“I worked with Kamal for an ambitious project for Visio and Power BI needing collaboration across three geographies. Kamal instantly recognized the unique value proposition that the scenario would bring to both the products and rallied the entire team along. Despite challenges, Kamal kept pushing the team across functions and helped in enabling uniquely positioned scenarios to the customers. Kamal is a natural leader. He has great capability to inspire team members and lead them to success. He is able to spot the customer needs and direct the team to work on the right priorities. His sense of business, customer needs and prioritization make product extremely useful to customers. He is strongly collaborative and makes the partner team really a part of the decision making and helps remove obstacles out of the way to build one team across boundaries. He is open towards possibilities and ensures that things happen the way they should be. It was certainly a privilege and great learning experience working with him. ”
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- http://kamalh.smugmug.com
- Photgraphs
- http://www.pbase.com/kamalh
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Tom Christian
Open has become the state-of-the-art (for now), Meta have changed the game - yet again - with the release of the Llama3.1 suite of models. The acceleration in this industry is breathtaking. The headlines: * Llama 3.1 models are immediately available in Cortex today. ✅ * 405B packs a punch - out performing other leading flagship models across a variety of benchmarks. 📈 * 8b & 70b models have been updated too. The already capable models have seen significant jumps, particularly in math and coding, in just 3 months. 🚀 * Meta's 3.1 suite will be made available for serverless fine tuning. 🤯 * We've also released Snowflake Cortex Guard (GA) to safeguard LLM applications. 🛡
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Brad Menezes
This week, Snowflake CEO Sridhar Ramaswamy gave the opening keynote at the Snowflake Data Summit 2024 in San Francisco featuring Jensen Huang. They shared a vision for “The era of enterprise AI”. 3 Takeaways 1) Snowflake announced a partnership where NVIDIA software will be able to be run in Snowflake environments. As Jensen said "bringing the best computing power to your data, instead of transferring your data to our compute engines". This will work in tandem with Snowflake's Arctic LLMs. 2) As data grows increasingly complex, keeping AI tools simple becomes more and more paramount to ensure rapid enterprise adoption. Launches like Snowflake Cortex AI Analyst, enable business users to interact with structured data using natural language to find answers faster, self-serve insights and save time. 3) The number of AI use cases is large and seem to be highly industry specific in the enterprise across both internal applications and customer facing experiences. Anu Jain, Global Head of Enterprise Data at J.P. Morgan is using AI to inform to give their frontline teams the tools to anticipate what products their customers are likely to need next. Caitlin Halferty, Global CDO at Ericsson is using AI to model sites their team would work on and determine hazards, costs, and how many workers to send. Thomas Davey, CDO at Booking.com is using AI to reinvent the booking journey with intelligent AI suggestions and itineraries for customers based on hyper-personalization.
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James Hall
This week marked a watershed moment for Snowflake: We launched #SnowflakeArctic, a state-of-the-art large language model uniquely designed to be the most open, enterprise-grade LLM on the market. Arctic not only outperforms many leading open models, but we brought it to market at one-eighth of the cost and in less than three months compared to other models. Arctic will significantly enhance our ability to deliver reliable, efficient AI to our customers. By delivering industry-leading intelligence and efficiency in a truly open way to the AI community, we are furthering the frontiers of what open-source AI can do. https://lnkd.in/eueT5Wcs
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Darren Haas
Thank you Databricks for hosting our folks at the Data+AI Summit. The team has been so excited to attend. The topics shared at the event reminded me of a Databricks article from 2021 that celebrated the progress of Arrow: “Apache Arrow has recently become the de-facto standard for columnar in-memory data analytics, and is already adopted by a plethora of open-source projects.” (Read it here: https://lnkd.in/gUEcsgRZ) Arrow recently crossed the 1 BILLION annual download mark. Voltron Data is the leading maintainer and contributor to the Arrow project, and numerous other open standards, and we are incredibly proud to see this. Arrow is actively used for building new integrations with #GPU acceleration that are driving forces for Machine Learning Pre-Processing and Analytics at scale, thanks to the amazing foundation of RAPIDS.ai. Our query engine, Theseus, is built with Arrow and RAPIDS and is a prime example of how powerful tools built with Open Standards are. Theseus can process 100TB queries in SECONDS. Check out the numbers in our benchmarking report: https://lnkd.in/gMKbxcrU
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Jeff Catlin
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David Armano
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Mo Sarwat
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Lucas Dickey
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Marco Diciolla
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Lalith Suresh
I've been learning a lot from talking to customers about the current state of data engineering. And I can't help but feel that enterprises have long been implementing incremental computation manually, they just didn't know it. Here's a post I wrote to discuss this and the historical context within which I view this problem. As a side note, expect a sprinkling of sci-fi references in all my posts going forward. (Maybe I should turn this into a puzzle hunt?)
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