MLOps: Integrating HuggingFace Transformers with MLflow
Integrate HuggingFace Transformers with MLflow for efficient MLOps, enhancing model tracking and deployment in machine learning projects.
Kubeflow vs MLFlow: Choosing the Right MLOps Platform
Explore production-grade MLOps with Kubeflow and MLFlow. Compare features, benefits, and use cases to choose the right open-source tool for your machine learning operations.
Kubeflow vs MLFlow: Comparing Open-Source MLOps Platforms
Compare Kubeflow and MLFlow for MLOps, exploring their similarities, differences, and selection criteria. Gain insights into open-source machine learning operations and Canonical's solutions.
Introduction to MLFlow: Managing Machine Learning Workflows
Explore MLFlow's role in managing machine learning workflows, its key components, and how it fits into the MLOps ecosystem. Learn about Charmed MLFlow and its applications.
Kubeflow vs MLflow - Choosing the Right Tool for Machine Learning Operations
Explore production-grade MLOps, comparing Kubeflow and MLflow for open-source, community-driven machine learning tooling. Gain insights on pros and cons to make informed decisions.
DeepBrew 2.0 - Starbucks Enterprise Managed ML Platform
Plataforma empresarial para operacionalizar soluciones de IA/ML a escala, con automatización, servicios gestionados y mejores prácticas. Utiliza Databricks, MLflow, Azure y herramientas de monitoreo para un desarrollo eficiente.
Scaling MLOps to Retrain 50k Weekly Models in Parallel Using UDFs
Discover how to scale MLOps for high-frequency retraining of 50k models weekly using Databricks, MLflow, and Pandas UDFs. Learn best practices for experiment tracking, code consistency, and handling data volatility at scale.
Latest Advancements in MLflow
Explore recent MLflow features, including GenAI additions, and learn how to optimize machine learning workflows with practical examples and best practices for implementation.
Building Enterprise-Grade GenAI Apps with MLflow and Vector Search
Explore enterprise-grade GenAI app development using MLflow and Vector Search, focusing on data protection, AI deployment challenges, and leveraging proprietary information securely with Databricks tools.
RAGs to Data Riches - Talking to Your Enterprise Data
Explore enterprise-grade RAG solutions with Databricks, featuring Unity Catalog integration, security measures, and seamless Microsoft Copilot integration for enhanced data value extraction.
Building Production Quality Generative AI Applications
Explore a developer workflow for deploying high-quality AI applications, covering experimentation, feedback, and performance metrics using Databricks tools like Vector Search and MLflow.
The MLOps Platform at WGU - Solutions to Production ML with Databricks
Explore WGU's MARVIN platform, addressing MLOps challenges with Databricks. Learn about streamlined workflows, project templates, and strategies for efficient ML model deployment at scale.
From Uncertainty to Certainty: Strategies for Deterministic LLMOps
Explore strategies for implementing deterministic approaches in LLM operations, focusing on assessment, evaluation, and refinement to enhance reliability and stakeholder trust in AI applications.
MLOps MLFlow - Integración con LlamaIndex en Español
Descubre cómo integrar LlamaIndex con MLFlow y OpenAI, explorando el seguimiento y gestión de modelos de lenguaje mediante la función Traces para optimizar tus proyectos de MLOps.
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.
LLMOps MLflow - Document Classification with Fine-Tuning and Inference
Domina el fine-tuning de modelos LLM usando MLflow, desde la creación de clasificadores de documentos hasta la implementación con Docker y Azure DevOps para inferencia eficiente.
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.
How to Build a Cloud Native Data Lake with Open Source Technologies
Discover how to deploy a Kubernetes-based data lake using open source tools, from initial setup to running a complete prototype platform on your local machine.