--- title: "Data Mesh, Agents, and EdTech - A Playbook for Your Analytics Arsenal" newsletter: "MLOps Community" date: 2025-01-16 source: https://aaif.live/newsletters/mlopscommunity/2025-01-16-data-mesh-agents-and-edtech-a-playbook-for-your-analytics-ar --- # Data Mesh, Agents, and EdTech - A Playbook for Your Analytics Arsenal *Plus, AI agents for recsys, pipeline orchestration tools compared, upcoming Reading Group, hidden gems, Slack spotlight, and the sandbox.* *MLOps Community — Agentic AI Foundation, 2025-01-16* https://go.mlops.community/b1tnai To help make sure Keir’s getting the good stuff, we’re running a State of AI survey [https://go.mlops.community/s5ctbn] Tell us how you’re using AI, the skills you rely on, your data preparation practices, and your thoughts on its future. Not only will you be helping Keir, but you’ll also be among the first to access the insights when the survey wraps up. ## Real World AI Agent Stories Real World AI Agent Stories Zach Wallace // Engineering Manager @ Nearpod There’s a legitimate chance to use the cliché “game-changer” for this episode, as Zach uses fantasy football as an illustration for agents in other contexts. It’s not all sport though, as he shares how Nearpod transformed its data architecture, unifying 20+ disparate sources, including DynamoDB and Aurora, into a scalable system. Leveraging DBT Core for transformations, Redshift for batch ELT, and a data product exchange framework (akin to a data mesh), his team built a foundation for consistent, reliable analytics while advancing domain-driven design. The conversation shifts to agent-driven architectures for LLMs, which Zach calls the natural evolution. Automating question generation in EdTech stood out as an example: * Multi-agent orchestration: Domain-specific agents tackled tasks like input validation and question creation, avoiding "jack-of-all-trades" pitfalls. * Efficiency-focused design: Narrowing agent scopes reduced token usage, improving speed and cutting costs. * Rapid prototyping: Proof-of-concepts were built in hours, fostering faster iterations and stronger cross-department collaboration. This episode’s a great one to have in your Arsenal! Video [https://go.mlops.community/w8cazx] || Spotify [https://go.mlops.community/hy22eo] || Apple [https://go.mlops.community/w4nfu3] ## AI Agents Are Revolutionizing E-Commerce AI Agents Are Revolutionizing E-Commerce Nishikant Dhanuka // Director of ML @ Prosus Group Beatriz Ferreira // Senior Data Scientist @ OLX ## Hidden Gems ## MLE-bench: Evaluating ML Agents on ML Engineering MLE-bench: Evaluating ML Agents on ML Engineering MLOps Community Reading Group Join us for the first reading Group of 2025 where we'll be discussing MLE-bench: Evaluating ML Agents on ML Engineering [https://go.mlops.community/39m8ej]. Our moderators will guide the chat, covering key concepts before opening the floor for a lively round-robin exchange of ideas. All happening Thursday, January 23, 11am -12pm ET Register here [https://go.mlops.community/ReadingGroupRegistration] [MLE-bench: Evaluating ML Agents on ML Engineering](https://go.mlops.community/39m8ej) ## ZenML VS Flyte VS Metaflow ZenML VS Flyte VS Metaflow With thanks to Ankur Tyagi for their contribution. Are you zen, flying high, or do you go with the flow? We're talking ML pipeline orchestration tools, as this blog takes a look at ZenML, Flyte, and Metaflow, examining their design, features, and real-world applications. It breaks down how they handle key aspects like scalability, reproducibility, and integration. Each tool is evaluated on its strengths in managing complex workflows, automating tasks, and ensuring consistency across environments. Some of the areas covered include: * How task orchestration and dependency management differ across the tools. * Ways each tool supports reproducibility, from versioning to artifact tracking. * How integration capabilities can influence tool selection based on your existing setup. The blog wraps with practical tips for choosing the right tool based on workflow complexity, infrastructure, and team experience. Have a read, and whichever tool you use, you’ll be orchestrating like a maestro in no time. Read it here [https://go.mlops.community/xhjuix] ## Hidden Gems ## All You Ever Wanted // // All You Ever Wanted // Gem [https://go.mlops.community/2r7cyn] // Song [https://go.mlops.community/kyckwx] The LinkedIn post referenced above, highlighting advancements in LLM-driven recommendation systems, covering personalization methods, multi-modal fusion, and fine-tuning techniques for product recall, touching on Meta’s “Preference Discerning” and challenges in real-time updates from Bytedance. Missing Link // Gem [https://go.mlops.community/wbv3q6] // Song [https://go.mlops.community/uakfcm] A blog exploring the evolution of AI agents, their current