Three Practical Use Cases for Databricks on AWS
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Automated data integration: Maximize your data lakehouse
By: Qlik
Type: eBook
The Databricks Lakehouse platform is a go-to for organizations eager to leverage their data fully. But to reap its full value, one must navigate data integration hurdles. This e-book details how to supercharge your Databricks Lakehouse by:
- Facilitating universal, real-time data ingestion from any source into Databricks
- Streamlining data transformation into analytics-ready forms without manual coding
- And creating a secure, governed data catalog for easy access to reliable data
Read this e-book now to find out how you can automate your entire data pipeline to reduce risk, boost ROI, and free your data engineers.
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The Big Book of Machine Learning Use Cases – 2nd Edition
By: DataBricks
Type: eBook
The world of machine learning is evolving so quickly that it’s challenging to find real-life use cases that are relevant to your day-to-day work.
That’s why Databricks created this comprehensive guide. Get everything you need — use cases, code samples and notebooks — so you can start putting the Databricks Lakehouse Platform to work today.
Plus, you’ll get case studies from leading companies like Comcast, Regeneron and Nationwide.
Learn how to:
- Improve on-shelf availability with out of stock modeling
- Make real-time decisions using quantitative modeling
- Use dynamic time warping and MLflow to detect sales trends series
- Detect financial fraud at scale with decision trees and MLflow on Databricks
By registering, I agree to the processing of my personal data by Databricks in accordance with their Privacy Policy. I can update my preferences at any time.
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Improving data quality in the age of generative AI
By: Alteryx
Type: White Paper
Generative AI can revolutionize your business. But it relies on one crucial element: high-quality data. Inaccurate or biased data can lead to unreliable AI models and costly mistakes.
This playbook from Alteryx and Databricks, Improving Data Quality in the Age of Generative AI, will give you strategies to overcome data quality challenges for your generative AI initiatives.
Inside, you’ll learn:
- Why “garbage in, garbage out” is more relevant than ever in the age of generative AI
- Why a modern data architecture is important for ensuring data quality
- How to bring in the domain experts who understand the data best
- Actionable steps from Alteryx and Databricks to integrate best practices
- And more
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118-page e-book: Mastering generative AI
By: DataBricks
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This 118-page e-book is a thorough guide to deploying GenAI applications. It details the entire GenAI process, from using foundation models to custom model pretraining and fine-tuning. Access the full e-book now to learn how you can unlock the potential of GenAI for your organization.
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5 Steps to Better Data Quality for Generative AI and Beyond
By: Dataiku
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Data quality is critical for successful Generative AI, but many organizations struggle with it. This white paper outlines 5 steps to improve data quality, including democratizing data quality efforts and embedding quality checks across operations. Read the full white paper to gain a competitive advantage through better data quality.
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Data-Driven Sales Mastery: Insights from Tech Leaders
By: TechTarget
Type: BrightTALK Webinar
In this webcast, leaders from TechTarget, Databricks and Airtable discuss how sales leaders can improve sales activity by leveraging data. But what data should they leverage, and how should they use it? Watch now to unlock answers.
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Cloud data lakes: How the top platforms compare
By: Qlik
Type: eBook
This e-book explores the top cloud data lake solutions from AWS, Google, Microsoft, Cloudera, Databricks, and Snowflake. Inside, you'll learn how these platforms compare in features like storage, processing, security, and analytics. Download this comprehensive comparison guide now to find the right cloud data lake for your enterprise.
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Cloud data lakes: How the top platforms compare
By: Qlik
Type: eBook
This e-book explores the top cloud data lake solutions from AWS, Google, Microsoft, Cloudera, Databricks, and Snowflake. Inside, you'll learn how these platforms compare in features like storage, processing, security, and analytics. Download this comprehensive comparison guide now to find the right cloud data lake for your enterprise.
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The rise of large language models & how to capitalize on it
By: DataBricks
Type: eBook
Large language models (LLMs) are becoming increasingly important in a variety of applications such as natural language processing, machine translation, code and text generation, and more. Read on to understand the recent rise of LLMs and discover strategies you can leverage to become more data-driven with an LLM.
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Ultimate guide to machine learning operations
By: DataBricks
Type: eBook
Generative models such as large language models (LLMs) and image generation models have revolutionized the machine learning (ML) landscape. However, their arrival also introduces new challenges. Read on to learn how you can leverage a unified, data-centric AI platform to achieve long-term efficiency and performance stability of ML in production.
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Advanced analytics meld with machine learning to press more value from big data
By: TechTarget ComputerWeekly.com
Type: eGuide
In this e-guide we shine the light on a number of case studies taken from around the world of data analytics. We delve into a number of Silicon Valley-based startups that are looking to shake up the data analytics industry as well as glimpsing into the future of AI-based analytics, data visualisation tools and the Australian data analytics market.