MATS Stack for Cross-System Orchestration of Machine Learning Pipelines
Explore the MATS stack for orchestrating ML pipelines across systems, featuring MLFlow, Airflow, Tensorflow, and Spark. Learn how Avast leverages this approach for efficient phishing detection and streamlined ML workflows.
ML Production Pipelines: Building and Deploying a Classification Model
Learn to build and deploy production-ready machine learning pipelines using Python, Databricks, and MLflow. Explore training and prediction workflows, model registration, and deployment options for both batch and on-demand processing.
Taking Deep Learning to Production with MLflow and RedisAI
Explore MLflow and RedisAI integration for efficient deep learning model deployment, featuring multi-framework support, auto-batching, and DAGing capabilities in a reliable runtime environment.
Productionalizing Models through CI/CD Design with MLflow
Explore MLflow integration with CI/CD for streamlined model deployment. Learn to design and implement automated pipelines using the model registry, enhancing efficiency in machine learning workflows.
Productionizing Machine Learning with Microservices Architecture
Learn to deploy ML models as microservices using Spark, enabling auto-scaling, versioning, and security. Discover how to aggregate real-time and historical data for inferencing and create actionable insights.
Delivery of Deep Transformer NLP Models Using MLflow and AWS SageMaker for Enterprise AI
Learn to deploy deep transformer NLP models using MLflow and AWS SageMaker for enterprise AI, covering the full machine learning lifecycle and CI/CD pipelines for production-quality implementation.
Consolidating MLOps at Schiphol Airport Using MLFlow
Explore how Schiphol Airport leverages MLFlow for rapid iteration, deployment, and monitoring of mission-critical machine learning models, enabling multiple weekly releases and efficient adaptation to changing dynamics.
Introduction to TinyML on Wio Terminal - Machine Learning on Microcontrollers
Introduction to TinyML on Wio Terminal: Learn to implement machine learning on microcontrollers using Arduino IDE, covering data gathering, processing, model training, and deployment.
How to Train and Deploy TinyML Models for Three Common Sensor Types
Learn to train and deploy tinyML models for sensor, audio, and vision data using Edge Impulse. Explore workflows, best practices, and optimization techniques for embedded machine learning applications.
Building a Magic Wand with TensorFlow Lite Micro - Tutorial
Learn to build an IMU-based gesture recognition model for Arduino, covering data gathering, training, and deployment using TensorFlow Lite Micro and CMSIS-NN library.
TinyML Talks - Speech-to-Intent Model Deployment to Low-Power Low-Footprint Devices
Explore techniques for training and deploying speech-to-intent models on low-power microcontrollers, enabling efficient voice control in resource-constrained devices.
Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps
Learn to quickly deploy deep learning models using Spark and TensorFlow, simplifying MLOps for data scientists. Explore distributed computing, feature engineering, and leveraging TensorFlow ecosystem libraries for efficient model production.
Federated Machine Learning with Kubernetes
Explore federated machine learning implementation on Kubernetes, enhancing data privacy and collaboration across distributed datasets without centralized storage.
Serving Machine Learning Models at Scale Using KServe
Explore KServe's Multi-Model Serving solution for scalable machine learning deployment. Learn design, implementation, and benchmarks for efficient model serving across frameworks.
Mastering LLM Delivery in Private Clouds: A Journey to Seamless Deployments with Kubernetes and OCI
Explore how Kubernetes and OCI artifacts simplify private LLM deployments, addressing data governance challenges and improving efficiency in artifact sharing between model developers and consumers.
Deploying Your Own Language Models - A Use Case Demo with Open Source Tools
Explore domain-specific language model adaptation and deployment using open source tools. Learn cost-effective techniques, optimization strategies, and ethical considerations for building powerful AI applications.
5 Steps to Deploy Cloud Native Sustainable Foundation AI Models
Explore deploying high-performance, energy-efficient AI foundation models on Kubernetes. Learn containerization, deployment, energy measurement, consumption reduction, and performance-energy tradeoffs.
Creating a Custom Serving Runtime in KServe ModelMesh - Hands-On Experience
Learn to create custom serving runtimes in KServe ModelMesh for ML model deployment. Explore key features, build container images, and run predictions using custom runtimes in this hands-on tutorial.
Build an Event-Driven Machine Learning Pipeline on Kubernetes
Explore building event-driven ML pipelines on Kubernetes, leveraging components for automated AI workflows, including model training, validation, serving, and operations with focus on eventing and pipeline CRDs.
End-to-end AI Marketplace with Execution Engine on Kubernetes
Explore an innovative AI marketplace solution leveraging Kubernetes for efficient execution, showcasing advanced integration of AI technologies in cloud-native environments.
Inference on KubeEdge
Explore KubeEdge for efficient edge computing inference with Adrian Gonzalez-Martin from Seldon. Learn about deploying and managing machine learning models at the network edge.