Pre-training and Fine-tuning of Code Generation Models
探索代码生成模型的构建过程，包括StarCoder的训练、负责任的AI实践，以及如何利用开源库来部署和使用这些强大的模型。
ML and MLOps with Databricks: LLMOps, MLFlow, Transformers, and Databricks - Spanish
Aprende LLMOps con MLFlow y Databricks: implementa y gestiona modelos de lenguaje a gran escala utilizando herramientas de vanguardia en español.
LLMOps: Personalizar ViT Classifier con tu Propia Data y Docker Inference
Personaliza un Vision Transformer con tu dataset, crea un docker y un punto de inferencia con FastAPI para clasificación de imágenes.
Vertex AI: Desplegar Llama3 8B a un Punto de Inferencia desde Model Garden
Explora el Model Garden de Vertex AI y despliega Llama3 8B para inferencia, consumiendo el endpoint con Python SDK.
Introducción a Azure Machine Learning Studio
Explora Azure Machine Learning Studio, el servicio en la nube de Microsoft para proyectos de aprendizaje automático. Descubre cómo respalda el ciclo de vida completo del desarrollo de modelos.
MlOps Mlflow: Trabajar con HuggingFace Transformers en Mlflow - Español
Aprende a integrar HuggingFace Transformers con MLflow para optimizar el desarrollo y despliegue de modelos de lenguaje avanzados.
MLOPS: Inferencia de Modelo ONNX en Azure ML Managed Endpoint AKS
Aprende a realizar inferencia en Azure Kubernetes Services utilizando un modelo ONNX y onnxruntime, creando un Managed Endpoint con Python SDK v2.
MLOPS: Cómo Trabajar con el CLI de Hugging Face Hub - Español
Aprende a instalar y utilizar el CLI de Huggingface Hub para gestionar modelos y datasets de manera eficiente en proyectos de aprendizaje automático.
Embarquer votre modèle de machine learning dans votre app
Découvrez comment intégrer un modèle de machine learning dans une application mobile, gérer le téléchargement des données, et comparer les performances avec un processus côté serveur.
Deep Learning Workshop for Satellite Imagery - Training & Prediction
Hands-on deep learning workshop for satellite imagery, covering model training, debugging, prediction, and advanced techniques like custom metrics, heatmaps, and model deployment.
Deep Learning Workshop for Satellite Imagery - Data Processing
Hands-on workshop on data processing for satellite imagery, covering fundamentals, Python processing, image manipulation, and preparation of training data for deep learning applications.
DevSecOps for AI: Introducing MLSecOps for Software 2.0
Explore MLSecOps for AI-driven software development, addressing security challenges and best practices in the evolving landscape of Software 2.0.
Amber - A Complete, ML-Based, Anomaly Detection Pipeline for Microcontrollers
Discover a complete ML-based anomaly detection pipeline for microcontrollers, featuring real-time sensor value processing, automatic hyperparameter tuning, and high-speed inference capabilities.
Kubeflow and Beyond - Automation of Model Training, Deployment and Testing
Automate ML workflow: data gathering, model training, deployment, testing, and production inferencing with a single command. Streamline the process for efficient continuous delivery.
Machine Learning with DataRobot AI Cloud Platform
Comprehensive tutorial on DataRobot AI Cloud Platform, covering machine learning model building, evaluation, and deployment. Explores key features like AutoML, bias detection, and model optimization.
An AI Engineer Guide to Comet Model Registry Platform
Learn to use Comet's Model Registry for logging, versioning, and deploying machine learning models. Explore key features like model export, registration, and monitoring in this hands-on tutorial using Keras and TensorFlow.
A Very First Look at H2O Hydrogen Torch
Explore H2O Hydrogen Torch: a no-code deep learning tool for diverse AI tasks. Learn its capabilities, experiment as novice and expert, and understand prediction pipelines and deployment options.
MLflow Model Serving - From Training to Production
Comprehensive overview of MLflow Model Serving, covering offline and online scoring, deployment options, and integration with Databricks, with practical examples and recent features.
Developing Scalable Machine Learning Pipelines for Gaming Industry
Explore scalable ML pipelines in gaming, leveraging Apache Spark for data-driven product development and improved user acquisition through advanced LTV modeling.
Building an MLOps Platform with MLflow for Rapid Model Productionalization
Discover how DataSentics built an MLOps platform around MLflow to streamline model productionalization, reducing deployment time to minutes. Learn about real-life experiences, platform demo, and lessons learned.
Enable Production ML with Databricks Feature Store
Simplify production ML with Databricks Feature Store. Learn to overcome data challenges, enable real-time inference, and streamline ML projects using a unified data lakehouse platform integrated with Apache Spark and MLflow.