Our expertise lies on the LLM-science side, or how to build a successful data flywheel to leverage user interactions to continuously improve the product. We also spearhead, pampa-friends - the first Spanish-speaking community of AI Engineers. Previously worked in management consulting, was a TA in fastai in SF, and led the cross-AI + dev tools team at Mercado Libre. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: pampa.ai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Francisco on LinkedIn: https://www.linkedin.com/in/fpingham/ Timestamps: [00:00] Francisco's preferred coffee [00:13] Takeaways [00:37] Please like, share, leave a review, and subscribe to our MLOps channels! [00:51] A Literature Geek [02:41] LLM-native company [03:54] Integrating LLM in workflows [07:21] Unexpected LLM applications [10:38] LLM's in development process [14:00] Vibe check to evaluation [15:36] Experiment tracking optimizations [20:22] LLMs as judges discussion [24:43] Presentaciones automatizadas para podcast [27:48] AI operating system and agents [31:29] Importance of SEO expertise [35:33] Experimentation and evaluation [39:20] AI integration strategies [41:50] RAG approach spectrum analysis [44:40] Search vs Retrieval in AI [49:02] Recommender Systems vs RAG [52:08] LLMs in recommender systems [53:10] LLM interface design insights

Markus Stoll is the Co-Founder of Renumics and the developer behind the open-source interactive ML dataset exploration tool, Spotlight. He shares insights on:

AI in Engineering and Manufacturing
Interactive ML Data Visualization
ML Data Exploration

Follow Markus for hands-on articles about leveraging ML while keeping a strong focus on data.

Visualize - Bringing Structure to Unstructured Data // MLOps Podcast #258 with Markus Stoll, CTO of Renumics. A huge thank you to SAS for their generous support! // Abstract This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models. // Bio Markus Stoll began his career in the industry at Siemens Healthineers, developing software for the Heavy Ion Therapy Center in Heidelberg. He learned about software quality while developing a treatment machine weighing over 600 tons. He earned a Ph.D., focusing on combining biomechanical models with statistical models, through which he learned how challenging it is to bridge the gap between research and practical application in the healthcare domain. Since co-founding Renumics, he has been active in the field of AI for Engineering, e.g., AI for Computer Aided Engineering (CAE), implementing projects, contributing to their open-source library for data exploration for ML datasets (Renumics Spotlight) and writing articles about data visualization. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://renumics.com/ MLSecOps Community: https://community.mlsecops.com/ Blogs: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 : https://medium.com/itnext/how-to-explore-and-visualize-ml-data-for-object-detection-in-images-88e074f46361 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Markus on LinkedIn: https://www.linkedin.com/in/markus-stoll-b39a42138/

MLOps podcast #186 with Mike Del Balso, CEO & Co-founder of Tecton and Josh Wills, Angel Investor, The Future of Feature Stores and Platforms. // Abstract Mike and Josh discuss creating templates and working at a detailed level, exploring Tecton's potential for sharing fraud and third-party features. They focus on technical aspects like data handling and optimizing models, emphasizing the significance of quality data for AI systems and the necessity for cohesive feature infrastructure in reaching production stages. // Bio Mike Del Balso Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. Josh Wills Josh Wills is an angel investor specializing in data and machine learning infrastructure. He was formerly the head of data engineering at Slack, the director of data science at Cloudera, and a software engineer at Google. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/Connect with Josh on LinkedIn: https://www.linkedin.com/in/josh-wills-13882b/ Timestamps: [00:00] Introduction to Mike [01:45] Takeaways [03:32] Features of the new paradigm of ML and LLMs [06:00] D. Sculley's papers [13:05] The birth of Feature Store [17:06] Data Pipeline Challenges Addressed [20:00] Operationalizing [26:50] Feature Store Challenges [30:26] Z access [36:23] Addressing Technical Debt Challenges [37:27] Real-Time vs. Batch Processing [47:10] Feature Store Evolution: Apache Iceberg [49:59] Feature Platform: Dedicated Query Engine [54:04] The bottleneck [56:00] LLMs, Feature Stores Overview [1:00:20] Vector databases [1:06:15] Workflow Templating Efficiency [1:08:35] Gamification suggestion for Tecton [1:10:25] Wrap up

