The Evolution of Infrastructure from Code to Infrastructure Plus Code
Explore cloud development evolution, from Infrastructure as Code to integrated approaches. Discover how CDK and Wing bridge infrastructure and application development, addressing cloud adoption challenges.
Tokenization Engine and Web3 Integration in Hyperledger Fabric
Explore tokenization engine and Web3 integration in Hyperledger Fabric, including use cases and examples of permissioned/public chain interoperability. Gain insights from Oracle's blockchain expert.
Obtaining New Insights on Model Behavior with Fiddler - MLOps Meetup #110
Gain insights on model behavior using Fiddler for monitoring, explainability, and analysis. Learn to instrument ML models for observability, register models, and publish events to understand data drift and underperforming cohorts.
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.
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.
From Arduinos to LLMs: Exploring the Spectrum of ML - MLOps Podcast Episode 162
Explore MLOps spectrum from LLMs to TinyML. Gain insights on model deployment challenges, IoT integration, and strategies for constrained environments. Learn about the intersection of machine learning and electronics.
From Virtualization to AI Integration - MLOps Coffee Sessions
Explore AI integration challenges for businesses, from virtualization to modern ML systems. Gain insights on bridging gaps in AI implementation and investment strategies.
How Data Platforms Affect Machine Learning and Artificial Intelligence - MLOps Podcast
Explore how data platforms impact AI and ML applications, covering foundations, design patterns, data quality, and team structures for effective ML implementation.
Handling Multi-Terabyte LLM Checkpoints - MLOps Podcast #228
Explore techniques for handling multi-terabyte LLM checkpoints, including storage options, saving and loading strategies, and infrastructure considerations for large-scale AI model training.
Retrieval Augmented Generation - Techniques and Applications
Explore Retrieval Augmented Generation with Syed Asad. Dive into production issues, embedding models, inference layer experiments, and the evolving landscape of AI engineering in this insightful discussion.
AWS Trainium and Inferentia - Enhancing AI Performance and Cost Efficiency
Explore AWS Trainium and Inferentia's AI accelerators for enhanced performance, cost savings, and seamless integration with popular frameworks. Learn about their benefits and implementation in ML workflows.
BigQuery as a Feature Store for AI/ML Applications
Discover how to transform BigQuery into an effective feature management system for AI/ML applications. Learn about designing feature tables, addressing challenges, and optimizing workflows without additional software.
Azure AI Computer Vision OCR: Image and Document Intelligence Services
Aprende a utilizar Azure AI Services para OCR de imágenes y documentos con Computer Vision y Document Intelligence.
Google Vertex AI Python SDK and Gemini for Data Science and Machine Learning
Explore Google Vertex AI, SDK, and Gemini through practical demonstrations and hands-on coding examples for machine learning and data science applications.
Azure ML: Pipelines with Python SDK for Data Science and Machine Learning
Create ML pipelines with Azure Machine Learning Studio's Python SDK for efficient data science and machine learning workflows on Azure.
Azure ML: Foundation Models Inference Endpoint for Q&A
Explore Azure ML's Model Catalogue and create an Online Inference EndPoint for Foundation QA HuggingFace model predictions. Gain hands-on experience with Azure ML studio and machine learning deployment.
Azure ML: Creating and Using Pipelines with Batch Endpoints
Create and deploy a multi-component pipeline with Azure ML, including component registration, data management, and Batch Endpoint setup for efficient machine learning workflows.
Fine-Tuning Foundation Models with Azure Machine Learning Studio
Fine-tune a BERT foundation model using Azure Machine Learning Studio for enhanced natural language processing capabilities.
Fine-Tuning Foundation Models for Document Classification with Azure ML - LLama2
Fine-tune LLM for document classification using Azure ML Studio's Python SDK. Learn to adapt Llama2, Mistral, or other models for text classification tasks.
Deploying a Multimodal Chatbot with Gemini Pro and Streamlit on Google App Engine
Desarrolla un chatbot multimodal con Vertex AI, Gemini Pro y Streamlit, y despliégalo en Google App Engine para aplicaciones de aprendizaje automático.
Vertex AI: Model Garden and Deploying LLaMA 3 8B to Inference Endpoint
Explore Vertex AI's Model Garden, deploy LLama3 8B to an Inference Endpoint, and consume it using Python SDK. Gain hands-on experience with advanced machine learning tools.