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.
Writing Machine Learning Pipelines Against Object Storage
Learn to create efficient machine learning pipelines using object storage, optimizing data processing and model deployment for scalable AI solutions.
Enforcing Data Quality in Data Processing and ML Pipelines with Flyte and Pandera
Learn to enforce data quality in processing and ML pipelines using Flyte and Pandera, enhancing reliability and efficiency in data-driven projects.
Democratization of ML Pipelines and Bringing ML Workflows to Heterogeneous Cloud Native Platforms
Explore advancements in ML pipeline portability using Intermediate Representation, enabling cross-platform compatibility and democratizing ML workflow development with enhanced features and registry protocols.
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.
MLOps MLFlow: Building Pipelines with MLflow Projects
Build MLflow project pipelines for efficient machine learning workflows. Learn to create and execute data science pipelines using MLflow Projects.
Computer Vision Workshop on OpenShift: AI/ML Platform for Model Development and Deployment
Explore computer vision use cases on OpenShift, from data collection and annotation to model training and deployment, with practical demonstrations on cars and trains detection.
Scaling ML Pipelines with KALE - The Kubeflow Automated Pipeline Engine
Discover KALE, the Kubeflow Automated Pipeline Engine, for scaling ML pipelines. Learn how to transform Jupyter Notebook experiments into production-grade pipelines for efficient model training and real-time serving.
Production Model Deployment
Explore strategies for deploying production models, including data sharing, service building, and AB testing. Learn about ML pipelines, engineering requirements, and effective handoff between data scientists and software engineers.
Reproducible and Maintainable Data Science Code with Kedro
Discover how Kedro, an open-source Python framework, transforms experimental data science code into production-ready ML pipelines, emphasizing reproducibility, maintainability, and modularity.
Auto-Sklearn - Automated Machine Learning in Python
Discover Auto-sklearn, an open-source AutoML tool that simplifies machine learning pipeline development. Learn its functionality, benefits, and how to implement it as a scikit-learn replacement.
We Build a ML Pipeline After We Deploy
Discover essential strategies for building end-to-end machine learning pipelines, covering automation, engineering, debugging, and monitoring using Python libraries.
The State of Production Machine Learning in 2023
Explore challenges and solutions in production machine learning, focusing on reliability and scalability. Learn key concepts and recommended tools to tackle the increasing complexity of ML pipelines.
Machine Learning Pipelines con K3s y Argo
Aprende a implementar pipelines de Machine Learning utilizando K3s y Argo, optimizando el flujo de trabajo en entornos cloud-native.
MLOps with MLflow - Creating Execution Pipelines Using Projects and Databricks
Explore MLflow Projects to create execution pipelines locally and leverage Databricks for tracking and artifact storage in machine learning workflows.
TFX- Production ML Pipelines with TensorFlow
Explore TensorFlow Extended (TFX) for production ML pipelines, covering components, metadata storage, orchestration, and custom implementations for scalable and maintainable machine learning solutions.
Azure ML: Pipelines with Python SDK for Data Science and Machine Learning
Create ML pipelines with Azure Machine Learning Studio's Python SDK for efficient data science and machine learning workflows on Azure.
Building Machine Learning Pipelines with Kubeflow
Master building ML pipelines using Kubeflow's components, from key considerations to best practices, and learn effective implementation strategies for scaling machine learning projects.