MLOps podcast #185 with Luigi Patruno, VP of Data Science at 2U, Inc, Lessons on Data Science Leadership. // AbstractPicture this: you've got data products to manage, and you're in charge of a team. It's not all sunshine and rainbows, right? Luigi dives into the nitty-gritty of the challenges - from juggling data projects to wrangling the team dynamics. It's a real adventure, let me tell you! // Bio Luigi Patruno is a results-driven data science leader passionate about identifying value-add business opportunities and converting these into analytical solutions that deliver measurable business outcomes. As a leader he focuses on defining strategic vision and, through motivation and discipline, driving teams of highly quantitative data scientists, machine learning engineers, and product managers to achieve extraordinary results. He is currently the VP of Data Science at 2U, where he leads the data science department focused on optimizing business operations through advanced analytics, experimentation, and machine learning. He enjoys teaching others how to leverage data science to improve their businesses through public speaking, teaching courses, and writing online at MLinProduction.com. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://mlinproduction.com/ YouTube channel: https://www.youtube.com/playlist?list=PLBLnN4jzkyqkjLIRpDNZcsG7TMMEk9Asa High Output Management book by Andrew Grove: https://www.amazon.nl/-/en/Andrew-S-Grove/dp/0679762884The One Minute Manager by Kenneth Blanchard Ph.D. and Spencer Johnson M.D.: https://www.amazon.com/Minute-Manager-Kenneth-Blanchard-Ph-D/dp/074350917X ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Luigi on LinkedIn: https://www.linkedin.com/in/luigipatruno91/ Timestamps: [00:00] Luigi's preferred coffee [00:30] Takeaways [03:04] Being practical [05:44] Data-Driven Decision-Making in Management [12:53] Recent Team Win [14:43] The perfect storm [20:22] Change Management and ROI [25:09] Change Management: Navigating Resistance [29:59] Clarifying North Star Communication [36:24] OKRs in Data Science [40:47] Success Likelihood in Business [45:08] Bus problem solution [49:25] Data Science-Platform Collaboration [53:19] Decentralized Platforms Explained [54:38] Data Platform Architecture Overview [57:14] Incentives for Team Motivation [1:09:45] The blind spots [1:12:22] Wrap up

MLOps podcast #184 with Richa Sachdev, Executive Director- Data Operations and Automation at JP Morgan Chase, Data Platforms in MLOps: Translating Business Goals into Product Decisions. // Abstract Richa, with her background in software engineering and experience in the financial sector, shares her insights on optimizing the end-user experience and the importance of understanding business goals and metrics. She discusses her journey in converting legacy applications, working with data platforms, and the challenges of integrating different databases. Richa also explores the role of automation in streamlining processes and improving customer interactions in the reward space. Join us as we unravel the fascinating world of MLOps and uncover the strategies and technologies that drive success in this ever-evolving field. // Bio A passionate and impact-driven leader whose expertise spans leading teams, architecting ML and data-intensive applications, and driving enterprise data strategy. Richa has worked for a Tier A Start-up developing feature platforms and in financial companies, leading ML Engineering teams to drive data-driven business decisions. Richa enjoys reading technical blogs focussed on system design and plays an active role in the MLOps Community. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.youtube.com/watch?v=i0To3DeHGuU
https://www.youtube.com/watch?v=tAOf2lVQUY4
https://www.youtube.com/watch?v=cXanVyaannQ
https://www.youtube.com/watch?v=2aWSsL24fv8 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Richa on LinkedIn: https://www.linkedin.com/in/richasachdev/ Timestamps: [00:00] Richa's preferred coffee [02:09] Takeaways [04:26] Richa's background to data [08:55] Prescriptive, Descriptive, and Predictive Data [11:50] Data Engineering Perspectives & Setup [17:34] Structured and Unstructured data [21:01] Richa's day-to-day at Chase [23:52] Figure out the business needs before the cool tech [26:46] Importance of business metrics [30:43] Optimizing end-user experience and trade-offs [36:06] Exhausting creativity in finding solutions [37:40] Consider faster implementation and increased ROI [40:20] Banks still using COBOL [41:17] Learning and growing as a versatile leader [42:04] Wrap up

