How can Snowflake’s Data Cloud help data scientists better analyze data and glean insights for the business?
If you’re a data scientist, Snowflake Summit 2021 will provide answers to this and many more questions that will help you spend less time finding and preparing data and more time helping your organization make smarter data-informed decisions. You’ll hear informative executive insights and learn about the newest features and capabilities in Snowflake. You’ll also have the opportunity to strengthen your technical skills in hands-on virtual labs led by subject-matter experts.
Here are some of the highlights of Summit 2021 for data scientists:
Summit Opening Keynote: “Data Together Now” with Frank Slootman and Geoffrey Moore
Snowflake Chairman and CEO Frank Slootman and author Geoffrey Moore will discuss trends in cloud computing and how technologies cross the chasm. They’ll also share customer stories about organizations embracing the Data Cloud. You will hear from these two technology luminaries about how you can fully leverage data to transform your organization and become data-driven.
Summit Product Keynote: “What’s Next in the Data Cloud” with Benoit Dageville and Christian Kleinerman
Benoit Dageville, co-founder and President of Products, and Christian Kleinerman, Snowflake’s SVP, Product Management, will share how the Data Cloud vision has become a reality, and they’ll unveil the latest Snowflake innovations in five key areas: connected industries, global governance, platform optimization, data programmability, and applications powered by Snowflake. You will see new capabilities in action and hear directly from customers and partners about what these new advancements mean for your organization.
What’s New: Extensibility in Snowflake
Data pipelines often entail various types of transformations for different use cases and different users. Managing these pipelines with different systems can bring many complexities and challenges. Snowflake is continuously making data pipelines extensible so that more can be done with less. In this session, we’ll discuss the present and future of Snowflake’s extensibility, including Java functions, Snowpark, and more. We’ll demo many exciting features in our roadmap and explore the scenarios they’ll unlock. You will also hear from our customer, Comscore, about what it’s building with Java functions.
What’s New: Unstructured Data Management in Snowflake
Unstructured data represents a tremendous amount of information, but it’s not easy to search, analyze, or query unstructured data, especially on the fly, posing more challenges to data architectures that are already complex. Snowflake’s Data Cloud has completely changed the data paradigm by providing unparalleled data access and governance for structured and semi-structured data. Now, we are adding support for unstructured data in Snowflake so customers can extend all these benefits to more use cases with simplicity. In this session, you will learn about Snowflake’s new support for unstructured data and see demos on how you can store, access, process, govern, and share it in a single data platform.
Collaborative and Scalable Data Science at Caterpillar with the Data Cloud
Because of disparate data sources, lack of trust in the data, and compute concurrency limitations, data scientists can spend 80% of their time collecting and preparing data. To address these challenges and increase collaboration across its analytics teams, Caterpillar embarked on a journey to unify data and build a scalable solution for machine learning to fulfill a digital vision of transforming data into recommended actions and insights that increase profitability, productivity, and safety. The leader of Caterpillar’s data and analytics architecture shares how Caterpillar used a combination of Amazon S3, Amazon SageMaker, and Snowflake to build and deploy models leveraging billions of data points from its semi-structured telematics data.
Faster Time to Value with Feature Engineering and Model Scoring in Snowflake with DataRobot
This presentation provides an overview of the DataRobot AutoML and MLOps platforms and how they integrate directly with Snowflake to provide users with increased efficiency for both training and scoring machine learning models. The integrations accelerate time to value with a focus on bringing the processes for training and scoring closer to where the data resides. You can learn about and view demonstrations of two new integrations between the products: executing DataRobot feature engineering directly in Snowflake and leveraging Snowflake’s new Java user-defined function (UDF) feature to score DataRobot models directly in Snowflake.
Snowpark and Java Functions Under the Hood
At Snowflake, we continue to extend our platform with features that help customers achieve more with less. Snowpark opens up a new programming model for Snowflake, and Java functions expand the possibilities for data transformation and analysis. In this session, the engineering leads for Snowpark and Java functions will dive into the details behind these features, explaining how they work, best practices to follow, and how Snowflake provides a secure and performant environment for the execution of Java code right inside of Snowflake.
