The world of machine learning is evolving so quickly that it’s challenging to find real-world use cases that are relevant to what you’re working on. That’s why we collected these technical blogs from industry thought leaders with practical use cases you can leverage today. This how-to reference guide provides everything you need — including code samples and notebooks — to start putting the Databricks platform to work. Get the eBook to learn how to: • Use dynamic time warping and MLflow to detect sales trends series • Access new product capabilities with demos • Perform multivariate time series forecasting with recurrent neural networks • Detect financial fraud at scale with decision trees and MLflow on Databricks   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“This how-to guide provides everything you need to learn how to translate raw data into actionable data. You’ll learn best practices from leaders and experts using code samples, notebooks and public data sets. Get your copy and start exploring the data lifecycle on the Databricks Lakehouse Platform — from data ingestion to data processing, analytics and machine learning — with real-life end-to-end use cases from leading companies such as J.B. Hunt, ABN AMRO and Atlassian. Download now to learn use cases such as: • Enabling real-time point-of-sale analytics • Building a cybersecurity lakehouse • Unlocking the power of health data”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“Want to learn how to overcome key data reliability challenges? Download a preview of the O’Reilly ebook, Delta Lake: The Definitive Guide, to learn about Delta Lake basic operations and how the time travel feature gives you access to historical data. These chapters will help you: • Understand Delta Lake and unpack transaction logs • Utilize Delta Tables, Delta Conversions and Delta Logs • Learn how to convert a Parquet table to a Delta table • Use time travel to roll back and examine previous versions of your data”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“This MIT Technology Review Insights report summarizes the findings from interviews with nine data leaders from leading global brands and a global survey of more than 350 CDOs, CIOs and other data leaders. Learn valuable insights such as: • Why only 13% of organizations are delivering on their data strategy (and what these leaders credit their success to) • Why over 50%of respondents said they’re struggling to scale ML use cases • Why open standards are the top requirement for future data architecture strategies “As tools advance and people become more familiar with advanced analytics and data science, we’ve got to give users the ability to run the analytics themselves, rather than just consume analytics that someone else produces,” – Bob Darin, CDO, CVS Health”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“The enterprise mandate for growth is getting very complex. Every organization is working to improve business outcomes while effectively managing a variety of risks — including economic, compliance, security and fraud, and competitive risks. Organizations’ data and the systems that process it play a critical role in minimizing these business risks as well as enabling their financial goals. CIO’s, CDO’s and other data and technology leaders have realized that their legacy IT platforms are unable to scale and meet the increasing demands for better analytics and AI. As a result, they are looking to transform how their organizations use and process data. Successful data transformation initiatives involve the design of hardware and software systems and the alignment of people, processes and culture. These initiatives always require a major financial investment and, therefore, need to yield a significant return on investment — that starts in months, not years. Read this comprehensive guide to learn about the 10 key considerations data and technology leaders must address when developing their enterprise data and AI strategy.”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“Bill Inmon, widely considered the father of the data warehouse, heralds the birth of the data lakehouse, which makes efficient ML and business analytics possible directly on data lakes. According to Bill, the data lakehouse presents an opportunity similar to the early years of the data warehouse market. The lakehouse’s unique ability to combine the data science focus of the data lake with the analytics power of the data warehouse — in an open environment — will unlock incredible value for organizations. Get your copy to discover the 5 key steps to building a successful data lakehouse: 1. Start with the data lake that already manages most of the enterprise data 2. Bring quality and governance to your data lake 3. Optimize data for fast query performance 4. Provide native support for machine learning 5. Prevent lock-in by using open data formats and APIs”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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In this technical training, we’ll explore how to use Apache SparkTM, Delta Lake and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“More than 80% of machine learning (ML) projects are scrapped before production. Learn how to overcome the many pitfalls of the ML lifecycle in this new eBook. Based on the soon-to-be-published “Machine Learning Engineering in Action” book from Manning Publications, it provides a step-by-step guide to help you plan, develop and deploy your ML projects at scale. Download this eBook to learn: • How to take ML projects from planning to production • Why ML projects fail and how to avoid common mistakes • How to productionize and make ML work at scale • How technologies and processes interact in a large ML initiative”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“Data management has been a critical and common practice employed across industries for many years. At its core, data management encompasses all disciplines related to managing data as a strategic and valuable resource, including collecting, processing, governing, sharing and analyzing data — and doing it all in a costefficient, effective and reliable manner. efficient, effective and reliable manner. But how do organizations truly avoid the data management mess and uplevel this process to more efficiently serve downstream analytics, data science and machine learning? Download this eBook to dive into data management on Databricks:ingestion, transformation, analytics, sharing and governance. In this eBook, you will learn how to: • Automatically and reliably ingest and prepare structured and unstructured data at scale for data lakes • Simplify your architecture and enable data scientists and analysts to query the freshest and most complete data using their SQL and BI tools of choice • Centrally share and govern data within and across organizations using open source Delta Sharing and a unified data catalog”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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“Bill Inmon, widely considered the father of the data warehouse, heralds the birth of the data lakehouse, which makes efficient ML and business analytics possible directly on data lakes. According to Bill, the data lakehouse presents an opportunity similar to the early years of the data warehouse market. The lakehouse’s unique ability to combine the data science focus of the data lake with the analytics power of the data warehouse — in an open environment — will unlock incredible value for organizations. Get your copy to discover the 5 key steps to building a successful data lakehouse: 1. Start with the data lake that already manages most of the enterprise data 2. Bring quality and governance to your data lake 3. Optimize data for fast query performance 4. Provide native support for machine learning 5. Prevent lock-in by using open data formats and APIs”   View More If your Download does not start Automatically, Click Download Whitepaper Download Whitepaper

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