MLOps MLFlow - Integration with LlamaIndex
Discover how to integrate MLFlow with LlamaIndex, leveraging OpenAI models and exploring MLflow traces for enhanced machine learning operations and tracking.
LLMOps with MLflow - Document Classification Using Fine-Tuned LLM Models
Master end-to-end document classification using MLflow for LLM fine-tuning, inference, and deployment with Docker. Build robust APIs and implement CI pipelines for efficient machine learning operations.
AI in Kubernetes - Getting Started with Machine Learning Pipeline Development
Discover how Kubernetes supports AI development with features like autoscaling, GPU management, and ML pipeline orchestration using Flyte for efficient model deployment and serving.
Optimizing AI and ML with Platform Engineering - A Case Study in Cloud Operations
Discover how platform engineering transforms AI and ML operations through cloud-native tools, focusing on efficiency, automation, and practical strategies to avoid over-engineering complexities.
BitNet.cpp - Inferencia de Modelos de hasta 100B en CPU
Descubre cómo implementar inferencia de modelos BitNet con precisión de 1 bit usando el framework de Microsoft para CPU, permitiendo el despliegue eficiente de modelos de hasta 100B de parámetros.
Fine-tuning T5-Large Model for Text Summarization with MLFlow
Master fine-tuning T5-Large models for text summarization using MLflow, covering model tracking, logging, and specialized training techniques for enhanced summarization capabilities.
Converting T5 Large Models to ONNX Format and 8-bit Quantization with MLflow and Optimum
Master model optimization techniques by converting T5 Large to ONNX format and implementing 8INT quantization using Hugging Face Transformers Optimum, with MLflow integration for text summarization tasks.
MLOps with MLFlow: Evaluating and Quantizing Fine-Tuned LLM Models
Discover how to evaluate fine-tuned LLMs using MLflow, convert models to ONNX format, and implement INT8 quantization for optimized performance and deployment.
Building Machine Learning Pipelines with Kubeflow
Master building ML pipelines using Kubeflow's components, from key considerations to best practices, and learn effective implementation strategies for scaling machine learning projects.
Integrating Generative AI into Business Applications for Accelerated ROI
Discover how to modernize legacy applications using Generative AI code translation with Red Hat OpenShift, Azure AI, and Watsonx for enhanced DevOps pipeline integration and faster delivery.
ML Workloads on Kubernetes with Kubeflow - Why and How
Discover how to effectively deploy and manage machine learning workloads on Kubernetes using Kubeflow, exploring key benefits and practical implementation strategies.
Engineering the Future of Generative AI Platforms on Kubernetes
Discover key principles for building scalable generative AI platforms using Kubernetes, covering platform evolution, deployment strategies, risk management, and community-driven development approaches.
Introduction to PromptFlow in Azure AI Studio
Explore PromptFlow's development tools for building LLM-based AI applications, covering the complete lifecycle from prototyping to production deployment and monitoring.
Evaluating LLM Responses with MLflow
Master MLflow's evaluation features to assess and analyze Large Language Model responses, enhancing your ability to measure and improve AI model performance.