Timothy L.

Delivering AI-ready data is harder than ever — too many tools, manual code, and pipelines that don’t scale. Join Databricks’ Bilal Aslam and Simon Whiteley, CTO at Advancing Analytics, to see how a unified, declarative platform helps you: - Replace repetitive code with tested pipelines - Support batch, streaming, and unstructured data - Monitor pipeline health with built-in quality checks, schema evolution, and error handling You’ll leave with a clear plan to simplify your stack, scale your workflows, and deliver reliable data for AI: https://lnkd.in/g2U4gW9h

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Meta’s Llama 4 Large Language Models are now available in Snowflake Cortex AI! Llama 4 introduces Meta’s first Mixture-of-Expert (MoE) architecture for faster, more efficient inference, helping customers build high-performing enterprise gen AI apps and deliver personalized experiences inside Snowflake. Now available: - Llama 4 Maverick: A powerful, natively multimodal model, built for efficient single-host inference. - Llama 4 Scout: A fast multimodal model with a 10 million token context window, optimized to run on a single GPU. With Llama 4 models in Cortex AI, customers can access leading-edge AI to build intelligent apps and data agents directly in Snowflake. Learn more: https://lnkd.in/g4t7UGB8

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What if you could just ask your Kafka topic a question and get the answer in plain language? Many orgs rely on Apache Kafka® for real-time data, but not everyone is fluent in Flink SQL. That’s where AI assistants come in. In this demo, see how tools like Cursor and the Model Control Protocol (MCP) make it possible to ask natural language questions and get real answers directly from your Kafka topics on Confluent Cloud. Read the blog -> https://cnfl.io/43rXWb2 Ready to try it now? Sign up for Confluent Cloud (Use code SOCIALFAM25X for an extra $50 dollars off) -> https://cnfl.io/4kmLCyH

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[Demo] See how Databricks makes Scala development faster and simpler with Serverless JAR Tasks and the Beta of Serverless Scala Databricks Connect. This end-to-end demo walks through building, deploying, and running JAR jobs on Serverless compute using Databricks Asset Bundles, with 5 to 10 second startup times, structured streaming support, and no infrastructure to manage: https://lnkd.in/g7uqRFPD

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Finding it hard to turn a machine learning idea into a production-ready application? At the upcoming #SnowflakeBUILD, Senior Developer Advocate Chanin Nantasenamat will show you how to build your first end-to-end machine learning project from scratch. In this hands-on lab, you’ll learn to build the entire ML lifecycle from absolute zero, built entirely on Snowflake: ‣ Data Preparation: Prepare a fun multi-modal bear dataset, working with both structured and unstructured data. ‣ Build and optimize ML models  ‣ Deploy and serve ML model in an interactive data app with Streamlit ✍️ Register today: https://lnkd.in/gGMyByjw

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📣 PSA for those that live under a rock: Apache Airflow 3 is now available! Vikram Koka and Oliver Dykstra from Astro customer Texas Rangers Baseball Club spoke with VentureBeat about everything new in Airflow 3, and why Airflow is the industry-standard for data orchestration and unified DataOps. Read the full article: https://bit.ly/42JtoQC #ApacheAirflow3 #Airflow3 #DataOrchestration #DataOps #DataEngineering

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ML Roles - Niching down or widening in scope? The ML Market is booming, and moving at such a pace that there seems to be a growing inconsistency in terms of title and expectations. I’m seeing ever more niche roles: GNN Engineer, Edge AI Engineer, RL Researcher and more. On the flip side, there also seems to be a bit of a trend in creating more holistic roles the with rise of roles such as Solutions Engineer. Now, having recruited for some of these myself, I certainly see the attraction. Candidates bring: • Cross-disciplinary expertise  • Customer-centric mindset  • Experience in CI/CD and Deployment  • Broad AI/ML adaptability These engineers are not only useful to myriad business areas, they’re also less welded to the specifics, offering a more malleable options for businesses either foraying into AI, working with a strong customer base needing bespoke solutions, or an AI generalist who can cover multiple areas at a decent level. In a rapidly evolving ML landscape, is it better to specialise deeply in a niche area, or develop broad, adaptable expertise that can bridge multiple disciplines?

