Adrian A.’s Post

I love hearing about how our customers are building on Snowflake. Judo Bank's, general manager of data, Isaac Davis, said after considering various tools, Judo settled on Snowflake. Despite involving around 1,400 workloads, the migration to Snowflake only took two months, helped in part by the in-house creation of tools to automate code changes. The migration put Judo “in a really good situation”, said Davis. There is less fluctuation in processing times, and the platform requires less ongoing management, freeing up around 5% of the team’s time, and provides secure data sharing and collaboration. While, Commonwealth Bank’s (CBA’s) chief data and analytics officer, Andrew McMullan, lauded Snowflake’s versatility for model creation and execution, including models from external platforms. Applications range from real-time fraud detection to personalised customer offers. The bank even uses Snowflake to detect and block abusive messages in online payment descriptions, sharing these algorithms with other financial institutions. All of the bank’s AI models are monitored in real time via Snowflake, with alerts going to the right people if a model’s performance drifts beyond specified limits. Read about how Snowflake is bringing AI to more customers across ANZ - https://lnkd.in/g8TX8f8r #snowflake #AI #data Theo Hourmouzis Krzysztof Zielinski Kate Power Natalie Mead Jon Robertson Ash Willis James Butler Eszter Szikora Brad Burns Kaitlyn Hopkins Lindsey Shepard Olivia Kealey

Some amazing Commercial customer stories in the article, anyone in Public Sector keen to learn more about what Snowflake can do for you please reach out. 🔨 Bunnings has transformed its data capabilities with Snowflake, generating over 300,000 personalised catalogue versions and using real-time weather data to optimise in-store merchandising. 🥑 Coles transitioned from on-premises systems to Snowflake's cloud platform, gaining security, scalability, and cost efficiency & cutting monthly operating cost. 🥋 Judo Bank swiftly migrated 1,400 workloads to Snowflake in just two months, freeing up time for its data team and strengthening data governance. 🏦 Commonwealth Bank leverages Snowflake to detect fraud, personalise offers, and collaborate securely on customer safety. With Snowflake's Data Exchange and Marketplace, CBA shares critical data insights without compromising privacy.

Snowflake's user friendliness, business manageability, SQL ubiquity ( and GPTability) and scalability is truly awesome. In my last six months of late to the party, hands on exploration, what excites me the most, is that business teams can 'own' Snowflake. Owning doesn't mean implementing or running the WH. What do I mean by owning it ? Owning = being accountable. The credit-based pricing is an unifier and financial delight, makes everything measurable, transparent, and scalable. Its unlike "lets spin a cluster". If marketing needs more data for personalization, you can calculate the total ROI - martech tool + data piping cost. If you need governance, its baked in. Something tells me that we are just getting started. A gen AI copilot that spews credit optimized SQL queries for business users and a taskrabbit for no copy transfers to tools, can revolutionise data democratization via Snowflake. #snowflake #ai #data #datacloud

In the rapidly evolving field of machine learning, managing features efficiently is paramount. The introduction of Snowflake Feature Store addresses this challenge by allowing teams to create, manage, and reuse features seamlessly. This not only enhances collaboration among data scientists but also accelerates the model development cycle. To maximize the effectiveness of your Feature Store, consider the following steps: start by identifying key features that deliver impactful insights, ensure their reusability across different projects, and leverage the powerful capabilities of the Snowflake platform to handle your data efficiently. By implementing these strategies, you'll be positioned to improve your machine learning workflows and derive more value from your data. Sharing knowledge and learning alongside our community is invaluable. We invite you to share your tips or experiences with Snowflake Feature Store. #MachineLearning #DataScience #Snowflake #FeatureStore #Collaboration #DataDriven https://lnkd.in/eb7tX-hC

Talk to all of your data in a conversational interface !. Snowflake Intelligence empowers business users to analyze, summarize and take action from structured and unstructured data in one place and in a conversational way.

It is not only about AI powered by data but Data powered by AI too, Snowflake just announced a brilliant capability (among many others): “Snowflake Intelligence” during their annual “Build” event. Using Snowflake Intelligence, users will be able to set up and deploy "data agents" that will work on structured and unstructured data that exists outside of Snowflake as well as on data hosted within Snowflake. Data agents, LLM-powered knowledge workers, perform both "read" and "write" tasks based on the instructions given by the user. Snowflake Intelligence will allow users to interact with data agents using natural language to ask questions. Users can also ask the data agents to take specific actions across different tools and applications such as Salesforce, Slack, SharePoint, and Google Workspace.

We’re introducing Snowflake Intelligence to make it easier for everyone to drive value with data. Imagine asking a data agent: “give me a summary of this Google Doc” or “tell me how many deals we had in North America last quarter,” and instantly following up with the next steps using that same agent. That’s exactly what Snowflake Intelligence will enable: a seamless way to leverage and take action on your data in one place. Excited to share this update with you all. This is what we mean by the Era of Enterprise AI! 🚀

The era of Eneterprise AI with ❄️Snowflake Intelligence❄️ (in preview soon). Snowflake Intelligence is a platform that will help enterprise users set up and deploy dedicated ‘data agents’ to extract relevant business insights from their data, hosted within their data cloud instance and beyond, and then use those insights to take actions across different tools and applications, like Google Workspace and Salesforce.

Exciting times in the Era of Enterprise AI as Snowflake unviels the Snowflake Intelligence. 🚀 With this groundbreaking platform, business users can create data agents, so they can analyze, summarize, and take action from their enterprise data.

The release of Snowflake Intelligence brings data agents for Enterprise AI, marking a new era where the power of data is more accessible than ever. This is huge news for organizations looking to close the gap between business and IT teams, fostering a collaborative environment where all stakeholders can talk to and leverage their enterprise data effectively. For businesses, this means enhanced collaboration and more intuitive access to data-driven insights, without the technical hurdles that often create silos. IT teams can now empower their colleagues with seamless data tools, simplifying complex analytics and creating a unified language for data across the organization. We're excited to share the next step in Snowflake’s AI journey, further enabling teams to easily, and safely, advance their businesses with data-driven insights they can act on to deliver measurable impact.