MLOPS: Inference ViVIT ONNX Model in Azure ML Managed Endpoint (AKS)
Aprenda a implementar un modelo ViViT en ONNX para inferencia en CPU utilizando Azure Kubernetes Services y onnxruntime con Python SDK v2.
Managing MLOps at Scale in OpenShift/Kubernetes
Explore MLOps at scale using open-source tools like KServe and OpenDataHub. Learn to productize AI/ML models efficiently and deploy integrated toolsets for modeling without infrastructure concerns.
Introduction to Machine Learning Operations (MLOps)
Explore MLOps practices for efficient ML model deployment and maintenance. Learn about Charmed Kubeflow and how to choose the right solution for your AI/ML 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.
Open Source MLOps on AWS
Explore open source MLOps on AWS using Charmed Kubeflow. Learn about AI/ML challenges, industry benefits, and setting up efficient MLOps environments for quick experimentation and iteration.
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.
Building and Deploying Real-World ML Models with Ray and Weights & Biases
Discover how to leverage Ray and Weights & Biases for scalable MLOps, hyperparameter optimization, and real-world ML model deployment through case studies and practical insights.
A Ray-based ML Platform for Early Cancer Detection
Explore how Ray enhances scalability and efficiency in machine learning for early cancer detection, focusing on improved model development and evaluation for potential diagnostic applications.
Challenges in ML Engineering
Explore challenges in ML engineering, including cost-efficient inference, automation, accuracy monitoring, and anomaly detection for real-world AI applications.
Leveraging DRA for Conquering ML Workload Challenges on Kubernetes
Explore dynamic GPU resource allocation for ML workloads on Kubernetes using DRA, optimizing utilization and addressing challenges in GPU sharing and diverse model support.
MLOps Tutorial - Automating CI/CD Workflow for Dockerized Flask App Using GitHub Actions
Automate CI/CD for Dockerized Flask apps using GitHub Actions. Learn to build Docker images, push to Docker Hub, and streamline deployment workflows for Python applications.
How McDonald's Uses Machine Learning to Optimize Restaurant Site Selection
Explore ML techniques for optimizing restaurant site selection, leveraging Databricks Platform to enhance global decision-making for McDonald's expansion strategy and drive business outcomes.
Revolutionizing Insurance Customer Service with Advanced AI Chatbot
Transformación del servicio al cliente en seguros mediante chatbot AI avanzado, mejorando productividad y calidad. Desafíos, lecciones y estrategias en el desarrollo e implementación de IA generativa para crecimiento empresarial.
Introducing the Databricks AI Security Framework (DASF) to Manage AI Security Risks
Explore robust AI security guidelines for secure ML and AI application deployment. Learn about the Databricks AI Security Framework and gain tools to test Databricks deployments for AI security.
Near Realtime Model Serving with Databricks Serverless - Albertsons Case Study
Explore near real-time model serving for pricing analytics using Databricks serverless compute. Learn framework development, performance optimization, and cost-effective scaling techniques.
A Practical Introduction to Machine Learning with Databricks Mosaic AI
Explore Databricks' real-time inference capabilities for AI models, featuring strategies for deploying APIs and leveraging feature serving for instant data processing and decision-making.
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.
Path to Production - Databricks Project CICD for Seamless Inner to Outer Dev Loops
Explore Databricks Asset Bundles for seamless project development, from inner to outer loops. Learn workflow authoring and productionization using Github Actions and Azure Pipelines, adhering to DevOps best practices.
Introduction to DBRX and Shutterstock ImageAI Foundation Models
Explore Databricks' foundational models like DBRX, their capabilities, and applications. Learn to leverage these models for enhanced data analytics and machine learning projects across industries.