This book is your comprehensive guide to building robust Generative AI solutions using the Databricks Data Intelligence Platform. Databricks is the fastest-growing data platform offering unified analytics and AI capabilities within a single governance framework, enabling organizations to streamline their data processing workflows, from ingestion to visualization. Additionally, Databricks provides features to train a high-quality large language model (LLM), whether you are looking for Retrieval-Augmented Generation (RAG) or fine-tuning. Databricks offers a scalable and efficient solution for processing large volumes of both structured and unstructured data, facilitating advanced analytics, machine learning, and real-time processing. In today's GenAI world, Databricks plays a crucial role in empowering organizations to extract value from their data effectively, driving innovation and gaining a competitive edge in the digital age. This book will not only help you master the Data Intelligence Platform but also help power your enterprise to the next level with a bespoke LLM unique to your organization. Beginning with foundational principles, the book starts with a platform overview and explores features and best practices for ingestion, transformation, and storage with Delta Lake. Advanced topics include leveraging Databricks SQL for querying and visualizing large datasets, ensuring data governance and security with Unity Catalog, and deploying machine learning and LLMs using Databricks MLflow for GenAI. Through practical examples, insights, and best practices, this book equips solution architects and data engineers with the knowledge to design and implement scalable data solutions, making it an indispensable resource for modern enterprises. Whether you are new to Databricks and trying to learn a new platform, a seasoned practitioner building data pipelines, data science models, or GenAI applications, or even an executive who wants to communicate the value of Databricks to customers, this book is for you. With its extensive feature and best practice deep dives, it also serves as an excellent reference guide if you are preparing for Databricks certification exams. What You Will Learn Foundational principles of Lakehouse architecture Key features including Unity Catalog, Databricks SQL (DBSQL), and Delta Live Tables Databricks Intelligence Platform and key functionalities Building and deploying GenAI Applications from data ingestion to model serving Databricks pricing, platform security, DBRX, and many more topics Who This Book Is For Solution architects, data engineers, data scientists, Databricks practitioners, and anyone who wants to deploy their Gen AI solutions with the Data Intelligence Platform. This is also a handbook for senior execs who need to communicate the value of Databricks to customers. People who are new to the Databricks Platform and want comprehensive insights will find the book accessible.

Uncover the power of Zero-ETL integrations for your data in AWS. Learn firsthand how integrating Amazon Aurora with Amazon Redshift and Amazon DynamoDB with Amazon OpenSearch enables near real-time analytics and machine learning. Tailored for developers, architects, and administrators, discover key insights, best practices, and techniques for seamless integration of fully managed services, reducing latency and unlocking the full potential of your data in AWS.

Learn more: https://go.aws/3x2mha0 Learn more about AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSEvents #GenerativeAI #AI #Cloud #AWSAIandDataConference

We are very happy to announce that we are organizing the 12th edition of the Eindhoven Data Community meetup. This time we will be back at one of our favourite locations, the Skybar in Microlab!

This meetup will be all about on how to make impact using Machine Learning. First, Maria Vechtomova, MLOps Tech Lead at Ahold Delhaize, will talk about how to standardize the processes around ML model deployment in order to significantly reduce time to production. Second, Yke Rusticus, ML engineer at Xebia Data, will tell more about how Vattenfall leverages data and AI to help customers get a grip on their energy consumption and help them become more sustainable one step at a time.

Leveraging data and AI to help customers become more sustainable one step at a time. Vattenfall's mission is to enable fossil free living in one generation. With gas prices rising and the effect of global warming becoming more apparent, more people are realising that we must act now. But where do you start? In this session, we will share how Vattenfall leverages data and AI to help customers get a grip on their energy consumption and help them become more sustainable one step at a time.

MLOps as a product 10 years ago, the first corporate companies started investing in data science. By now, most have multiple machine learning models running in production, but productionalizing new models takes longer than companies would like it to. The goal of MLOps is to standardize the processes around machine learning model deployment and significantly reduce time to production. In large organizations, this quickly pays off. Maria Vechtomova believes that MLOps should be viewed as a product, and therefore managed as a product. In her talk, she will walk through the components of an MLOps platform (such as MLOps infrastructure, reusable deployment pipelines and governance) and the composition of the ideal MLOps product team.

Program

• 17:00 – 18:00 🍕 Food
• 18:00 – 18:10 🎤 Welcome
• 18:10 – 19:00 🎤 Yke Rusticus - Leveraging data and AI to help customers become more sustainable one step at a time.
• 19:00 – 19:15 ⏸️ Break
• 19:15 – 20:00 🎤 Maria Vechtomova - MLOps as a product
• 20:00-21:00 🥤 Drinks

Speaker 1: Yke Rusticus Yke is a Machine Learning Engineer at Xebia Data with a background in astronomy and artificial intelligence. In the industry, he learned that models and algorithms often do not get past the experimentation phase, leading him to specialise in MLOps to bridge the gap between experimentation and production. As a professional in this field, Yke has developed ML platforms and use cases across different cloud providers, and is passionate about sharing his knowledge through tutorials and trainings.

