Umair Mesiya

Join PyTorch at Snowflake's #DataCloudDevDay on June 6 in San Francisco to learn how to accelerate GenAI performance with Native PyTorch. Register: https://hubs.la/Q02xQStt0

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🧱 Databricks VS ❄️ Snowflake GTM strategies My friend Jason Saltzman from Live Data Technologies shared some great insights into the hiring patterns of two AI giants—Databricks and Snowflake. Here’s the TL;DR: Databricks has ~2x more eng talent compared to Snowflake. Snowflake has ~2x more sales reps compared to Databricks. This highlights two very distinct GTM strategies: 1) Snowflake prioritizes rapid sales team expansion to dominate market share. Their strategy is built around scaling their sales force in response to regional demand signals—a “feet on the ground” philosophy common in competitive markets. This approach allows them to capture opportunities with speed and precision, particularly in high-demand areas. 2) Databricks has focused on community-driven strategy and delivering exceptional product value. Their free, forever-accessible Community Edition lowers barriers to entry, making it easy for users to experience their platform. A self-service trial model lets anyone start using Databricks with just a credit card—no sales rep required. They capitalize on community endorsements to influence key decision-makers, like CIOs, which dramatically shortens their sales cycle. They believe that product quality can handle much of the selling, which is why they hire significantly more AI/ML talent compared to their competitors. Same industry. Totally different GTM strategies. What’s the takeaway? Your team should reflect your strategy. If you're building a GTM team make sure the profiles you are looking at have had success in similar GTM motions. Your sales leader should have experience with a similar approach—they’ll bring the insights and instincts needed to succeed without a long ramp time. And another thing these two companies excel at? Hiring customer-facing talent with strong technical acumen. I've said this a billion times but: you need to hire GTM talent that has relevant technical acumen. You can't sell what you can't teach. I’ve written about these two GTM strategies before. Check it out here: Snowflake: https://lnkd.in/g6j3QdQR Databricks: https://lnkd.in/gH2y_gkF And make sure you check Jason's post here: https://lnkd.in/gyWud5Yh Super cool stuff!!!! If you're hiring GTM in Data, AI/ML, HPC/ Compute, Cloud Infra - this is our jam. DM me if you need help

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Snowflake and Meta Team Up to Host and Optimize New Flagship Model Family in Snowflake Cortex AI “Snowflake’s world-class AI Research Team is blazing a trail for how enterprises and the open source community can harness state-of-the-art open models like Llama 3.1 405B for inference and fine-tuning in a way that maximizes efficiency,” said Vivek Raghunathan, VP of AI Engineering, Snowflake. “We’re not just bringing Meta’s cutting-edge models directly to our customers through Snowflake Cortex AI. We’re arming enterprises and the AI community with new research and open source code that supports 128K context windows, multi-node inference, pipeline parallelism, 8-bit floating point quantization, and more to advance AI for the broader ecosystem.” Dave Lindley E15 Group Ryan K. Hakkōda Matthew Scullion Matillion Kevin Niparko Twilio Denise Persson Barbara Lewis https://lnkd.in/ehYGAG7N #fintech #finance #banking #paytech #payments #fintechnews #paymentsnews

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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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A great read in iTnews today highlighting how Seven West Media leveraged Databricks to forecast audience view patterns on both a seven-day and 28-day horizon. The company is now scaling its use of Databricks to support additional analytics use cases. Andrew Brain, Audience Intelligence and Growth Director, emphasized the significance of this advancement, stating, "We can now understand what our audience is going to be from a value perspective on volume and also what they’re going to be watching." This insight is pivotal for their business. #databricks #ai Read more about Seven West Media's predictive analytics success here: https://lnkd.in/e4rb3_mp

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📣 At Databricks, we want to help customers build more #inference-friendly #llms. Our MixAttention architecture demonstrates that reductions in KV cache size make it possible to maintain model quality while improving inference speed and reducing memory footprint. Read our latest blog post to learn more: https://lnkd.in/gcHxM4DX

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Databricks has acquired Tabular to increase its AI offerings. The deal is estimated to be between $1 billion to $2 billion. Tabular is a storage platform developed by the creators of Apache Iceberg Ryan Blue, CEO and co-founder of Tabular, stated, "With Tabular joining Databricks, we intend to build the best data management platform based on open lakehouse formats so that companies don’t have to worry about picking the ‘right’ format or getting locked into proprietary data formats." Read more: https://lnkd.in/e_6-UBbc #ai #Databricks

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💡 How is #GenAI transforming businesses right now? In partnership with Databricks, we surveyed 400 senior AI professionals to examine GenAI’s deployment across industries, tracking its impact, investment strategies, ROI, and year-over-year trends. Authored by Databricks experts Maria Pere-Perez and Riley Maris, this blog reveals 10 critical insights every executive must consider, from setting strategic priorities to tackling challenges like #DataQuality and scaling, showing how top organizations are turning GenAI into a powerful engine for innovation and efficiency. 👉 Stay ahead of the curve — unlock the full insights here: | https://bit.ly/4eRL6WR |

