Machine Learning Using Kubeflow and Kubernetes
Explore machine learning workflows on Kubernetes using Kubeflow, covering deployment, training, inference, and advanced features like hyperparameter tuning and automated pipelines.
MLflow - Platform for Complete Machine Learning Lifecycle
Discover how MLflow simplifies machine learning development by tracking experiments, registering projects, and streamlining model deployment across various platforms.
The Modern Trolley Problem
Exploring ethical dilemmas in self-driving car algorithms, demonstrating object recognition, and discussing responsible AI principles and sustainable engineering practices.
Taking Models to the Next Level with Azure Machine Learning
Explore Azure Machine Learning for custom model training, from pre-built cognitive services to AutoML and hyperparameter tuning, with practical examples for text analysis and sentiment analysis.
vLLM - Servidor Especializado en Inferencia de LLMs
Descubre cómo implementar y optimizar servidores de inferencia LLM con vLLM, una tecnología especializada para el procesamiento eficiente de modelos de lenguaje a gran escala.
AI for Hospitals at Scale - Building and Deploying Machine Learning Models in Healthcare
Discover how machine learning models are trained and deployed across hospitals, focusing on zero-shot learning for patient deterioration prediction and scalable microservice architecture for healthcare applications.
Navigating LLM Deployment - Tips, Tricks and Techniques
Master essential strategies for optimizing and deploying self-hosted language models, covering best practices for monitoring, implementation, and maximizing value across industries like finance and healthcare.
Building an Enterprise Scale MLOps Platform - From Development to Deployment
Discover how NatWest built a streamlined MLOps platform to enhance productivity, automate ML workflows, and ensure security compliance in enterprise-scale machine learning operations.
Engineers Guide for Shepherding Models into Production
Discover essential engineering practices for deploying machine learning models into production systems, from input/output definition to optimization and integration within larger infrastructures.
Что я хотел бы знать об MLOps год назад - Практический опыт внедрения Machine Learning
Discover essential MLOps insights through real-world experiences at Kolesa Group, covering ML model deployment, Docker, Kubernetes integration, and practical monitoring strategies for machine learning systems.
Using MLflow and Databricks to Deploy ML Models in Production
Explore MLflow and Databricks for production ML model deployment. Learn end-to-end management, model tracking, and deployment strategies, plus Databricks' Feature Store and AutoML.
Adding Interactivity to Your Data Science Models - Getting Started with Streamlit
Learn to create interactive web apps for data science using Streamlit. Explore basic concepts, add widgets, interact with data, and deploy to the cloud. Build apps for visualization, model training, and analytics.
Boost Your AI Projects with Unknown Data and Unknown Questions
Leverage data products, implement hybrid model deployment, and drive AI innovation by accessing unknown data with unknown questions for transformative advancements in your projects.
Managing Machine Learning Experiments with MLflow
Learn to manage ML experiments, reproduce pipelines, and deploy models using MLflow, an open-source tool for streamlining the machine learning lifecycle.
Train Your First NLP Transformer Model with Amazon SageMaker
Learn to train and deploy NLP Transformer models using Amazon SageMaker and Hugging Face. Gain hands-on experience with cutting-edge deep learning techniques for natural language processing.
MLOps That Works: Building ML Pipelines for Autonomous Factory Models
Explore MLOps strategies for autonomous factories. Learn design patterns and anti-patterns for deploying ML models in dynamic industrial environments using flexible pipelines.
DevOps for ML and Other Half-Truths - Processes and Tools for the ML Lifecycle
Explore processes and tools for enterprise-ready ML deployment, addressing the challenges of standardization in the ML lifecycle and supporting IT operations management.
Unify and Baseten - Boosting LLM Deployment
Supercharge LLM deployment by integrating Baseten model endpoints into Unify Platform. Learn dynamic routing, open-source model usage, and practical demonstrations for optimized AI workflows.
How Riot Games Uses Data to Maximize Engagement and Enjoyment
Explore data challenges in League of Legends, including model deployment, ML stack evolution, player-facing ML, and decision science at scale for improved gaming experiences.
Testing Models and Data in CI/CD Pipelines for Machine Learning
Explore best practices for testing ML models in CI/CD pipelines. Learn to validate models and data using deepchecks, addressing common pitfalls and ensuring continuous relevance and trustworthiness.
Custom ML Model Creation and Deployment Using AWS SageMaker
Deploy custom machine learning models efficiently with AWS SageMaker, streamlining the process from creation to production-ready implementation.