MLOps.community

Bernie Wu is VP of Business Development for MemVerge. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // MLOps Podcast #270 with Bernie Wu, VP Strategic Partnerships/Business Development of MemVerge. // Abstract Limited memory capacity hinders the performance and potential of research and production environments utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. This discussion explores how leveraging industry-standard CXL memory can be configured as a secondary, composable memory tier to alleviate this constraint. We will highlight some recent work we’ve done in integrating of this novel class of memory into LLM/RAG/vector database frameworks and workflows. Disaggregated shared memory is envisioned to offer high performance, low latency caches for model/pipeline checkpoints of LLM models, KV caches during distributed inferencing, LORA adaptors, and in-process data for heterogeneous CPU/GPU workflows. We expect to showcase these types of use cases in the coming months. // Bio Bernie is VP of Strategic Partnerships/Business Development for MemVerge. His focus has been building partnerships in the AI/ML, Kubernetes, and CXL memory ecosystems. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. He is also on the Board of Directors for Cirrus Data Solutions. Bernie has a BS/MS in Engineering from UC Berkeley and an MBA from UCLA. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.memverge.com Accelerating Data Retrieval in Retrieval Augmentation Generation (RAG) Pipelines using CXL: https://memverge.com/accelerating-data-retrieval-in-rag-pipelines-using-cxl/ Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON: https://youtu.be/DxyOlRdCofo Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // MLOps Podcast #228: https://youtu.be/6MY-IgqiTpg Compute Express Link (CXL) FPGA IP: https://www.intel.com/content/www/us/en/products/details/fpga/intellectual-property/interface-protocols/cxl-ip.htmlUltra Ethernet Consortium: https://ultraethernet.org/ Unified Acceleration (UXL) Foundation: https://www.intel.com/content/www/us/en/developer/articles/news/unified-acceleration-uxl-foundation.html RoCE networks for distributed AI training at scale: https://engineering.fb.com/2024/08/05/data-center-engineering/roce-network-distributed-ai-training-at-scale/ --------------- ✌️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 Bernie on LinkedIn: https://www.linkedin.com/in/berniewu/ Timestamps: [00:00] Bernie's preferred coffee [00:11] Takeaways [01:37] First principles thinking focus [05:02] Memory Abundance Concept Discussion [06:45] Managing load spikes [09:38] GPU checkpointing challenges [16:29] Distributed memory problem solving [18:27] Composable and Virtual Memory [21:49] Interactive chat annotation [23:46] Memory elasticity in AI [27:33] GPU networking tests [29:12] GPU Scheduling workflow optimization [32:18] Kubernetes Extensions and Tools [37:14] GPU bottleneck analysis [42:04] Economical memory strategies [45:14] Elastic memory management strategies [47:57] Problem solving approach [50:15] AI infrastructure elasticity evolution [52:33] RDMA and RoCE explained [54:14] Wrap up

Gideon Mendels is the Chief Executive Officer at Comet, the leading solution for managing machine learning workflows. How to Systematically Test and Evaluate Your LLMs Apps // MLOps Podcast #269 with Gideon Mendels, CEO of Comet. // Abstract When building LLM Applications, Developers need to take a hybrid approach from both ML and SW Engineering best practices. They need to define eval metrics and track their entire experimentation to see what is and is not working. They also need to define comprehensive unit tests for their particular use-case so they can confidently check if their LLM App is ready to be deployed. // Bio Gideon Mendels is the CEO and co-founder of Comet, the leading solution for managing machine learning workflows from experimentation to production. He is a computer scientist, ML researcher and entrepreneur at his core. Before Comet, Gideon co-founded GroupWize, where they trained and deployed NLP models processing billions of chats. His journey with NLP and Speech Recognition models began at Columbia University and Google where he worked on hate speech and deception detection. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.comet.com/site/ All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s Opik by Comet: https://www.comet.com/site/products/opik/ --------------- ✌️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 Gideon on LinkedIn: https://www.linkedin.com/in/gideon-mendels/ Timestamps: [00:00] Gideon's preferred coffee [00:17] Takeaways [01:50] A huge shout-out to Comet ML for sponsoring this episode! [02:09] Please like, share, leave a review, and subscribe to our MLOps channels! [03:30] Evaluation metrics in AI [06:55] LLM Evaluation in Practice [10:57] LLM testing methodologies [16:56] LLM as a judge [18:53] OPIC track function overview [20:33] Tracking user response value [26:32] Exploring AI metrics integration [29:05] Experiment tracking and LLMs [34:27] Micro Macro collaboration in AI [38:20] RAG Pipeline Reproducibility Snapshot [40:15] Collaborative experiment tracking [45:29] Feature flags in CI/CD [48:55] Labeling challenges and solutions [54:31] LLM output quality alerts [56:32] Anomaly detection in model outputs [1:01:07] Wrap up

