Discover how Dataiku's LLM Mesh empowers IT teams to create secure, scalable GenAI apps that align with operations and governance.

Looking to deliver safe, scalable, cost-effective, and future-proof LLM applications aligned with your operations and governance principles? Enter: The LLM Mesh. In this session, we’ll explore how Dataiku equips IT and Data teams to build secure, enterprise-ready GenAI applications, ensuring maximum control while delivering the high performance your business demands.

As organizations transition from digital to AI-native, data becomes the linchpin of innovation, empowering AI to turn raw information into actionable insights. Cloudera hybrid data platform brings all data to modern use cases including Generative AI. This session explores how Cloudera can help your organization deploy robust AI use cases to production faster, without compromising performance, accuracy, and security.

There are many things to think about on your Generative AI journey, but in this talk we’ll focus on two key ones. Have you identified use cases that will solve real business problems? Secondly, is your data platform prepared? 

From this session, Cynozure’s Solution Architect, Tom Wilson will share his experience of what it takes to successfully integrate GenAI across your data platform, touching on data quality, governance and model management. With this, he’ll also share the practical applications of GenAI, both now and the near-future, that will positively shake up the way we do business and deliver value to organisations that embrace them.  

Join us to learn about: 

• The important role your data platform plays in unlocking GenAI’s potential 

• Lessons, experiences and watch-outs from doing this 

• Use cases GenAI is best suited to right now 

• The complex and evolved use cases we can expect to see going forward  

Now that the hype of Gen AI is slowing down, the data functions are expected to scale effectively and realise the value from a technology that is evolving even faster than it can be implemented. So how do data leaders ensure that the benefits are optimised, the risks reduced and that the reality is not overtaken by the hype?  

Beyond deployment and value creation, there is the issue of trust-in the tools, the data and the outputs. To say nothing of the fear of people that their roles will be replaced! What are the skills that the ‘new’ data leader needs to perfect to manage teams, stakeholders and growing expectations?  

Hear from the lived experience from 2 of the industry’s leading proponents as they discuss their journeys and share their knowledge.

How about a workplace where generative AI accelerates every data management task, transforming routine into innovative experiences? A vision which can be in production for the AWS customers in just 60 days through a combination of Amazon Bedrock, which enables rapid development and deployment of AI applications, and Stratio Generative AI Data Fabric, which provides accurate output based on quality data with business meaning. Join us to learn how a combination of these products is empowering data managers and chief data officers to drive innovation and efficiency across their organizations. 

https://shorturl.at/vTyPl

As generative AI ramps up, there is a new urgency for database and analytics modernisation as the most popular and impactful AI innovations can be found in the cloud. Learn how to build a modern, cloud-based data foundation for data-driven applications, real-time analytics, and high-performance, immersive gen AI experiences.

Join us to explore how Zeenea’s data platform helps ensure data readiness, mitigate risks, and unlock GenAI potential in your organization.

As Generative AI continues to revolutionize industries, having high-quality, well-prepared data has never been more crucial. In this session, Emma McGrattan, SVP of Engineering & Product at Actian, and Guillaume Bodet, CPTO at Zeenea, will explore how Zeenea's cutting-edge Data Discovery Platform, now part of Actian, is poised to play a pivotal role in achieving data readiness for GenAI. Attendees will discover how Zeenea’s metadata management solutions, including its comprehensive data catalog, lineage insights, quality index, business glossary, and data marketplace, empower organizations to truly know and trust their data. Join us to learn how to leverage these tools to mitigate risks, ensure compliance, and confidently unlock the full potential of GenAI in your organization. Don’t miss this opportunity to prepare your data for the next wave of AI innovation! Speaker Bios: Emma McGrattan, SVP of Engineering & Product, Actian Emma is SVP of Engineering and Product at Actian leading global research and development. She is a recognized authority in data management and analytics technologies and holds multiple patents. Emma has over two decades of experience leading a global software development organization focused on innovation in high-performance analytics, data management, integration, and application development technologies. Prior to joining Actian, Emma was Vice President for Ingres at Computer Associates. Educated in Ireland, Emma holds a Bachelor of Electrical Engineering degree from Dublin City University.

As organisations shift from generative AI proof of concepts to building production ready applications, the requirements for efficiency, monitoring, safety and governance become critical to both trust and success.

You will learn:

Key design patterns and methodology for evaluating, experimenting and monitoring enterprise gen AI apps to address common failure modes

The role of iteration and improvement as part of ongoing delivery

Practical considerations for implementation using examples from Snowflake’s Cortex Analyst, Cortex Search and TruLens, an open source project.

