Day 2 Morning Keynote | Data + AI Summit 2022 Production Machine Learning | Patrick Wendell MLflow 2.0 | Kasey Uhlenhuth Revolutionizing agriculture with AI: Delivering smart industrial solutions built upon a Lakehouse architecture | Ganesh Jayaram Intuit’s Data Journey to the Lakehouse: Developing Smart, Personalized Financial Products for 100M+ Consumers & Small Businesses | Alon Amit and Manish Amde Workflows | Stacy Kerkela Delta Live Tables | Michael Armbrust AI and creativity, and building data products where there's no quantitative metric for success, such as in games, or web-scale search, or content discovery | Hilary Mason What to Know about Data Science and Machine Learning in 2022 | Peter Norvig Data-centric AI development: From Big Data to Good Data | Andrew Ng

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Data + AI Summit Keynote talk from Michael Armbrust

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Intuit is the global technology platform that helps 100M consumers and small businesses overcome their most important financial challenges. In 2020-21, Intuit QuickBooks Capital facilitated more than $1.4B in loans to approximately 40,000 small businesses to help manage their cash flow through the pandemic, by harnessing the power of data and AI.

Pivotal to Intuit’s success is a lakehouse data architecture, catalyzed by the adoption of Databricks, for collecting, processing, and transforming petabytes of raw data into a unified mesh of high quality data. Altogether, enabling the company to accelerate delivery of awesome AI-driven personalized customer experiences at scale with products such as TurboTax, QuickBooks and Mint.

In this talk, Intuit’s AI+Data Vice President of Product, Alon Amit and Director of Engineering, Manish Amde, will provide insight into the company’s migration to a lakehouse architecture, highlight use cases to illustrate its value, and share lessons learned.

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Data lineage tracking continues to be a major problem for many organizations. The variety of data tools and frameworks used in big companies’ and a lack of standards and universal lineage tracking solutions (especially open-source ones) makes it very difficult or sometimes even impossible to reliably track and visualize dataflows end to end. Spline is one of a very few open-source solutions available nowadays that tries to address that problem. Spline has started as a data-lineage tracking tool for Apache Spark. But now it offers a generic API and model that is capable to aggregate lineage metadata gathered from different data tools, wire it all together, providing a full end-to-end representation of how the data flows through the pipelines, and how it transforms along the way.

In this presentation we will explain how Spline can be used as a central data-lineage tracking tool for the organization. We’ll briefly cover the high-level architecture and design ideas, outline challenges and limitations of the current solution, and talk about deployment options. We’ll also talk about how Spline compares to some other open-source tools, and how OpenLineage standard can be leveraged to integrate with them.

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Swedbank is the largest bank in Sweden & third largest in Nordics. They have about 7-8M customers across retail, mortgage , and investment (pensions). One of the key drivers for the bank was to look at data across all silos and build analytics to drive their ML models - they couldn’t. That’s when Swedbank made a strategic decision to go to the cloud and make bets on Databricks, Immuta, and Azure.

-Enterprise analytics in cloud is an initiative to move Swedbanks on-premise Hadoop based data lake into the cloud to provide improved analytical capabilities at scale. The strategic goals of the “Analytics Data Lake” are: -Advanced analytics: Improve analytical capabilities in terms of functionality, reduce analytics time to market and better predictive modelling -A Catalyst for Sharing Data: Make data Visible, Accessible, Understandable, Linked, and Trusted Technical advancements: Future proof with ability to add new tools/libraries, support for 3rd party solutions for Deep Learning/AI

To achieve these goals, Swedbank had to migrate existing capabilities and application services to Azure Databricks & implement Immuta as its unified access control plane. A “data discovery” space was created for data scientists to be able to come & scan (new) data, develop, train & operationalise ML models. To meet these goals Swedbank requires dynamic and granular data access controls to both mitigate data exposure (due to compromised accounts, attackers monitoring a network, and other threats) while empowering users via self-service data discovery & analytics. Protection of sensitive data is key to enable Swedbank to support key financial services use cases.

The presentation will focus on this journey, calling out key technical challenges, learning & benefits observed.

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In this keynote, you will learn more about Amgen's data platform journey from data warehouse to data lakehouse. They’’ll discuss our decision process and the challenges they faced with legacy architectures, and how they designed and implemented a sustaining platform strategy with Databricks Lakehouse, accelerating their ability to democratize data to thousands of users.
Today, Amgen has implemented 400+ data science and analytics projects covering use cases like clinical trial optimization, supply chain management and commercial sales reporting, with more to come as they complete their digital transformation and unlock the power of data across the company.

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The US Air Force VAULT platform is a cloud-native enterprise data platform designed to provide the Department of the Air Force (DAF) with a robust, interoperable, and secure data environment. The strategic goals of VAULT include:

• Leading Data Culture - Increase data use and literacy to improve efficiency and effectiveness of decisions, readiness, mission operations, and cybersecurity.
• A Catalyst for Sharing Data - Make data Visible, Accessible, Understandable, Linked, and Trusted (VAULT).
• Driving Data Capabilities - Increase access to the right combination of state-of-the-art technologies needed to best utilize data.

To achieve these goals, the VAULT team created a self-service platform to onboard and extract, transform and load data, perform data analytics, machine learning and visualization, and data governance. Supporting over 50 tenants across NIPR and SIPR, adds complexity to maintaining data security while ensuring data can be shared and utilized for analytics. To meet these goals VAULT requires dynamic and granular data access controls to both mitigate data exposure (due to compromised accounts, attackers monitoring a network, and other threats) while empowering users via self-service analytics. Protection of sensitive data is key to enable VAULT to support key use cases such as personal readiness to optimally place Airmen trainees to meet production goals, increase readiness, and match trainees to their preferences.

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In this presentation we will talk about how we will use available NER based sensitive data detection methods, automated record of activity processing on top of spark and feast for collaborative intelligent analytics & governed data sharing. Information sharing is the key to successful business outcomes but it's complicated by sensitive information both user centric and business centric.

Our presentation is motivated by the need to share key KPIs, outcomes for health screening data collected from various surveys to improve care and assistance. In particular, collaborative information sharing was needed to help with health data management, early detection and prevention of disease KPIs. We will present a framework or an approach we have used for these purposes.

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Vision AI and Azure Cognitive services can be applied in a variety of ways for healthcare, especially for Animal Health. Animal Diagnostics market size is valued at over USD 4.5 Billion in 2020 and is expected to grow at CAGR of 8.5% from 2021 to 2027(Markets&Markets Study).

The overall livestock advanced monitoring market is expected to grow from USD 1.4 billion in 2021 to USD 2.3 billion by 2026; it is expected to grow at a CAGR of 10.4% during 2021–2026.

We hope to showcase various uses of AI/ML for the care of livestock and companion animals to help assist vets and farm-owners. Live demos will include real life case studies and forward looking applications of the same using reinforced learning techniques and services.

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Hear Ganesh Jayaram, CIO of John Deere, talk about how the company is leveraging big data and AI to deliver ‘smart’ industrial solutions that are revolutionizing agriculture, driving sustainability and ultimately helping to feed the world. The John Deere Data Factory that is built upon the Databricks Lakehouse Platform is at the core of this innovation. It ingests 8 petabytes of data and trillions of records to give data teams fast, reliable access to standardized data sets to deliver over 3000 ML and analytics use cases that democratize data across John Deere, to deliver a culture of empowerment where data is everybody's responsibility.

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