Critical Use of MLOps in Finance Using Cloud-Managed ML Services That Scale
Leverage MLOps to improve quality and consistency of machine learning solutions in finance. Accelerate model experimentation, deployment, and tracking using cloud-managed services for efficient scaling.
MLOps Beyond Training - Simplifying and Automating the Operational Pipeline
Explore a production-first approach to MLOps, automating operational pipelines for measurable ROI. Learn to design continuous workflows, integrate components, and simplify the transition from research to scalable production.
Hands-on Workshop: Create Machine Learning Retraining Pipelines
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
UnionML: A Microframework for Building Machine Learning Applications
Explore UnionML, a microframework simplifying ML application development. Learn to streamline the process from research to production, addressing common challenges in the ML lifecycle.
A Guide to Building a Continuous MLOps Stack with GCP and Superwise
Dive into MLOps CI/CD pipeline automation with GCP and Superwise. Learn to build, deploy, and monitor continuous ML pipelines, focusing on automations and production-first insights for issue detection and resolution.
Introduction to Model Deployment with Ray Serve
Learn to deploy ML models using Ray Serve. Explore Ray Core APIs, create scalable architectures, and integrate with MLflow and FastAPI. Gain hands-on experience in distributed computing and model deployment.
Cloudera How To Videos
Comprehensive video series covering Cloudera's data platform, including security, governance, analytics, deployment, and management topics for enterprise-level data solutions.
Flask Python
Learn to deploy machine learning models using Flask, a Python web framework. Explore Flask's capabilities, advantages, and applications in this comprehensive tutorial.
Amazon SageMaker
Learn to build, train, and deploy ML models using AWS SageMaker, covering notebook instances, S3 buckets, XGBoost algorithm, and endpoint predictions.
Azure Percept - AI IoT Edge Made Easy - Part 1
Explore Azure Percept DK for IoT edge AI: hardware overview, model building, and deployment demos. Hands-on tutorials for object detection and bird classification using Azure Percept Studio.
Understanding the Machine Learning Process and Embedding Models into Apps
Learn to build ML models with Azure ML and VS Code, operationalize them in apps, and implement MLOps practices. Covers PyTorch Lightning, model prediction, and integration with DevOps and Kubernetes.
Get Started with PyTorch
Explore PyTorch's fundamentals, from data handling to model building and deployment. Learn about its unique features, strong community, and ecosystem for effective deep learning development.
High-Level Introduction to MLOps - AI Show Episode 33
Microsoft data scientists discuss MLOps challenges, principles, and best practices, offering insights on successful implementation and tips for overcoming common obstacles in AI projects.
Hugging Face on Azure - Partnership and Solutions Announcement
Explore Hugging Face's integration with Azure, featuring state-of-the-art ML models, enterprise capabilities, and open-source collaboration for responsible machine learning.
YOLO V3 - Robust Deep Learning Object Detection Tutorials
Learn to implement YOLO v3 for robust object detection, from web scraping and dataset annotation to model training and deployment, using OpenCV and Python.
Amazon SageMaker Technical Deep Dive Series
Comprehensive deep dive into Amazon SageMaker, covering notebook instances, algorithms, custom models, training, deployment, tuning, and advanced features for efficient machine learning workflows.
R Data Science Project
Learn practical R data science skills through hands-on tutorials covering exploratory analysis, machine learning, data visualization, and building interactive web applications with Shiny.
End to End Machine Learning Project Implementation with Dockers, GitHub Actions and Deployment
Comprehensive guide to implementing a machine learning project, covering data analysis, model training, web application development, and deployment using Docker and GitHub Actions.
MLOps Crash Course for Beginners
Learn to create and deploy reproducible MLOps pipelines, covering key concepts like continuous training, drift detection, and model deployment for real-world data science value beyond Jupyter notebooks.
Streamlining AI Workflows with Apache Airflow and Ray
Discover how to build a unified AI infrastructure by integrating Apache Airflow and Ray for seamless orchestration of data processing, model training, and inference workflows, including LLM fine-tuning and LoRA deployments.
Building Intelligent AI Infrastructure with ORI - Dynamic Query Routing and Model Management
Discover how ORI dynamically routes AI queries to optimal language models, featuring chain atomic queries, benchmark matching, and ethical frameworks integrated with Ray Serve and vLLM for high-speed inference.