MLOps podcast #183 with Ketan Umare, CEO of Union.AI, MLOps vs ML Orchestration co-hosted by Stephen Batifol. // Abstract Let's explore the relationship between Union and Flyte, emphasizing the significance of community-driven development and the challenge of balancing feature requests with security considerations. This conversation highlights the importance of real-time data and secure data handling in orchestrating machine learning models. The Flyte community's empathy and support for newcomers underscore the community's value in democratizing machine learning, making it more accessible and efficient for a broader audience. // Bio Ketan Umare is the CEO and co-founder at Union.ai. Previously he had multiple Senior roles at Lyft, Oracle, and Amazon ranging from Cloud, Distributed storage, Mapping (map-making), and machine-learning systems. He is passionate about building software that makes engineers' lives easier and provides simplified access to large-scale systems. Besides software, he is a proud father, and husband, and enjoys traveling and outdoor activities. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://union.ai/ Flyte: https://flyte.org/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Ketan on LinkedIn: https://www.linkedin.com/in/ketanumare/ Timestamps: [00:00] Ketan's preferred coffee [01:05] Takeaways [03:08] Please like, share, and subscribe to our MLOps channels! [03:15] Shout out to Ketan and UnionAI for sponsoring this episode! [04:23] Orchestration recent changes [07:51] Community with Flyte [11:26] ML orchestration [15:40] 50/50 is generous [20:06] Real-time ML [21:15] Over engineering without benefits [23:20] Balancing everything [27:40] Union verse Flyte [32:52] High value features of Union AI at the back of Flyte [40:18] Building LLM infrastructure [45:30] Traditional ML is the whole prompting [46:46] LLMs to evaluating prompts [48:55] Wrap up

MLOps podcast #182 with GetYourGuide's Jean Machado, DataScience Manager, Meghana Satish, MLOps Engineer, Olivia Houghton, Machine Learning Operations Engineer, Theodore Meynard, Data Science Manager, MLOps@GetYourGuide. // Abstract Join a team to talk about the journey of GYG with MLOps. From the conception of their platform to the creation of the MLOps engineer role and to their current stack state. // Bio Jean Machado Jean Carlo Machado is a DataScience Manager at GetYourGuide for the Growth Data Products team and the Machine Learning Platform Team. He is privileged to be able to work on turning ideas in data scinece from inception to production. Before GYG Jean was working in a startup in Brazil building its infrastructure from the ground up. Jean also likes community building and using technology for social good. Meghana Satish Meghana Satish is currently working as an MLOps Engineer at GetYourGuide. She has previously held positions at Amazon AWS in Berlin and Microsoft IT in Hyderabad. In addition to her career in technology, Meghana is also a talented singer, dancer, and yoga practitioner. Olivia Houghton Olivia has been working as an MLOps engineer at GetYourGuide for the past year and a half or so. Olivia's main work is in building and managing their activity ranking service. Theodore Meynard Theodore Meynard, Data Science Manager at GetYourGuide, leads the evolution of their ranking algorithm, enriching customer experiences. His hands-on journey from data scientist to leader has honed his expertise in MLOps and real-time ML. Beyond work, he's a co-organizer of PyData Berlin, underlining his commitment to community and collaborative learning. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://investing1012dot0.substack.com/ The Openness of AI report: https://research.contrary.com/reports/the-openness-of-ai ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jean on LinkedIn: https://www.linkedin.com/in/jean-carlo-machado-53b15977/ Connect with Meghana on LinkedIn: https://www.linkedin.com/in/meghana-satish-2a825282/?originalSubdomain=de Connect with Olivia on LinkedIn: https://www.linkedin.com/in/oliviaphoughton/ Connect with Theodore on LinkedIn: https://www.linkedin.com/in/theodore-meynard/

Timestamps: [00:00] GetYourGuide team's preferred coffee [00:55] Takeaways [02:20] Shout out to Berlin MLOps Community [02:38] Please like, share, and subscribe to our MLOps channels! [03:39] The GetYourGuide platform [05:45] GetYourGuide use cases [11:51] Strong Leadership Vision [13:59] Creating rituals [16:55] Feedback on the loop for improvements [18:35] Different components of GetYourGuide's ML Platform [21:04] V2 service templates [24:26] Biggest pain points [27:02] Feature flags [30:51] Data foundation [36:25] Data Testing [39:53] Cross-team Tool Adoption Process [44:59] Regrets about design decisions made in the past [47:53] What's next for the platform with LLMs? [52:49] Non-data scientists suggesting use cases, language flexibility [55:14] DevSecOps team's AI study group ideation [59:25] Experiments in growth data products, marketing split [1:01:47] Shout out to the Berlin MLOps Community! [1:03:31] Wrap up