Raj Rikhy is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect and Salesforce. // MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft. // Abstract In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications. // Bio Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect and Salesforce. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/ --------------- ✌️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 Raj on LinkedIn: https://www.linkedin.com/in/rajrikhy/

//Abstract If there is one thing that is true, it is data is constantly changing. How can we keep up with these changes? How can we make sure that every stakeholder has visibility? How can we create a culture of understanding around data change management? //Bio - Benjamin Rogojan: Data Science And Engineering Consultant @ Seattle Data Guy - Chad Sanderson: CEO & Co-Founder @ Gable - Christophe Blefari: CTO & Co-founder @ NAO - Maggie Hays: Founding Community Product Manager, DataHub @ Acryl Data A big thank you to our Premium Sponsors  @Databricks ,  @tecton8241 , &  @onehouseHQ for their generous support!

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

Simba Khadder is the Founder & CEO of Featureform. He started his ML career in recommender systems where he architected a multi-modal personalization engine that powered 100s of millions of user’s experiences. Unpacking 3 Types of Feature Stores // MLOps Podcast #265 with Simba Khadder, Founder & CEO of Featureform. // Abstract Simba dives into how feature stores have evolved and how they now intersect with vector stores, especially in the world of machine learning and LLMs. He breaks down what embeddings are, how they power recommender systems, and why personalization is key to improving LLM prompts. Simba also sheds light on the difference between feature and vector stores, explaining how each plays its part in making ML workflows smoother. Plus, we get into the latest challenges and cool innovations happening in MLOps. // Bio Simba Khadder is the Founder & CEO of Featureform. After leaving Google, Simba founded his first company, TritonML. His startup grew quickly and Simba and his team built ML infrastructure that handled over 100M monthly active users. He instilled his learnings into Featureform’s virtual feature store. Featureform turns your existing infrastructure into a Feature Store. He’s also an avid surfer, a mixed martial artist, a published astrophysicist for his work on finding Planet 9, and he ran the SF marathon in basketball shoes. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: featureform.comBigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255: https://www.youtube.com/watch?v=NtDKbGyRHXQ&ab_channel=MLOps.community --------------- ✌️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 Simba on LinkedIn: https://www.linkedin.com/in/simba-k/ Timestamps: [00:00] Simba's preferred coffee [00:08] Takeaways [02:01] Coining the term 'Embedding' [07:10] Dual Tower Recommender System [10:06] Complexity vs Reliability in AI [12:39] Vector Stores and Feature Stores [17:56] Value of Data Scientists [20:27] Scalability vs Quick Solutions [23:07] MLOps vs LLMOps Debate [24:12] Feature Stores' current landscape [32:02] ML lifecycle challenges and tools [36:16] Feature Stores bundling impact [42:13] Feature Stores and BigQuery [47:42] Virtual vs Literal Feature Store [50:13] Hadoop Community Challenges [52:46] LLM data lifecycle challenges [56:30] Personalization in prompting usage [59:09] Contextualizing company variables [1:03:10] DSPy framework adoption insights [1:05:25] Wrap up

