After describing the work done at StoneX and her role at the organization, Elettra explains what drew her to neural networks, defines data science and how she overcame the challenges of learning something new on the job, breaks down what a data scientist needs to succeed, and shares her thoughts on why many still don’t fully understand the industry. Our guest also tells us how she identifies an inadequate data set, the recent innovations that are under construction at StoneX, how to ensure that your AI and ML models are compliant, and the importance of understanding AI as a mere tool to help you solve a problem.

Key Points From This Episode:

• Elettra Damaggio explains what StoneX Group does and how she ended up there.
• Her professional journey and how she acquired her skills.
• The state of neural networks while she was studying them, why she was drawn to the subject, and how it’s changed.
• StoneX’s data science and ML capabilities when she arrived, and Elettra’s role in the system.
• Her first experience of being thrown into the deep end of data science, and how she swam.
• A data scientist’s tools for success.
• The multidisciplinary leaders and departments that she sought to learn from when she entered data science.
• Defining data science, and why many do not fully understand the industry.
• How Elettra knows when her data set is inadequate.
• The recent projects and ML models that she’s been working on.
• Exploring the types of guardrails that are needed when training chatbots to be compliant.
• Elettra’s advice to those following a similar career path as hers.

Quotes:

“The best thing that you can have as a data scientist to be set up for success is to have a decent data warehouse.” — Elettra Damaggio [0:09:17]

“I am very much an introverted person. With age, I learned how to talk to people, but that wasn’t [always] the case.” — Elettra Damaggio [0:12:38]

“In reality, the hard part is to get to the data set – and the way you get to that data set is by being curious about the business you’re working with.” — Elettra Damaggio [0:13:58]

“[First], you need to have an idea of what is doable, what is not doable, [and] more importantly, what might solve the problem that [the client may] have, and then you can have a conversation with them.” — Elettra Damaggio [0:19:58]

“AI and ML is not the goal; it’s the tool. The goal is solving the problem.” — Elettra Damaggio [0:28:28]

Links Mentioned in Today’s Episode:

Elettra Damaggio on LinkedIn

StoneX Group

How AI Happens

Sama

Mike Miller is the Director of Project Management at AWS, and he joins us today to share about the inspirational AI-powered products and services that are making waves at Amazon, particularly those with generative prompt engineering capabilities. We discuss how Mike and his team choose which products to bring to market, the ins and outs of PartyRock including the challenges of developing it, AWS’s strategy for generative AI, and how the company aims to serve everyone, even those with very little technical knowledge. Mike also explains how customers are using his products and what he’s learned from their behaviors, and we discuss what may lie ahead in the future of generative prompt engineering.

Key Points From This Episode:

• Mike Miller’s professional background, and how he got into AI and AWS.
• How Mike and his team decide on the products to bring to market for developers.
• Where PartyRock came from and how it fits into AWS’s strategy.
• How AWS decided on the timing to make PartyRock accessible to all.
• What AWS’s products mean for those with zero coding experience.
• The level of oversight that is required to service clients who have no technical background.
• Taking a closer look at AWS’s strategy for generative AI.
• How customers are using PartyRock, and what Mike has learned from these observations.
• The challenges that the team faced whilst developing PartyRock, and how they persevered.
• Trying to understand the future of generative prompt engineering.
• A reminder that PartyRock is free, so go try it out!

Quotes:

“We were working on AI and ML [at Amazon] and discovered that developers learned best when they found relevant, interesting, [and] hands-on projects that they could work on. So, we built DeepLens as a way to provide a fun opportunity to get hands-on with some of these new technologies.” — Mike Miller [0:02:20]

“When we look at AIML and generative AI, these things are transformative technologies that really require almost a new set of intuition for developers who want to build on these things.” — Mike Miller [0:05:19]

“In the long run, innovations are going to come from everywhere; from all walks of life, from all skill levels, [and] from different backgrounds. The more of those people that we can provide the tools and the intuition and the power to create innovations, the better off we all are.” — Mike Miller [0:13:58]

“Given a paintbrush and a blank canvas, most people don’t wind up with The Sistine Chapel. [But] I think it’s important to give people an idea of what is possible.” — Mike Miller [0:25:34]

Links Mentioned in Today’s Episode:

Mike Miller on LinkedIn

Amazon Web Services

AWS DeepLens

AWS DeepRacer

AWS DeepComposer

PartyRock

Amazon Bedrock

How AI Happens

Sama

Key Points From This Episode:

• Welcoming Seth Walker to the podcast.
• Why Seth jokes about being chaotic in his approach to machine learning.
• The importance of being agile in AI.
• All about Seth’s company, Carrier, and what they do.
• Seth tells us about his background and how he ended up at Carrier.
• How Seth goes about unlocking the power of AI.
• The different levels of success when it comes to AI creation and how to measure them.
• Seth breaks down the different things Carrier focuses on.
• The importance of prompt engineering.
• What makes him excited about the new iterations of machine learning.

Quotes:

“In many ways, Carrier is going to be a necessary condition in order for AI to exist.” — Seth Walker [0:04:08]

“What’s hard about generating value with AI is doing it in a way that is actually actionable toward a specific business problem.” — Seth Walker [0:09:49]

“One of the things that we’ve found through experimentation with generative AI models is that they’re very sensitive to your content. I mean, there’s a reason that prompt engineering has become such an important skill to have.” — Seth Walker [0:25:56]

Links Mentioned in Today’s Episode:

Seth Walker on LinkedIn

Carrier

How AI Happens

Sama