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PODCAST

DataFramed

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you"re a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses

technology

All Episodes

39:06

#244 Using Data to Optimize Costs in Healthcare with...

DataFramed ·

2024/09/16

In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved?Travis Dalton is the President

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54:05

#243 No-Code LLMs In Practice with Birago Jones &...

DataFramed ·

2024/09/12

As AI becomes more accessible, a growing question is: should machine learning experts always be the ones training models, or is there a better way to leverage other subject matter experts in the business who know the use-case best? What if getting started building AI apps required no coding skills? As businesses look to implement AI at scale, what part can no-code AI apps play in getting projects off the ground, and how feasible are smaller, tailored solutions for department specific

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50:02

#242 Data Storytelling for Kids with Cole Nussbaumer...

DataFramed ·

2024/09/09

We’ve all met someone with a limiting belief, someone who describes their relationship with data as: “I’m not a data person” or “I can’t tell a data story.” Oftentimes, this mindset starts in childhood. Data storytelling is an incredible vehicle to challenge and reshape these beliefs early on. Imagine if kids could develop the skills to ask the right questions, interpret data, and tell powerful stories with it from a young age. How can we introduce children to data storytelling in a fun and engaging

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44:10

#241 Getting Generative AI Into Production with Lin...

DataFramed ·

2024/09/05

Lot’s of AI use-cases can start with big ideas and exciting possibilities, but turning those ideas into real results is where the challenge lies. How do you take a powerful model and make it work effectively in a specific business context? What steps are necessary to fine-tune and optimize your AI tools to deliver both performance and cost efficiency? And as AI continues to evolve, how do you stay ahead of the curve while ensuring that your solutions are scalable and sustainable?Lin Qiao is the CEO

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39:07

#240 Generative AI in the Enterprise with Steve...

DataFramed ·

2024/09/02

The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while maintaining control and transparency? And how can you build excitement and trust around AI within your

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51:20

#239 New Models for Digital Transformation with...

DataFramed ·

2024/08/29

The pressure to innovate with AI is immense. There is seemingly a race against the clock for organizations to incorporate AI into their product offering, aside from continual digital transformation. As the speed of AI development accelerates, many organizations struggle to keep up, facing challenges from data readiness to changing traditional business processes. How can businesses ensure that their AI initiatives not only align with strategic goals but also foster real, tangible progress? What steps

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39:49

#238 Data & AI for Improving Patient Outcomes with...

DataFramed ·

2024/08/26

One of the prerequisites for being able to do great data analyses is that the data is well structured and clean and high quality. For individual projects, this is often annoying to get right. On a corporate level, it’s often a huge blocker to productivity. And then there’s healthcare data. When you consider all the healthcare records across the USA, or any other country for that matter, there are so many data formats created by so many different organizations, it’s frankly a horrendous mess. This is

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49:24

#237 Guardrails for the Future of AI with Viktor...

DataFramed ·

2024/08/22

Guardrails are not something we actively use in our day-to-day lives, they’re in place to keep us safe when we lack the control needed to keep us on course, and for that, they are essential. Navigating the complexities of decision-making in AI and data can be challenging, especially on a global scale when many are searching for any sort of competitive advantage. Every choice you make can have significant impacts, and having the right frameworks, ethics and guardrails in place are crucial. But how do

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41:51

#236 Optimizing Sales Using AI with Ellie Fields,...

DataFramed ·

2024/08/19

Doing sales better is perhaps the most direct route to making more revenue, so it should be a priority for every business. B2B sales is often very complex, with a mix of emails and video calls and prospects interacting with your website and social content. And you often have multiple people making decisions about a purchase. All this generates a massive data—or, more accurately, a mess of data—which very few sales teams manage to harness effectively. How can sales teams can make use of data,

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45:26

#235 Developing Generative AI Applications with...

DataFramed ·

2024/08/15

One of the big use cases of generative AI is having small applications to solve specific tasks. These are known as AI agents or AI assistants. Since they’re small and narrow in scope, you probably want to create and use lots of them, which means you need to be able to create them cheaply and easily. I’m curious as to how you go about doing this from an organizational point of view. Who needs to be involved? What’s the workflow and what technology do you need?Dmitry Shapiro is the CEO of MindStudio

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42:54

#234 High Performance Generative AI Applications with...