Speaker 2: Maria Vechtomova Maria is a Lead ML Engineer at Ahold Delhaize, bridging the gap between data scientists infra and IT teams at different brands and focusing on standardization of ML model deployments across all the brands of Ahold Delhaize. Maria believes that a model only starts living when it is in production. For this reason, last seven years, she focused on MLOps. Together with Basak and Raphael, running Marvelous MLOps. For more info, follow Marvelous MLOps on Substack: https://marvelousmlops.substack.com

Recently, there has been a lot of buzz surrounding Gnerative AI and LLMs, with many discussions focusing on the huge impacts they have on the world. How will these changes influence our approach to data and analytics? How can we leverage the capabilities of GPT effectively to optimize our data processes, save time, and streamline our efforts?

In the upcoming meetup, we will delve into GPT and how it will transform our relationship with data. We will discuss the importance of semantic layer in unlocking the full power of visual data exploration and gaining deeper insights, enabling us to make better informed decisions with the same data. Furthermore, we will shed light on the expanding utilization of graph technology, which plays a significant role in data analysis, and we will emphasize the importance of data governance. Lastly, we will discuss how organizations capitalize on technological advancements to foster innovation in a data driven world. The lecture is for anyone interested in GenerativeAI and Data, suitable for beginners and experts alike.

Lecturer: Inna Tokarev Sela, The founder and CEO of illumex, the unified Business Data Language for Enterprises with proprietary Generative AI for automated Data and Analytics Interpretation. Throughout her career leading Data Products and as a data stakeholder, Inna recognized the oxymoron of our domain: despite huge investments in data and analytics, most business decisions are still not based on data or insights. Therefore, she uses her vast industry and academic experience to solve this conundrum and help data and business teams scale trust-based decision-making. Before embarking on her entrepreneurial journey, Inna was VP of AI at Sisense and Senior Director of Machine Learning at SAP. Additionally, She authored several patents on knowledge graphs, natural language, and deep learning, is a frequent speaker at leading D&A and AI conferences, and is Head of the Women in Data Israel chapter.

Recently, there has been a lot of buzz surrounding Gnerative AI and LLMs, with many discussions focusing on the huge impacts they have on the world. How will these changes influence our approach to data and analytics? How can we leverage the capabilities of GPT effectively to optimize our data processes, save time, and streamline our efforts?

In the upcoming meetup, we will delve into GPT and how it will transform our relationship with data. We will discuss the importance of semantic layer in unlocking the full power of visual data exploration and gaining deeper insights, enabling us to make better informed decisions with the same data. Furthermore, we will shed light on the expanding utilization of graph technology, which plays a significant role in data analysis, and we will emphasize the importance of data governance. Lastly, we will discuss how organizations capitalize on technological advancements to foster innovation in a data driven world. The lecture is for anyone interested in GenerativeAI and Data, suitable for beginners and experts alike.

Lecturer: Inna Tokarev Sela, The founder and CEO of illumex, the unified Business Data Language for Enterprises with proprietary Generative AI for automated Data and Analytics Interpretation. Throughout her career leading Data Products and as a data stakeholder, Inna recognized the oxymoron of our domain: despite huge investments in data and analytics, most business decisions are still not based on data or insights. Therefore, she uses her vast industry and academic experience to solve this conundrum and help data and business teams scale trust-based decision-making. Before embarking on her entrepreneurial journey, Inna was VP of AI at Sisense and Senior Director of Machine Learning at SAP. Additionally, She authored several patents on knowledge graphs, natural language, and deep learning, is a frequent speaker at leading D&A and AI conferences, and is Head of the Women in Data Israel chapter.

We are starting to see a paradigm shift in how AI systems are built across enterprises. In 2023 and beyond, this shift is being propelled by the era of foundation models. Foundation models can be seen as the next evolution in using "pre-trained" models and transfer learning. In order to fully leverage these breakthrough models, we’ve seen a common formula for success: leading AI teams within enterprises need to be able successfully harness their own store of unstructured data and pair this with the right model in order to ship intelligent applications that deliver next-generation experiences to their customers.

In this session you will learn how to incorporate foundation models into your data and machine learning workflows so that anyone can build AI faster and, in many cases, get the business outcome without needing to build AI models altogether. Which foundation AI models can be used to pre-label / enrich data and what specific data pipeline (data engine) will enable this? Real-world use cases of when to incorporate large language models and fine-tuning to improve machine learning models in real-time. Discover the power of leveraging both Labelbox and Databricks to streamline this data management and model deployment process.

Talk by: Manu Sharma

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc