The Killer Feature Store - Orchestrating Spark ML Pipelines and MLflow for Production
Explore feature store implementation as an orchestration engine for ML pipeline stages using Spark and MLflow, focusing on deployment management, A/B testing, discovery, and governance.
Tuning Machine Learning Models - Scaling, Workflows, and Architecture
Automating and scaling ML model tuning using Hyperopt and Apache Spark. Best practices for workflows, architecture, and optimization in hyperparameter tuning for improved performance and accuracy.
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.
Scaling Up AI Research to Production with PyTorch and MLFlow
Explore PyTorch's latest advancements in AI research and production, including distributed training, model optimization, and deployment using MLFlow, with insights on scaling and efficiency.
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.
Automated and Explainable Deep Learning for Clinical Language Understanding
Explore advanced deep learning techniques for clinical NLP, including automated model training, explainable AI, and scalable architectures to extract critical healthcare insights from unstructured medical notes.
Productionizing Deep Reinforcement Learning with Spark and MLflow
Aprenda a aplicar Deep Reinforcement Learning em escala usando Spark e MLflow para personalização de jogos e aumento de engajamento, com dicas práticas para superar desafios de produção.
Data Versioning and Reproducible ML with DVC and MLflow
Learn to enhance ML reproducibility using DVC and MLflow. Explore data versioning, model tracking, and artifact management for efficient ML development and deployment.
Testing ML Models in Production - Detecting Data and Concept Drift
Explore robust ML model testing in production using MLflow, SciPy, and statsmodels. Learn key statistical tests, metrics for drift detection, and gain practical insights for maintaining model effectiveness.
Productionizing ML Models Using MLflow Model Serving
Explore MLflow Model Serving for efficient ML model deployment, consumption, and monitoring. Learn to deploy versions, validate, connect to Power BI, and manage access rights.
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.
MLflow Model Serving - Methods and Best Practices
Explore MLflow model serving techniques, including batch and real-time scoring, open-source and Databricks-managed options, with emphasis on new production-ready model serving features.
Monitoring ML Models, IoT Data, and Quality Checks on Delta Lake - Quby's Approach
Discover how Quby monitors IoT data, ML models, and data quality using Databricks. Learn about Delta Lake, MLflow, and Slack integration for efficient data processing and model performance tracking.
Machine Learning with Kubernetes
Explore MLOps with Kubernetes: Learn to build scalable ML pipelines using Kubeflow, optimize cluster design, and leverage Istio for enhanced observability and security in production environments.
Building Robust ML Production Systems Using OSS Tools for Continuous Delivery for ML
Learn to build robust ML production systems using open-source tools for continuous delivery, including workflow orchestration, data versioning, and model deployment and monitoring.
End-to-End Deep Learning Project: Kidney Disease Classification with MLflow, DVC, and Deployment
Comprehensive deep learning project on kidney disease classification, covering data ingestion, model preparation, training, evaluation, MLflow integration, DVC pipeline, and AWS deployment.
GenOps: Building an MLOps Platform to Support GenAI Workloads with Open-Source and Kubeflow
Explore building an end-to-end MLOps platform on GKE using open-source tools like Kubeflow to support Generative AI models, with practical tips and a demo for leveraging these technologies.
Accountability Taxonomy for AI Software Bill of Materials
Explore AI accountability through SBOM concepts, focusing on EU AI Act requirements and related US bills. Learn about a taxonomy for system trustworthiness and its practical application in MLOps.
Deploy and Monitor ML Pipelines with Open Source and Free Applications
Learn to deploy and monitor ML pipelines using open-source tools. Automate data refresh, generate forecasts, and set up monitoring dashboards with GitHub Actions, Docker, MLflow, and more.
Building Reproducible ML Processes with an Open Source Stack
Explore open-source tools for reproducible machine learning, covering code, data, and environment management. Includes hands-on demonstration of experiment replication using KubeFlow, Git, Minio, and lakeFS.
Optimizing Kubernetes Cluster Scaling for Advanced Generative Models
Explore advanced techniques for scaling Kubernetes clusters to support complex generative models, leveraging tools like Flyte, MLflow, and Kubeflow for efficient deployment and management.