Advanced SQL Tips and Tricks for Data Preparation
The success of your machine learning models depends on how well the data is presented to the model. This is best achieved by transforming data into features that better represent the underlying problem to the model. And just like data, features should not live in their separate silos. By curating and sharing your features inside Snowflake, they get all the same benefits from the Snowflake platform in terms of performance, security, and discoverability. In this session, you’ll hear from our product team about patterns and techniques that can be applied to perform in-database feature engineering, storing and serving over thousands of features with Snowflake.
Automated Data Pipelines with Snowflake
Data pipelines are the lifeblood of modern analytics. It’s no easy task for data engineers to keep data pipelines reliable and scalable throughout the ingestion, transformation, and delivery stages. In this session, we’ll explore how these challenges can be addressed by automating data pipelines using existing and new Snowflake capabilities, particularly when you’re working with semi-structured data.
Introducing Snowflake Integration with Amazon SageMaker Autopilot
Building machine learning (ML) pipelines can often be complicated due to the tools and depth of technical knowledge required to build, train, and deploy the right prediction models. Join this session to learn how easy it can be to create and deploy high-performing ML models using Amazon SageMaker Autopilot with your data in Snowflake. This integration empowers a wide set of users such as BI analysts, database developers, and citizen data scientists to harness the power of predictive analytics without deep ML expertise. Through a demo, you will learn how to leverage Snowflake’s external functions to set up and work with SageMaker Autopilot using familiar SQL commands, natively from within Snowflake.
How Novartis Scaled Machine Learning with Dataiku and Snowflake
AI and machine learning continue to gain ground with the help of the democratization of data outside of so-called “traditional” data-centric roles. In this session, you’ll learn how Novartis leveraged Dataiku and Snowflake to transform its data culture, enabling users to explore, prototype, build, and deliver data science and machine learning projects at scale.
“Molly’s Game”: A Playbook for Resilience and Reinvention
Join Snowflake’s John Sapone, SVP of Enterprise Sales, in conversation with Molly Bloom, author of the best-selling memoir “Molly’s Game,” which was turned into a movie directed by Aaron Sorkin. Hear Molly Bloom’s story and learn how her life was shaped by resilience and continual reinvention. The conversation will cover how Molly managed risk, grew networks of people, and mastered the art of pivoting while growing her poker game from a single contact to a spreadsheet and ultimately into the most highly sought-after, $100M poker game.
The Snowflake Startup Challenge – Grand Finale
Hundreds of early-stage startups from over 50 countries who built their applications on Snowflake competed for a chance to win up to $250,000 in funding from Snowflake Ventures and gain marketing exposure. Join this session to watch as the final three companies pitch themselves to a panel of judges who will announce the grand prize winner on the spot. Judging and selecting the winner will be Benoit Dageville, Snowflake Co-Founder and President of Products; Denise Persson, Snowflake Chief Marketing Officer; Mike Speiser, Managing Director at Sutter Hill Ventures; and Carl Eschenbach, Partner at Sequoia Capital.
Virtual Hands-On Labs:
Bring Third-Party Data into Your Machine Learning Forecast with Dataiku
Learn how to build machine learning models with Snowflake and Dataiku.
Accelerating Data Science Using AutoML with DataRobot
Discover how to prepare data in Snowflake as well as build and train a customer-churn model with DataRobot.
Build a Recommendation Engine with Amazon SageMaker
Learn how to build a recommendation system using Amazon SageMaker with data stored in Snowflake.
Accelerating Data Engineering with Snowflake and dbt
This lab will feature a step-by-step guide using SQL to transform data with the instant scalability of Snowflake and to easily apply engineering principles with dbt.
To learn more about Snowflake Summit events geared towards data scientists, see your personalized agenda. You can also view the full conference agenda and read our definitive guide to Snowflake Summit 2021. We hope to see you there!
This post contains express and implied forwarding-looking statements, including statements regarding Snowflake’s (i) business strategy, (ii) products, services, and technology offerings, including those that are under development, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of our products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Annual Report on Form 10-K for the fiscal year ended January 31, 2021 that Snowflake has filed with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forwarding-looking statements as predictions of future events.
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