MLOps podcast #181 with Kyle Harrison, General Partner at Contrary, The Centralization of Power in AI. // Abstract Kyle Harrison delves into the limitations imposed by language, underscoring how it can impede our grasp and manipulation of reality while stressing the critical need for improved language model performance for real-time applications. He further explores the perils of centralizing power in AI, with a specific focus on the "Openness of AI", where concerns about privacy are brought to the forefront, prompting his call for businesses to reconsider their reliance on it. The discussion also traverses the evolving landscape of AI, drawing comparisons between prominent machine learning frameworks such as TensorFlow and PyTorch. Notably, the episode underscores the vital role of open-source initiatives within the AI community and highlights the unexpected involvement of Meta in driving open-source development. // Bio Kyle Harrison is a General Partner at Contrary, where he leads Series A and growth-stage investing. He joined Contrary from Index where he was a Partner, and before that he was a growth investor at Coatue. His portfolio includes iconic startups and public companies including Ramp, Replit, Cohere, Snowflake, and Databricks. He also regularly shares his analysis on the venture capital landscape via his Substack Investing 101. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://investing1012dot0.substack.com/ The Openness of AI report: https://research.contrary.com/reports/the-openness-of-ai ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kyle-harrison-9274b278/ Timestamps: [00:00] Kyle's preferred beverage [00:20] Takeaways [03:52] Hype in technology space [09:20] Application Layer Revenue [14:44] Stability AI Lawsuit [18:08] Concern over concentration of power in AI [20:20] Transparency concerns [23:35] Open Source AI [25:57] To use or not to use Open AI [30:51] Lack of technical expertise and business-building capabilities [35:09] AI Transparency and Accountability [37:50] Traditional ML [41:47] Finding a unique approach [45:41] AGI limitations [47:43] Using Agents [49:46] Agents getting past demos [54:39] Tech Challenges & Hoverboard Dreams [58:04] Both AI hype and skepticism are foolish [01:27] Wrap up

MLOps podcast #180 with Sachin Abeywardana Deep Learning Engineer at Canva AI, Adventures in Building CLIP and Other (Largeish) Language Models sponsored by Prem AI. // Abstract Sachin takes us on an adventure, sharing insights on the pitfalls of not understanding the broader product and the importance of incorporating AI and machine learning capabilities. From the use of AI models to grammar correction and code generation to the fascinating Clip model and the challenges of balancing work and family life, this episode promises to be both informative and thought-provoking. // Bio Sachin is the father of two beautiful children. He completed his PhD in Bayesian Machine Learning at University of Sydney in 2015. In 2016 he discovered Deep Learning and hasn't looked back. He currently works as a Senior Machine Learning Engineer at Canva and is mainly focusing on NLP problems. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sachin Blogs: https://sachinruk.github.io/blog.html https://sachinruk.github.io/blog/ Graph ML link: http://web.stanford.edu/class/cs224w/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sachin on LinkedIn: https://www.linkedin.com/in/sachinabeywardana/ Timestamps: [00:00] Sachin's preferred beverage [00:26] Takeaways [02:30] Chat GPT user [05:58] Understanding on reliable Agents [08:10] Sachin's background [12:45] Staying at Deep Learning [16:17] Recommendation or Lead Scoring [17:36] Vector database [19:00] Sachin's blogs [23:26] The cap people [26:10] Pursuing business case [27:33] Canva [31:16] Incorporating AI and Machine Learning [32:17] Sponsor Ad [38:22] Eliminating unnecessary steps [39:00] Interacting with the product team [43:04] Criticisms on the current architecture limitations [45:58] Insufficient exploration of Transformers [47:42] Explaining GraphML [52:35] Fine-tuning ChatGPT2 [57:54] Leading ML Engineers and teams [59:40] Being practical with Math [1:05:52] Wrap up