Building Threat Detection Systems - An MLE's Perspective
Explore threat detection systems from an MLE's perspective. Gain insights on rule engines, testing ML models, and the importance of writing for professional development in MLOps.
Real-Time Machine Learning: Features and Inference - MLOps Podcast #135
Explore real-time ML features and inference with LinkedIn experts. Gain insights on implementation, challenges, and best practices for moving from batch to interactive near real-time machine learning.
Systems Engineering in Machine Learning - Navigating Low-Level Challenges
Explore systems engineering in ML with Andrew Dye, discussing low-level work, blending disciplines, and ML infrastructure challenges. Gain insights on distributed training, managing scale, and bridging gaps between firmware and MLOps.
Machine Learning Operations - What is it and Why Do We Need It?
Explore Machine Learning Operations (MLOps) with expert Niklas Kühl, discussing its definition, importance, and practical applications in industrial ML projects for effective automation and operationalization.
Explainability in the MLOps Cycle - MLOps Podcast Episode 138
Explore explainability in MLOps with Dattaraj Rao. Gain insights on rule-based systems, ML model deployment, and emerging trends in AI. Learn about responsible AI and practical applications in various industries.
Foundational Models in Enterprise AI - Challenges and Opportunities
Explore the future of AI with foundation models, their enterprise applications, and challenges. Learn about Snorkel AI's approach to optimizing these models for cost, quality, and control.
The Motivation for MLOps - Architectural Perspective
Explore MLOps adoption motivations, key areas, and architectural perspectives with Steven Fines. Gain insights on ML solution development, operations, and enterprise integration challenges.
Accelerating ML with Kubeflow for Autonomous Vehicles
Explore how Aurora Innovation accelerates ML model development for autonomous vehicles using Kubeflow, covering infrastructure evolution, pipeline building, and organization-wide adoption.
Let's Talk About Raw Documents - Extracting Structured Data for ML Pipelines
Explore raw document processing for ML pipelines with Unstructured.io's open-source tools. Learn to extract structured data, build custom preprocessing APIs, and integrate with popular ML frameworks.
Machine Learning in Production: A Data Scientist's Perspective from Ubisoft
Explore ML in production at Ubisoft with Jean-Michel Daignan. Gain insights on platform development, SDK tools, generative AI, and balancing value delivery with rapid development in the gaming industry.
How a Manager Became a Believer in DevOps for Machine Learning
Explore insights on leading data teams, improving as a software engineer, and migrating from monolith to microservices in healthcare. Learn from Keith Trnka's experience in NLP, machine learning, and telemedicine.
Declarative MLOps: Streamlining Model Serving on Kubernetes
Explore declarative MLOps on Kubernetes for streamlined model serving. Learn containerization, CI/CD with GitOps, and best practices for scalable, high-velocity ML deployments using native K8s operators.
Solving the Last Mile Problem of Foundation Models with Data-Centric AI
Explore data-centric AI techniques to enhance foundation models' accuracy for complex, high-value use cases. Learn to overcome hallucinations, biases, and misclassifications in real-world AI applications.
Want High Performing LLMs? It's All About Your Data - Importance of Quality Data in AI Development
Explore data-centric AI development for high-performing LLMs. Learn pre-training techniques, avoid common pitfalls, and understand the critical role of quality data in model performance.
Age of Industrialized AI - Challenges and Solutions for LLMs in Production
Explore the challenges and solutions for integrating LLMs into software, addressing hallucinations, security risks, and future developments in AI infrastructure.
Machine Learning Education at Uber - Developing an Internal ML Platform
Explore Uber's innovative ML education program, designed to empower employees with machine learning skills and enhance platform development through user feedback and engineering principles.
MLOps Build or Buy - Startup vs. Enterprise Perspectives
Explore MLOps strategies for startups and enterprises, focusing on build vs. buy decisions, data privacy, and recommender systems. Gain insights from Slack's ML engineers on platform development and integration challenges.
Why is MLOps Hard in an Enterprise? - Challenges and Solutions
Explore MLOps challenges in enterprise settings, focusing on data ecosystem complexity, cross-team collaboration, and standardization issues. Learn strategies for model reusability and effective implementation.
Fine-Tuning LLMs: Best Practices and When to Go Small - Lecture 124
Explore best practices for fine-tuning LLMs, including strategies for improving performance, overcoming challenges, and determining when to use smaller models for specific tasks.
Clean Code for Data Scientists - MLOps Coffee Sessions
Explore clean coding practices for data scientists, Shopify's real-time serving platform Merlin, and insights on LLMs in production with Matt Sharp, Data Developer at Shopify.
The Long Tail of ML Deployment - Challenges and Solutions
Explore ML deployment challenges and solutions with Baseten co-founder Tuhin Srivastava. Gain insights on engineering perspectives, LLM limitations, and the future of ML-powered products in this informative podcast episode.