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Databricks has recently announced the launch of its AI Accelerator Program, a strategic initiative designed to support early-stage AI startups utilising its advanced data platform. This program offers up to $250,000 in funding, valuable platform credits and dedicated mentorship tailored to accelerate innovation and growth in the AI ecosystem. By leveraging its extensive customer and partner network, Databricks aims to empower the next generation of AI companies, helping them transition from concept to market readiness with greater efficiency. This development underscores Databricks’ commitment to driving cutting-edge advancement in artificial intelligence and data-driven innovation. Well worth a read of the Forbes article highlighting this significant step forward in the AI startup landscape: https://lnkd.in/dbeMh2Kt #Databricks #AIAccelerator #Innovation #StartupSupport #DataScience #AspireTechnology

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🔄 Building fault-tolerant systems doesn't have to be complicated! Watch how Alex from Temporal Technologies demonstrates building reliable distributed systems that can: • Handle worker failures gracefully • Manage complex error scenarios • Implement human-in-the-loop workflows • Maintain state across system failures Learn how to leverage Temporal's workflow management framework to build resilient applications that scale. Perfect for developers working on distributed systems, e-commerce platforms, or any complex business processes. Watch now to see how you can reduce development cycles and improve system reliability! Full video 👉 https://go.aws/43dAezp #AWS #CloudNative #OpenSource

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You ever brick a game with one line of data? Carly Taylor, MSc has. And she remembers. On this episode of Chain of Thought, Carly—Field CTO, Gaming at Databricks—joins Conor Bronsdon to talk about what happens when data breaks your game… and your sleep schedule. “I’ve broken games before with data updates. One time I flipped a greater than / less than symbol. That was a fun one.” QA isn’t just about code. A mistyped condition in a live patch can cascade across systems—and in games, that means angry players and overnight fire drills.

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How well do multimodal LLMs really perform at extracting key insights from documents? From open-source models like Llava-NeXT to API models like GPT-4o and Gemini 1.5 Pro, Quantiphi's Research and Development team put them to the test using real-world datasets across three unique approaches to Key Information Extraction (KIE). Which worked best? Which models stood out? Explore the findings in our latest blog that examines the capabilities of multimodal LLMs in Key Information Extraction (KIE): https://lnkd.in/d8eDHtSu #Quantiphi #ResearchAndDevelopment #SolvingWhatMatters #MultimodalLLMs #AIInsights #AIInnovation

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We're joining with Databricks and Women in Data® to help close the gender health gap with GenAI - find out how 👇 Kubrick Associate Director Mirela Gyurova set out to see if Generative AI could help uncover bias in Lupus research - a condition of which 90% of patients are women.   What she found was a way to expedite research for underfunded areas of medical research while improving the governance of critical human decision-making.   In this webinar, Mirela takes us under the hood of her GenAI tool, exploring the challenges of LLM training, stringency, and best practice to help mitigate biases, rather than exacerbate them.   Learn how her Multi-Class Classification model leverages AI self-reflection, accuracy measurement, and human input to uncover a dramatic disparity in bias detection and mitigation in health research. Register for your place: https://bit.ly/3JGzmfb #AI #GenerativeAI #Webinar #WomeninData #WomensHealth #WomensHealthGap #AIforGood

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📣 New Blog Alert! 📣 Ever wished your data infrastructure could just manage itself? With Databricks Serverless, it practically does. Say goodbye to scaling headaches, costly idle time, and the complexity of managing clusters. Want to know how it works and what it could mean for your workflows? Mo Uddin dives deep into the ins and outs of Serverless in our latest blog! Want to know more? Read the blog now 👇 https://hubs.la/Q037MQGF0 #DataAnalytics #DataEngineering #Databricks #BigData

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