In today’s episode, I’m going to perhaps work myself out of some consulting engagements, but hey, that’s ok! True consulting is about service—not PPT decks with strategies and tiers of people attached to rate cards. Specifically today, I decided to reframe a topic and approach it from the opposite/negative side. So, instead of telling you when the right time is to get UX design help for your enterprise SAAS analytics or AI product(s), today I’m going to tell you when you should NOT get help! 

Reframing this was really fun and made me think a lot as I recorded the episode. Some of these reasons aren’t necessarily representative of what I believe, but rather what I’ve heard from clients and prospects over 25 years—what they believe. For each of these, I’m also giving a counterargument, so hopefully, you get both sides of the coin. 

Finally, analytical thinkers, especially data product managers it seems, often want to quantify all forms of value they produce in hard monetary units—and so in this episode, I’m also going to talk about other forms of value that products can create that are worth paying for—and how mushy things like “feelings” might just come into play ;-)  Ready?

Highlights/ Skip to:

(1:52) Going for short, easy wins (4:29) When you think you have good design sense/taste  (7:09) The impending changes coming with GenAI (11:27) Concerns about "dumbing down" or oversimplifying technical analytics solutions that need to be powerful and flexible (15:36) Agile and process FTW? (18:59) UX design for and with platform products (21:14) The risk of involving designers who don’t understand data, analytics, AI, or your complex domain considerations  (30:09) Designing after the ML models have been trained—and it’s too late to go back  (34:59) Not tapping professional design help when your user base is small , and you have routine access and exposure to them   (40:01) Explaining the value of UX design investments to your stakeholders when you don’t 100% control the budget or decisions 

Quotes from Today’s Episode “It is true that most impactful design often creates more product and engineering work because humans are messy. While there sometimes are these magic, small GUI-type changes that have big impact downstream, the big picture value of UX can be lost if you’re simply assigning low-level GUI improvement tasks and hoping to see a big product win. It always comes back to the game you’re playing inside your team: are you working to produce UX and business outcomes or shipping outputs on time? ” (3:18) “If you’re building something that needs to generate revenue, there has to be a sense of trust and belief in the solution. We’ve all seen the challenges of this with LLMs. [when] you’re unable to get it to respond in a way that makes you feel confident that it understood the query to begin with. And then you start to have all these questions about, ‘Is the answer not in there,’ or ‘Am I not prompting it correctly?’ If you think that most of this is just an technical data science problem, then don’t bother to invest in UX design work… ” (9:52) “Design is about, at a minimum, making it useful and usable, if not delightful. In order to do that, we need to understand the people that are going to use it. What would an improvement to this person’s life look like? Simplifying and dumbing things down is not always the answer. There are tools and solutions that need to be complex, flexible, and/or provide a lot of power – especially in an enterprise context. Working with a designer who solely insists on simplifying everything at all costs regardless of your stated business outcome goals is a red flag—and a reason not to invest in UX design—at least with them!“ (12:28)“I think what an analytics product manager [or] an AI product manager needs to accept is there are other ways to measure the value of UX design’s contribution to your product and to your organization. Let’s say that you have a mission-critical internal data product, it’s used by the most senior executives in the organization, and you and your team made their day, or their month, or their quarter. You saved their job. You made them feel like a hero. What is the value  of giving them that experience and making them feel like those things… What is that worth when a key customer or colleague feels like you have their back with this solution you created? Ideas that spread, win, and if these people are spreading your idea, your product, or your solution… there’s a lot of value in that.” (43:33)

“Let’s think about value in non-financial terms. Terms like feelings. We buy insurance all the time. We’re spending money on something that most likely will have zero economic value this year because we’re actually trying not to have to file claims. Yet this industry does very well because the feeling of security matters. That feeling is worth something to a lot of people. The value of feeling secure is something greater than whatever the cost of the insurance plan. If your solution can build feelings of confidence and security, what is that worth? Does “hard to measure precisely” necessarily mean “low value?”  (47:26)

This episode features an engaging discussion between Raja Iqbal, Founder and CEO of Data Science Dojo, and Amr Awadallah, Founder and CEO of Vectara, the trusted GenAI Platform for All Builders. Raja sits down with Amr Awadallah, a visionary who has played a key role in shaping the world of technology. From his early days at Microsoft to his leadership roles at VMware and Vectara, Awadallah has been a driving force behind groundbreaking innovations in data, cloud computing, and artificial intelligence.This episode is a must-watch for anyone interested in a comprehensive outlook on AI's current state and future trajectory.