Large Language Models (LLMs) such as ChatGPT have revolutionized AI applications, offering unprecedented potential for complex real-world scenarios. However, fully harnessing this potential comes with unique challenges such as model brittleness and the need for consistent, accurate outputs. These hurdles become more pronounced when developing production-grade applications that utilize LLMs as a software abstraction layer.

In this session, we will tackle these challenges head-on. We introduce Guardrails AI, an open-source platform designed to mitigate risks and enhance the safety and efficiency of LLMs. We will delve into specific techniques and advanced control mechanisms that enable developers to optimize model performance effectively. Furthermore, we will explore how implementing these safeguards can significantly improve the development process of LLMs, ultimately leading to safer, more reliable, and robust real-world AI applications

Talk by: Shreya Rajpal

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

Interested in diving deeper into the world of large language models (LLMs) and their real-life applications? In this session, we bring together our experienced team members and some of our esteemed customers to talk about their journey with LLMs. We'll delve into the complexities of getting these models to perform accurately and efficiently, the challenges, and the dynamic nature of LLM technology as it constantly evolves. This engaging conversation will offer you a broader perspective on how LLMs are being applied across different industries and how they’re revolutionizing our interaction with technology. Whether you're well-versed in AI or just beginning to explore, this session promises to enrich your understanding of the practical aspects of LLM implementation.

Talk by: Sai Ravuru, Eric Peter, Ankit Mathur, and Salman Mohammed

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

In this session, we will highlight all parts of the analytics lifecycle using Dataiku + Databricks. Explore, blend, and prepare source data, train a machine learning model and score new data, and visualize and publish results — all using only Dataiku's visual interface. Plus, we will use LLMs for everything from simple data prep to sophisticated development pipelines. Attend and learn how you can truly have it all with Dataiku + Databricks.

Talk by: Amanda Milberg

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

Given the success of OpenAI’s GPT-4 and Google’s PaLM, every company is now assessing its own use cases for Large Language Models (LLMs). Many companies will ultimately decide to train their own LLMs for a variety of reasons, ranging from data privacy to increased control over updates and improvements. One of the most common reasons will be to make use of proprietary internal data.

In this session, we’ll go over how to train your own LLMs, from raw data to deployment in a user-facing production environment. We’ll discuss the engineering challenges, and the vendors that make up the modern LLM stack: Databricks, Hugging Face, and MosaicML. We’ll also break down what it means to train an LLM using your own data, including the various approaches and their associated tradeoffs.

Topics covered in this session: - How Replit trained a state-of-the-art LLM from scratch - The different approaches to using LLMs with your internal data - The differences between fine-tuning, instruction tuning, and RLHF

Talk by: Reza Shabani

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

Insights into Infosys' Topaz AI First Innovations including AI-enabled Analytics and AI-enabled Automation to help clients in significant cost savings, improved efficiency and customer experience across industry segments.

Talk by: Neeraj Dixit

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

Will LLMs change the way we work?  Ask questions from a panel of LLM and AI experts on what problems LLMs will solve and its potential new challenges

Talk by: Ben Harvey, Jan van der Vegt, Ankit Mathur, Debu Sinha, and Sean Owen

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

As Foundation Models (FMs) continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This talk will describe our work on applying FMs to structured data tasks like data linkage, cleaning and querying. We will then discuss challenges and solutions that these models present for production deployment in the modern data stack.

Talk by: Ines Chami

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

Using Databricks, we built a “Unified Talent Solution” backed by a robust data and AI engine for analyzing skills of a combined pool of permanent employees, contractors, part-time employees and vendors, inferring skill gaps, future trends and recommended priority areas to bridge talent gaps, which ultimately greatly improved operational efficiency, transparency, commercial model, and talent experience of our client. We leveraged a variety of ML algorithms such as boosting, neural networks and NLP transformers to provide better AI-driven insights.

One inevitable part of developing these models within a typical DS workflow is iteration. Databricks' end-to-end ML/DS workflow service, MLflow, helped streamline this process by organizing them into experiments that tracked the data used for training/testing, model artifacts, lineage and the corresponding results/metrics. For checking the health of our models using drift detection, bias and explainability techniques, MLflow's deploying, and monitoring services were leveraged extensively.