Stefano Bosisio is an accomplished MLOps Engineer with a solid background in Biomedical Engineering, focusing on cellular biology, genetics, and molecular simulations. Reinvent Yourself and Be Curious // MLOps Podcast #264 with Stefano Bosisio, MLOps Engineer at Synthesia. // Abstract This talk goes through Stefano's experience, to be an inspirational source for whoever wants to jump on a career in the MLOps sector. Moreover, Stefano will also introduce his MLOps Course on the MLOps community platform. // Bio Sai Bharath Gottam Stefano Bosisio is an MLOps Engineer, with a versatile background that ranges from biomedical engineering to computational chemistry and data science. Stefano got an MSc in biomedical engineering from the Polytechnic of Milan, focusing on cellular biology, genetics, and molecular simulations. Then, he landed in Scotland, in Edinburgh, to earn a PhD in chemistry from the University of Edinburgh, where he developed robust physical theories and simulation methods, to understand and unlock the drug discovery problem. After completing his PhD, Stefano transitioned into Data Science, where he began his career as a data scientist. His interest in machine learning engineering grew, leading him to specialize in building ML platforms that drive business success. Stefano's expertise bridges the gap between complex scientific research and practical machine learning applications, making him a key figure in the MLOps field. Bonus points beyond data: Stefano, as a proper Italian, loves cooking and (mainly) baking, playing the piano, crocheting and running half-marathons. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://medium.com/@stefanobosisio1First MLOps Stack Course: https://learn.mlops.community/courses/languages/your-first-mlops-stack/ --------------- ✌️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 Stefano on LinkedIn: https://www.linkedin.com/in/stefano-bosisio1/ Timestamps: [00:00] Stephano's preferred coffee [00:12] Takeaways [01:06] Stephano's MLOps Course [01:47] From Academia to AI Industry [09:10] Data science and platforms [16:53] Persistent MLOps challenges [21:23] Internal evangelization for success [24:21] Adapt communication skills to diverse individual needs [29:43] Key components of ML pipelines are essentia l[33:47] Create a generalizable AI training pipeline with Kubeflow [35:44] Consider cost-effective algorithms and deployment methods [39:02] Agree with dream platform; LLMs require simple microservice [42:48] Auto scaling: crucial, tricky, prone to issues [46:28] Auto-scaling issues with Apache Beam data pipelines [49:49] Guiding students through MLOps with practical experience [53:16] Bulletproof Problem Solving: Decision trees for problem analysis [55:03] Evaluate tools critically; appreciate educational opportunities [57:01] Wrap up

Global Feature Store: Optimizing Locally and Scaling Globally at Delivery Hero // MLOps Podcast #263 with Delivery Hero's Gottam Sai Bharath, Senior Machine Learning Engineer & Cole Bailey, ML Platform Engineering Manager. // Abstract Delivery Hero innovates locally within each department to develop MLOps practices most effective in that particular context. We also discuss our efforts to reduce redundancy and inefficiency across the company. Hear about our experiences in creating multiple micro feature stores within our departments, and our goal to unify these into a Global Feature Store that is more powerful when combined. // Bio Sai Bharath Gottam With a passion for translating complex technical concepts into practical solutions, Sai excels at making intricate topics accessible and engaging. As a Senior Machine Learning Engineer at Delivery Hero, Sai works on cutting-edge machine learning platforms that guarantee seamless delivery experiences. Always eager to share insights and innovations, Sai is committed to making technology understandable and enjoyable for all. Cole Bailey Bridging data science and production-grade software engineering. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.deliveryhero.com/ --------------- ✌️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 Sai on LinkedIn: https://www.linkedin.com/in/sai-bharath-gottam/ Connect with Cole on LinkedIn: www.linkedin.com/in/cole-bailey Timestamps: [00:00] Sai and Cole's preferred coffee [00:42] Takeaways [01:51] Please like, share, leave a review, and subscribe to our MLOps channels! [02:08] Life changes in Delivery Hero [05:21] Global Feature Store and Pandora [12:21] Tech integration strategies [20:08] Defining Feature and Feature Store [22:46] Feature Store vs Data Platform [26:26] Features are discoverable [32:56] Onboarding and Feature Testing [36:00] Data consistency [41:07] Future Vision Feature Store [44:17] Multi-cloud strategies [46:33] Wrap up