DataFramed ·

2024/08/12

Perhaps the biggest complaint about generative AI is hallucination. If the text you want to generate involves facts, for example, a chatbot that answers questions, then hallucination is a problem. The solution to this is to make use of a technique called retrieval augmented generation, where you store facts in a vector database and retrieve the most appropriate ones to send to the large language model to help it give accurate responses. So, what goes into building vector databases and how do they

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39:26

#233 Generative AI at EY with John Thompson, Head of...

DataFramed ·

2024/08/08

By now, many of us are convinced that generative AI chatbots like ChatGPT are useful at work. However, many executives are rightfully worried about the risks from having business and customer conversations recorded by AI chatbot platforms. Some privacy and security-conscious organizations are going so far as to block these AI platforms completely. For organizations such as EY, a company that derives value from its intellectual property, leaders need to strike a balance between privacy and

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01:00:11

#232 How are Businesses Really Using AI? With...

DataFramed ·

2024/08/05

There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the same problem: not having an AI strategy and not understanding how to tactically implement AI. There’s a

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45:02

#231 Manage Your Data Better with Shinji Kim, CEO at...

DataFramed ·

2024/08/01

One of the most annoying conversations about data that happens far too often is: “Can you do an analysis and answer this business problem for me?” “Sure, where’s the data?” “I don’t know. Probably in one of our databases.” At this point more time is spent hunting for data than actually analyzing it. Rather than grumbling about it, it would obviously be more productive to learn how to solve data discoverability issues. What’s the best way to properly document data sets? How can you avoid spending all

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39:32

#230 Scaling Experimentation at American Express with...

DataFramed ·

2024/07/29

One of the best applications of data science is that it allows experimentation within any organization at scale. The ability to test a new checkout feature, the color of a button, and analyze whether that improves customer experiences can be truly magical when done correctly. However, doing this at scale means that the entire organization needs to be bought into the experimentation agenda. So how do you do this and how do you make sure this becomes part of your organization’s culture?Amit Mondal

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38:46

#229 Inside Meta's Biggest and Best Open-Source AI...

DataFramed ·

2024/07/25

Meta has been at the absolute edge of the open-source AI ecosystem, and with the recent release of Llama 3.1, they have officially created the largest open-source model to date. So, what's the secret behind the performance gains of Llama 3.1? What will the future of open-source AI look like?Thomas Scialom is a Senior Staff Research Scientist (LLMs) at Meta AI, and is one of the co-creators of the Llama family of models. Prior to joining Meta, Thomas worked as a Teacher, Lecturer, Speaker and Quant

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34:44

#228 Are Spreadsheets Still Relevant For Data...

DataFramed ·

2024/07/22

Excel often gets unfair criticism from data practitioners, many of us will remember a time when Excel was looked down upon—why would anyone use Excel when we have powerful tools like Python, R, SQL, or BI tools? However, like it or not, Excel is here to stay, and there’s a meme, bordering on reality, that Excel is carrying a large chunk of the world’s GDP. But when it really comes down to it, can you do data science in Excel?Jordan Goldmeier is an entrepreneur, a consultant, a best-selling author of

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57:01

#227 DataFramed x Analytics On Fire: Riding the AI...

DataFramed ·

2024/07/18

This special episode of DataFramed was made in collaboration with Analytics on Fire! Nowadays, the hype around generative AI is only the tip of the iceberg. There are so many ideas being touted as the next big thing that it’s difficult to keep up. More importantly, it’s challenging to discern which ideas will become the next ChatGPT and which will end up like the next NFT. How do we cut through the noise?Mico Yuk is the Community Manager at Acryl Data and Co-Founder at Data Storytelling Academy

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52:11

#226 Creating Custom LLMs with Vincent Granville,...

DataFramed ·

2024/07/15

Despite GPT, Claude, Gemini, LLama and the other host of LLMs that we have access to, a variety of organizations are still exploring their options when it comes to custom LLMs. Logging in to ChatGPT is easy enough, and so is creating a 'custom' openAI GPT, but what does it take to create a truly custom LLM? When and why might this be useful, and will it be worth the effort?Vincent Granville is a pioneer in the AI and machine learning space, he is Co-Founder of Data Science Central, Founder of

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48:07

#225 The Full Stack Data Scientist with Savin Goyal,...

DataFramed ·

2024/07/11

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates

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