capabilities, and real-world challenges. It covers key advancements in agent architectures and reasoning techniques while addressing unresolved issues like reliability and autonomy. Don’t Look Back In Anger // Gem [https://go.mlops.community/kgqxr9] // Song [https://go.mlops.community/a0rrx9] A retrospective on key trends in databases, covering new database technologies, major advancements in cloud-native architectures, and shifts in industry adoption patterns, highlighting significant open-source developments and emerging challenges, particularly around scaling and performance. We Can Work It Out // Gem [https://go.mlops.community/rp5blh] // Song [https://go.mlops.community/owaxsl] A blog post discussing how AI has yet to significantly enhance productivity for engineers in large organizations, highlighting the need for intelligent repository tools to manage complex codebases, automate routine tasks, and improve developer workflows at scale. ## Slack Spotlights Slack Spotlights Stephen Oladele [https://go.mlops.community/733wfg] shares some of the chat you might have missed ## Nick Warwick In case you missed it, Nick Warwick started an engaging thread [https://go.mlops.community/yo9a23] last week in #mlops-questions-answered [https://go.mlops.community/tn6w3k] about refining semantic search in his RAG implementation. He’s working on improving how relevant context is fetched based on user queries, but has noticed unwanted results even after restricting similarity values. He shared his ideas for refining the process: 1. Using an LLM to refine user questions. 2. Implementing an LLM-based review of the semantic search results for an extra layer of relevance. 3. Improving embeddings by adding more contextual information. 4. Increasing vector dimensions. Here’s how the community helped: * One suggestion was adding a re-ranking step post-similarity search and emphasized the importance of measuring retrieval precision/recall before applying changes blindly for better optimization. Someone kindly shared this Pinecone guide [https://go.mlops.community/e5ovf1] for more details. * Another recommendation was oversampling combined with low-bit quantization to maintain accuracy while reducing memory usage, along with resources from ElasticSearch on dense vector optimization [https://go.mlops.community/3tgzdn] and semantic reranking [https://go.mlops.community/giro3u]. * Someone mentioned improving retrieval accuracy by embedding smaller chunks of text (6–10 lines) instead of entire pages. * One memberadvised visualizing query and document embeddings to spot issues and identify potential areas for improvement. They also suggested rewriting queries to ensure accurate context before embedding them. The discussion was a great example of how our community collaborates to solve complex MLOps challenges. Got similar challenges or insights to share on how to improve Nick’s RAG workflow? Join the conversation in Slack [https://go.mlops.community/yo9a23] and contribute your own experiences! ## Job of the Week [https://go.mlops.community/9p6ake](https://go.mlops.community/9p6ake) ## The Sandbox The Sandbox A little place to test some ideas Trying out a few things here - let us know what you think here [https://go.mlops.community/hx6agg] or email steve@mlops.community Back to the Feature A highlight from last week Robert’s take on building ontology-free graphs sparked interest, showing how flexible, schema-less graphs simplify relationships and improve efficiency. We explored ephemeral graph instances, fine-tuned models enhancing entity extraction, and Ask News’ real-time AI-driven knowledge graphs for richer storytelling. Video [https://go.mlops.community/shikl7] || Spotify [https://go.mlops.community/d4g6c0] || Apple [https://go.mlops.community/ipp7tf] Tech Teaser A mini MLOps mindbender It's about this time of the month New Year resolutions are broken, so you make an AI for pizza delivery that dynamically predicts arrival times. Each additional delivery area adds 4 features per input and doubles the number of training examples. What happens to the computational cost of a single training iteration? Click here [https://go.mlops.community/TechTeasers] for the answer. Interested in partnering with us? Get in touch: partners@mlops.community Thanks for reading. See you in Slack [https://go.mlops.community/slack], YouTube [https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ?view_as=subscriber], and podcast [https://home.mlops.community/public/content/] land. Oh yeah, and we are also on X [https://twitter.com/mlopscommunity] and LinkedIn [https://go.mlops.community/linkedin]. The MLOps Community newsletter is edited by Jessica Rudd [https://www.linkedin.com/in/jmrudd/]. --- Source: https://aaif.live/newsletters/mlopscommunity/2025-01-16-data-mesh-agents-and-edtech-a-playbook-for-your-analytics-ar