Our solution built on Databricks platform, simplified ML by defining a data-centric workflow that unified best practices from DevOps, DataOps, and ModelOps. Databricks Feature Store allowed us to productionize our models and features jointly. Insights were done with visually appealing charts and graphs using PowerBI, plotly, matplotlib, that answer business questions most relevant to clients. We built our own advanced custom analytics platform on top of delta lake as Delta’s ACID guarantees allows us to build a real-time reporting app that displays consistent and reliable data - React (for front-end), Structured Streaming for ingesting data from Delta table with live query analytics on real time data ML predictions based on analytics data.

Talk by: Nitu Nivedita

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

Ask questions from a panel of data science experts who have deployed LLMs and AI models into production.

Talk by: David Talby, Conor Murphy, Cheng Yin Eng, Sam Raymond, and Colton Peltier

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

PaLM 2 is a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction.

PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities.

Talk by: Andy Dai

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

In this demo, we will show you the fastest and functional answer engine and search copilot that exists right now: Perplexity.ai. It can solve a wide array of problems starting from giving you fast answers to any topic to planning trips and doing market research on things unfamiliar to you, all in a trustworthy way without hallucinations, providing you references in the form of citations. This is made possible by harnessing the power of LLMs along with retrieval augmented generation from traditional search engines and indexes.

We will also show you how information discovery can now be fully personalized to you: personalization through prompt engineering. Finally, we will see use cases of how this search copilot can help you in your day to day tasks in a data team: be it a data engineer, data scientist, or a data analyst.

Talk by: Aravind Srinivas

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

The challenge is no longer how big, diverse, or distributed your data is. It's that you can't trust it. Companies are utilizing rules and metrics to monitor data quality, but they’re tedious to set up and maintain. We will present a set of fully unsupervised machine learning algorithms for monitoring data quality at scale, which requires no setup, catching unexpected issues and preventing alert fatigue by minimizing false positives. At the end of this talk, participants will be equipped with insight into unsupervised data quality monitoring, its advantages and limitations, and how it can help scale trust in your data.

Talk by: Vicky Andonova

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

Patients are increasingly taking an active role in managing their healthcare costs and are more likely to choose providers and treatments based on cost considerations. Learn how technology can help build cost-efficient care models across the healthcare continuum, delivering higher quality care while improving patient experience and operational efficiency.

Talk by: Janine Pratt

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

This talk introduces AI Functions, a new feature in Databricks SQL that enables seamless integration of Large Language Models (LLMs) into SQL workflows. We illustrate how AI Functions simplifies the use of LLMs like OpenAI’s ChatGPT for tasks such as text classification, and bypassing the need for complex pipelines.

By demonstrating the setup and application of AI Functions, this shows how this tool democratizes AI and puts the power of LLMs directly into the hands of your data analysts and scientists. The talk concludes with a look towards the future of AI Functions and the exciting possibilities they unlock for businesses.

Talk by: Shitao Li and Yu Gong

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

Send us a text Part 2 : Vinca LaFleur of West Wing Writers. As a writer the West Wing is the dream job. Learn how to embody "It all starts with the ideas and content" - Vinca

00:30 Tricks and tips to a presentation04:14 Speech gotchas07:29 Knowing when you have a hit09:39 How much is delivery11:53 GOAT speechBobby Kennedy in Indianapolis: https://www.youtube.com/watch?v=A2kWIa8wSC014:49 Gettysburg case study17:11 ChatGPT?20:26 Mental cups26:55 West Wing Writers31:51 Chief34:30 Book recommended mostMade to Stick: https://www.amazon.com/Made-Stick-Ideas-SurviveOthers/dp/1400064287 LinkedIn: linkedin.com/in/vinca-lafleur-28909315 Website: https://www.westwingwriters.com/ Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Large Language Models (LLMs) are taking AI mainstream across companies and individuals. However, public LLMs are trained on general-purpose data. They do not include your own corporate data and they are black boxes on how they are trained. Because terminology is different for healthcare, financial, retail, digital-native and other industries, companies today are looking for industry-specific LLMs to better understand the terminology, context and knowledge that better suits their needs. In contrast to closed LLMs, open source-based models can be used for commercial usage or customized to suit an enterprise’s needs on their own data. Learn how Databricks makes it easy for you to build, tune and use custom models, including a deep dive into Dolly, the first open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use.