Adam Kamor is the Co-founder of Tonic, a company that specializes in creating mock data that preserves secure datasets. RAG Quality Starts with Data Quality // MLOps Podcast #262 with Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai. // Abstract Dive into what makes Retrieval-Augmented Generation (RAG) systems tick—and it all starts with the data. We’ll be talking with an expert in the field who knows exactly how to transform messy, unstructured enterprise data into high-quality fuel for RAG systems. Expect to learn the essentials of data prep, uncover the common challenges that can derail even the best-laid plans, and discover some insider tips on how to boost your RAG system’s performance. We’ll also touch on the critical aspects of data privacy and governance, ensuring your data stays secure while maximizing its utility. If you’re aiming to get the most out of your RAG systems or just curious about the behind-the-scenes work that makes them effective, this episode is packed with insights that can help you level up your game. // Bio Adam Kamor, PhD, is the Co-founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of unstructured data solutions that are transforming the work of fellow developers, analysts, and data engineers alike. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.tonic.ai Various topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/blog https://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-services https://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversation https://www.youtube.com/watch?v=5xdyt4oRONU https://www.tonic.ai/blog/what-is-retrieval-augmented-generation-the-benefits-of-implementing-rag-in-using-llms --------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/adam-kamor-85720b48/ Timestamps: [00:00] Adam's preferred coffee [00:24] Takeaways [00:59] Huge shout out to Tonic.ai for supporting the community! [01:03] Please like, share, leave a review, and subscribe to our MLOps channels! [01:18] Naming a product [03:38] Tonic Textual [08:00] Managing PII and Data Safety [10:16] Chunking strategies for context [14:19] Data prep for RAG [17:20] Data quality in AI systems [20:58] Data integrity in PDFs [27:12] Ensuring chatbot data freshness [33:02] Managed PostgreSQL and Vector DB [34:49] RBAC database vs file access [37:35] Slack AI data leakage solutions [42:26] Hot swapping [46:06] LLM security concerns [47:03] Privacy management best practices [49:02] Chatbot design patterns [50:39] RAG growth and impact [52:40] Retrieval Evaluation best practices [59:20] Wrap up

Jonathan Rioux is a Managing Principal of AI Consulting for EPAM Systems, where he advises clients on how to get from idea to realized AI products with the minimum of fuss and friction. Who's MLOps for Anyway? // MLOps Podcast #261 with Jonathan Rioux, Managing Principal, AI Consulting at EPAM Systems. // Abstract The year is 2024 and we are all staring into the cliff towards the abyss of disillusionment for Generative AI. Every organization, developer, and AI-adjacent individual is now talking about "making AI real" and "turning a ROI on AI initiatives". MLOps and LLMOps are taking the stage as the solution; equip your AI teams with the best tools money can buy, grab tokens by the fistful, and look at value raking in. Sounds familiar and eerily similar to the previous ML hype cycles? From solo devs to large organizations, how can we avoid the same pitfalls as last time and get out of the endless hamster wheel? // Bio Jonathan is a Managing Principal of AI Consulting for EPAM, where he advises client on how to get from idea to realized AI products with the minimum of fuss and friction. He's obsessed with the mental models of ML and how to organize harmonious AI practices. Jonathan published "Data Analysis with Python and PySpark" (Manning, 2022). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: raiks.ca --------------- ✌️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 Jonathan on LinkedIn: https://www.linkedin.com/in/jonathanrx/ Timestamps: [00:00] Jonathan's preferred coffee [00:25] Takeaways [01:44] MLOps as not being sexy [03:49] Do not conflate MLOps with ROI [06:21] ML Certification Business Idea [11:02] AI Adoption Missteps [15:40] Slack AI Privacy Risks [18:17] Decentralized AI success [22:00] Michelangelo Hub-Spoke Model [27:45] Engineering tools for everyone [33:38 - 35:20] SAS Ad [35:21] POC to ROI transition [42:08] Repurposing project learnings [46:24] Balancing Innovation and ROI [55:35] Using classification model [1:00:24] Chatbot evolution comparison [1:01:20] Balancing Automation and Trust [1:06:30] Manual to AI transition [1:09:57] Wrap up

Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw, where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when is fine-tuning necessary over prompting and how we have created a loop of sampling - collecting feedback - fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.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 Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up

Vikram Rangnekar is an open-source software developer focused on simplifying LLM integration. He created LLMClient, a TypeScript library inspired by Stanford's DSP paper. With years of experience building complex LLM workflows, he previously worked as a senior software engineer at LinkedIn on Ad Serving. Ax a New Way to Build Complex Workflows with LLMs // MLOps Podcast #259 with Vikram Rangnekar, Software Engineer at Stealth. // Abstract Ax is a new way to build complex workflows with LLMs. It's a typescript library based on research done in the Stanford DSP paper. Concepts such as prompt signatures, prompt tuning, and composable prompts help you build RAG and agent-powered ideas that have till now been hard to build and maintain. Ax is designed for production usage. // Bio Vikram builds open-source software. Currently working on making it easy to build with LLMs. Created Ax a typescript library that abstracts over all the complexity of LLMs, it is based on the research done in the Stanford DSP paper. Worked extensively with LLMs over the last few years to build complex workflows. Previously worked as a senior software engineer with LinkedIn on Ad Serving. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links The unofficial DSPy framework. Build LLM-powered Agents and "Agentic workflows" based on the Stanford DSP paper: https://axllm.dev All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s --------------- ✌️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 Vikram on LinkedIn: https://www.linkedin.com/in/vikramr Timestamps: [00:00] Vikram preferred coffee [00:41] Takeaways [01:05] Data Engineering for AI/ML Conference Ad [01:41] Vikram's work these days [04:54] Fine-tuned Model insights [06:22] Java Script tool evolution [16:14] DSP knowledge distillation [17:34] DSP vs Manual examples [22:53] Optimizing task context [27:58] API type validation explained [30:25] LLM value and innovation [34:22] Navigating complex systems [37:30] DSP code generators explained [40:56] Exploring LLM personas [42:45] Optimizing small agents [43:32] Complex task assistance [49:53] Wrap up

MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Building in Production Human-centred GenAI Solutions sponsored by QuantumBlack, AI by McKinsey. // Abstract Trust is paramount in the adoption of new technologies, especially in the realm of education. Mohamed and Mara shed light on the importance of AI governance programs and establishing AI governance boards to ensure safe and ethical use of technology while managing associated risks. They discuss the impact on customers, potential risks, and mitigation strategies that organizations must consider to protect their brand reputation and comply with regulations. // Bio Mara Pometti Mara is an Associate Design Director at McKinsey & Company, where she helps organisations drive AI adoption through human-centered methods. She defines herself as a data-savvy humanist. Her practice spans across AI, data journalism, and design with the overarching objective of finding the strategic intersection between AI models and human intents to implement responsible AI systems that move organisations forward. Previously, she led the AI Strategy practice at IBM, where she also developed the company’s first-ever data storytelling program. Yet, by background, she is a data journalist. She worked as a data journalist for agencies and newsrooms like Aljazeera. Mara lectured at many universities about how to humanize AI, including the London School of Economics. Her books and writing explore how to weave a humanistic approach to AI development. Mohamed Abusaid Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture. Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links QuantumBlack, AI by McKinsey: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients --------------- ✌️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 Mara on LinkedIn: https://www.linkedin.com/in/mara-pometti Connect with Mohamed on LinkedIn: https://www.linkedin.com/in/mabusaid/

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 ManufacturingInteractive ML Data VisualizationML 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/