MLOps Community Podcast
# Data Visualization
# RAG
# Renumics
Visualize - Bringing Structure to Unstructured Data
This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models.
Markus Stoll & Demetrios Brinkmann · Sep 3rd, 2024
Popular topics
# Interview
# Artificial Intelligence
# Machine Learning
# MLops
# Coding Workshop
# Presentation
# Deployment
# MLOps
# Monitoring
# Feature Stores
# Case Study
# Model Serving
# FinTech
# Cultural Side
# Scaling
# Security
# Analytics
# ML Platform
# Explainable AI
# CPU
Sean Morgan & Demetrios Brinkmann · Aug 30th, 2024
MLSecOps, which is the practice of integrating security practices into the AIML lifecycle (think infusing MLOps with DevSecOps practices), is a critical part of any team’s AI Security Posture Management. In this talk, we’ll discuss how to threat model realistic AIML security risks, how you can measure your organization’s AI Security Posture, and most importantly how you can improve that security posture through the use of MLSecOps.
# MLSecOps
# AISPM
# Protect AI
Harcharan Kabbay & Demetrios Brinkmann · Aug 27th, 2024
The discussion begins with a brief overview of the Retrieval-Augmented Generation (RAG) framework, highlighting its significance in enhancing AI capabilities by combining retrieval mechanisms with generative models.
The podcast further explores the integration of MLOps, focusing on best practices for embedding the RAG framework into a CI/CD pipeline. This includes ensuring robust monitoring, effective version control, and automated deployment processes that maintain the agility and efficiency of AI applications.
A significant portion of the conversation is dedicated to the importance of automation in platform provisioning, emphasizing tools like Terraform. The discussion extends to application design, covering essential elements such as key vaults, configurations, and strategies for seamless promotion across different environments (development, testing, and production). We'll also address how to enhance the security posture of applications through network firewalls, key rotation, and other measures.
Let's talk about the power of Kubernetes and related tools to aid a good application design.
The podcast highlights the principles of good application design, including proper observability and eliminating single points of failure. I would share strategies to reduce development time by creating templates for GitHub repositories by application types to be re-used, also templates for pull requests, thereby minimizing human errors and streamlining the development process.
# GenAI Applications
# RAG
# CI/CD Pipeline
Nicolas Mauti & Demetrios Brinkmann · Aug 23rd, 2024
Need a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system.
We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you’re looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all!
# BigQuery
# Feature Store
# Malt
Andy McMahon & Demetrios Brinkmann · Aug 20th, 2024
As we move from MLOps to LLMOps we need to double down on some fundamental software engineering practices, as well as augment and add to these with some new techniques. In this case, let's talk about this!
# MLOps
# LLMOps
# Barclays
Yuri Plotkin & Demetrios Brinkmann · Aug 13th, 2024
Curiosity has been the underlying thread in Yuri's life and interests. With the explosion of Generative AI, Yuri was fascinated by the topic and decided he needed to learn more. Yuri pursued learning by reading, deriving, and understanding seminal papers within the last generation. The endeavors culminated in the writing of a book on the topic, The Variational Book, which Yuri expects to release shortly in the coming months. A bit of detail about the topics he covers can be found here: www.thevariationalbook.com.
# Generative AI
# The Variational Book
# Video Generation Tech
Ron Heichman · Aug 6th, 2024
Integrating AI APIs effectively is pivotal for building applications that leverage LLMs, especially given the inherent issues with accuracy, reliability, and safety that LLMs often exhibit. I aim to share practical strategies and experiences for using AI APIs in production settings, detailing how to adapt these APIs to specific use cases, mitigate potential risks, and enhance performance. The focus will be testing, measuring, and improving quality for RAG or knowledge workers utilizing AI APIs.
# AI APIs
# LLMs
# SentinelOne
Chinar Movsisyan & Demetrios Brinkmann · Jul 30th, 2024
We live in a world driven by large language models (LLMs) and generative AI, but ensuring they are ready for real-world deployment is crucial. Despite the availability of numerous evaluation tools, many LLM products still struggle to make it to production.
We propose a new perspective on how LLM products should be measured, evaluated, and improved. A product is only as good as the user's experience and expectations, and we aim to enhance LLM products to meet these standards reliably.
Our approach creates a new category that automates the need for separate evaluation, observability, monitoring, and experimentation tools. By starting with the user experience and working backward to the model, we provide a comprehensive view of how the product is actually used, rather than how it is intended to be used. This user-centric aka feedback-centric approach is the key to every successful product.
# LLMs
# Generative AI
# Feedback Intelligence
Nikhil Suresh & Demetrios Brinkmann · Jul 23rd, 2024
Nik joins the podcast to discuss an interesting trend: the anti-AI hype. Dive into what many companies might be missing when non-technical management rushes to roll out machine learning initiatives without bringing on the technical experts who can set them up for success. It's great to converse about bridging the gap between management and tech to make AI work effectively!
# Anti-AI hype
# AI Operations
# Hermit Tech
Eric Landry & Demetrios Brinkmann · Jul 19th, 2024
Eric Landry discusses the integration of AI in healthcare, highlighting use cases like patient engagement through chatbots and managing medical data. He addresses benchmarking and limiting hallucinations in LLMs, emphasizing privacy concerns and data localization. Landry maintains a hands-on approach to developing AI solutions and navigating the complexities of healthcare innovation. Despite necessary constraints, he underscores the potential for AI to proactively engage patients and improve health outcomes.
# Healthcare
# Continual.ai
# Zeteo Health
Aniket Kumar & Demetrios Brinkmann · Jul 16th, 2024
Dive into the world of Large Language Models (LLMs) like GPT-4. Why is it crucial to evaluate these models, how we measure their performance, and the common hurdles we face? Drawing from Aniket's research, he shares insights on the importance of prompt engineering and model selection. Aniket also discusses real-world applications in healthcare, economics, and education, and highlights future directions for improving LLMs.
# LLMs
# Evaluation
# MyEvaluationPal
# Ultium Cells