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Read this great story from hipages Group on how they overcame challenges including legacy data systems slowing productivity and insight, requiring days to transition data between multiple warehouses. Partnering with Databricks hipages overcame these hurdles by transforming their data architecture into a single lakehouse architecture, enhancing both speed and reliability.  “We were able to tackle all our platform, productivity and insights challenges by migrating to a single platform, Databricks. Our architecture is more streamlined and our data users are more autonomous.” — Minas Kamel, Senior Product Manager, hipages #data #ai #lakehouse https://lnkd.in/gfXSqNSy

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The data platform space is growing rapidly, but how companies choose to scale is what truly sets them apart. At Snowflake, our focus has always been on creating sustainable growth that empowers innovation, supports our customers, and positions us for long-term success. With strong free cash flow and impressive revenue per rep, Snowflake is positioned to continue leading the way as businesses increasingly seek out platforms that not only scale but also deliver consistent value. As we look to the future, I’m excited about: - How we can use our financial strength to accelerate innovation in data, AI, and analytics. - Continuing to deliver impactful solutions that help organizations drive data-driven decision making. - Building on a foundation that allows us to remain agile and customer-focused in a rapidly changing market. The next chapter for Snowflake looks bright, and I’m eager to be part of a company that’s not just growing—but growing smarter. What excites you most about the future of data and cloud platforms? #CloudData #AI #DataInnovation #Snowflake #GrowthStrategy #TheDataCloud #FutureOfWork

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Observe natively integrates with Snowflake Trail and continuously collects telemetry data. Observe for Snowflake provides complete visibility into the performance of Snowflake Data Cloud, UDFs, and Snowpark Container Services apps. In episode ten of of our #observability trends webinar series, Kyle Champlin and Daniel Odievich will explore how you can maximize performance and achieve compliance readiness in Snowflake with Observe. Register now: https://lnkd.in/gftXxx_4 (P.S. - If you can’t attend on the day, register anyway and we’ll send you the recording.)

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We’re proud to be a Leader in the Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms! Thank you to our customers and partners for supporting our journey. Read the full report to learn more here | https://lnkd.in/eQDQxthK #AI #DataScience #MachineLearning #GenerativeAI

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Today, Snowflake launched #SnowflakeArctic, our first foundation #LLM built for the Enterprise use case. Snowflake eschewed the "bigger = better" mantra currently dominating frontier LLM research. Instead, it focused on techniques and approaches designed to bring the cost of training and inference down to an approachable level. Using a unique Dense+MoE Hybrid transformer model, which combined a "small" 10b dense transformer with 128x3.66b MoE for 480B total and 17B active parameters. This approach allowed Snowflake to train Arctic with far less cost and time than other models - with a budget of under $2m and less than 3K GPU weeks of compute capacity. That's 17x less compute budget than Llama 3 and 7x less than DBRX for comparable capability across metrics important to the Enterprise. The demonstrated cost efficiency of Arctic, opens the way for many more enterprises to train their own first-party model tailored to their own expert domains. This is an incredibly exciting time to be in this space! Play with it at https://lnkd.in/gneYWTav #AI #Snowflake #GenAI #Opensource #SnowflakeArctic

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As a Snowflake Alum, I was initially nervous with Snowflake's seemingly late entry into the Generative AI race. But my initial doubts have turned into enthusiasm lately as Snowflake's strategy appears to be taking shape. From its very beginning, everything Snowflake has introduced has been geared toward efficiency and ease of use, and it's clearly no different with their efforts in the Generative AI space. The true promise of Gen AI is not whether or not it can do your child's homework, but how it can be integrated into everyday business activities that drive growth and efficiency. With the launch of Arctic, Snowflake's vision for the future of Generative AI is becoming much clearer. Get your popcorn ready!!! #ITconsulting #technologyconsulting #Snowflake #TheGreenLeafWay https://lnkd.in/ej-Gm3HG

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Databricks Sees Compound Systems as Cure to AI Ailments Another well writeen article by Alex Woodie in Datarama "The solution, according to Databricks, is to build compound #GenAI applications that utilize the best of each #LLM. With today’s updates to its #Mosaic AI platform, Databricks says customers can string together compound AI systems that connect LLMs to customers’ data using vector databases, vector search, and retrieval augmented generation (#RAG) capabilities." https://lnkd.in/giuNi_5Q

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If you missed Databricks Data+AI Summit 2024, there is still plenty of content recorded for you. Included, is the excellent session by #thoughtleaders Elise Georis, Staff Product Manager and Peter Pogorski, Senior Product Manager. Introducing Databricks’ New Native Ingestion Connectors A major challenge data practitioners face is maintaining #dataingestionpipelines—from on-prem #database connectivity, to evolving SaaS #APIs, to holistic #governance.   Watch this session to learn more about a new set of native ingestion connectors for popular SaaS applications, databases, and file sources. These connectors are auto-optimized, low-maintenance, and integrated with the rest of the #DataIntelligencePlatform—from governance via #UnityCatalog to #orchestration via Databricks Workflows. And you can do it all via a no-code UI or an API.In this demo-packed session, you’ll learn about what’s new, what’s on the roadmap, how to take full advantage of these new ingestion capabilities. https://lnkd.in/g5gKf_bK

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Materialized Views and Streaming Tables are now Generally Available in Databricks SQL! 🥳 In our internal benchmarks, Materialized Views delivered ~7x better data freshness at 1/50th of the cost of a similar CREATE TABLE AS statement. 👀 Read more here!

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