Evaluating and Building AI Systems - ML 166

Evaluating and Building AI Systems - ML 166

Michael Berk dives deep into the adventures of AI and machine learning with our special guest, Richmond Alake, a staff developer advocate at MongoDB. Richmond's journey from web development to AI was driven by a quest for excitement and new challenges. In this episode, he shares how he transitioned into the AI field, his passion for using writing as a learning tool, and the importance of continuous learning in evolving tech landscapes.


They explore the intricacies of building and evaluating Retrieval-Augmented Generation (RAG) systems, the benefits of MongoDB's versatile database functionalities, and the pressing challenges in machine learning data collection and evaluation. Richmond also gives us a peek into MongoDB's advanced solutions for AI application development and how strategic data chunking can impact efficiency.
Whether you're a budding AI enthusiast or an experienced developer looking to expand your horizons, this episode is packed with practical advice, career insights, and the latest trends in AI and machine learning. Stay tuned as we uncover how to navigate the complexity of RAG pipelines and the evolving landscape of generative AI. Let's get started!

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Episoder(209)

Why Authenticity Beats Algorithms: The New Rules of Digital Marketing - ML 185

Why Authenticity Beats Algorithms: The New Rules of Digital Marketing - ML 185

In this episode, we dive deep into the evolving landscape of digital marketing and brand storytelling. We explore how the intersection of authenticity, community, and technology is reshaping how brand...

4 Apr 202555min

Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184

Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184

Welcome back to another episode of Adventures in Machine Learning, where hosts Michael Berk and Ben Wilson delve into the intricate process of implementing model serving solutions. In this episode, th...

13 Feb 202551min

Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

Welcome to another insightful episode of Top End Devs, where we delve into the fascinating world of machine learning and data science. In this episode, host Charles Max Wood is joined by special guest...

24 Jan 202555min

Cows, Camels, and the Human Brain - ML 182

Cows, Camels, and the Human Brain - ML 182

What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researche...

9 Jan 202542min

A/B Testing with ML ft. Michael Berk - ML 181

A/B Testing with ML ft. Michael Berk - ML 181

Michael Berk joins the adventure to discuss how he uses Machine Learning within the context of A/B testing features within applications and how to know when you have a viable test option for your setu...

2 Jan 202545min

Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

In today's episode, we dive into the critical decision-making process of building versus buying technology solutions, especially when it comes to agentic logic-based frameworks. With the industry stil...

26 Des 202431min

Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

Peter Elger and Eóin Shanaghy join Charles Max Wood to dive into what Artificial Intelligence and Machine Learning related services are available for people to use. Peter and Eóin are experts in AWS a...

19 Des 202454min

Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

In this episode, Ben and Michael explore burnout, particularly in machine learning and data science. They highlight that burnout stems from exhaustion, cynicism, and inefficiency and can be caused by ...

12 Des 20241h 12min

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