In this session, you will:

• See a real-life demo of creating your own LLMs specific to your industry
• Learn how to securely train on your own documents if needed
• Learn how Databricks makes it quick, scalable and inexpensive
• Deep dive into Dolly and its applications

Talk by: Sean Owen

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

In the fast-paced world of data science and AI, we will explore how large language models (LLMs) can elevate the development process of Apache Spark applications.

We'll demonstrate how LLMs can simplify SQL query creation, data ingestion, and DataFrame transformations, leading to faster development and clearer code that's easier to review and understand. We'll also show how LLMs can assist in creating visualizations and clarifying data insights, making complex data easy to understand.

Furthermore, we'll discuss how LLMs can be used to create user-defined data sources and functions, offering a higher level of adaptability in Apache Spark applications.

Our session, filled with practical examples, highlights the innovative role of LLMs in the realm of Apache Spark development. We invite you to join us in this exploration of how these advanced language models can drive innovation and boost efficiency in the sphere of data science and AI.

Talk by: Gengliang Wang and Allison Wang

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

MLflow is one of the most used open source machine learning frameworks with over 13 million monthly downloads. With the recent advancements in generative AI, MLflow has been rapidly integrating support for a lot of the popular AI tools being used such as Hugging Face, LangChain, and OpenAI. This means that it’s becoming easier than ever to build AI pipelines with your data as the foundation, yet expanding your capabilities with the incredible advancements of the AI community.

Come to this session to learn how MLflow can help you:

• Easily grab open source models from Hugging Face and use Transformers pipelines in MLflow
• Integrate LangChain for more advanced services and to add context into your model pipelines
• Bring in OpenAI APIs as part of your pipelines
• Quickly track and deploy models on the lakehouse using MLflow

Talk by: Corey Zumar and Ben Wilson

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

With the recent surge in popularity of ChatGPT and other LLMs such as Dolly, many people are going to start training, tuning, and deploying their own custom models to solve their domain-specific challenges. When training and tuning these models, there are certain considerations that need to be accounted for in the MLOps process that differ from traditional machine learning. Come watch this session where you’ll gain a better understanding of what to look out for when starting to enter the world of applying LLMs in your domain.

In this session, you’ll learn about:

• Grabbing foundational models and fine-tuning them
• Optimizing resource management such as GPUs
• Integrating human feedback and reinforcement learning to improve model performance
• Different evaluation methods for LLMs

Talk by: Joseph Bradley and Eric Peter

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

In a time when AI is evolving at breakneck speeds, taking a step back and gaining a bird's-eye view of the evolving AI ecosystem is paramount to understanding where the field is headed. With this bird's-eye view come a series of questions. Which trends will dominate generative AI in the foreseeable future? What are the truly transformative use-cases that will reshape our business landscape? What does the skills economy look like in an age of hyper intelligence? Enter Joanne Chen, General Partner at Foundation Capital. Joanne invests in early-stage AI-first B2B applications and data platforms that are the building blocks of the automated enterprise. She has shared her learnings as a featured speaker at conferences, including CES, SXSW, WebSummit, and has spoken about the impact of AI on society in her TED talk titled "Confessions of an AI Investor." Joanne began her career as an engineer at Cisco Systems and later co-founded a mobile gaming company. She also spent many years working on Wall Street at Jefferies & Company, helping tech companies go through the IPO and M&A processes, and at Probitas Partners, advising venture firms on their fundraising process. Throughout the episode, Richie and Joanne cover emerging trends in generative AI, business use cases that have emerged in the past year since the advent of tools like ChatGPT, the role of AI in augmenting work, the ever-changing job market and AI's impact on it, as well as actionable insights for individuals and organizations wanting to adopt AI. Links mentioned in the show: JasperAIAnyScaleCerebras[Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business[Course] Generative AI Concepts