From Notebook to Kubernetes: Scaling GenAI Pipelines with ZenML
Explore a lightning talk demonstrating how ZenML, an open-source MLOps framework, facilitates the seamless transition from local development to cloud-scale deployment of generative AI pipelines. Follow a workflow that begins in a Jupyter notebook with lo…
Building Pipelines with Kubeflow - Hands-on Workshop
Discover Kubeflow pipelines: create environments, build and upload pipelines, and set up retraining schedules in this hands-on workshop for MLOps practitioners.
Intro to MLOps: Build, Deploy, and Retrain a Model in Production
Build, deploy, and retrain a production ML model using open-source tools. Learn robust MLOps pipeline techniques for quick model deployment and continuous improvement.
TensorFlow Extended - Machine Learning Pipelines and Model Understanding
Explore TFX for building production-ready ML pipelines. Learn data processing, model analysis, and versioning techniques for large datasets, enhancing your machine learning workflow.
TensorFlow Extended - TFX Overview and Pre-Training Workflow
Explore TensorFlow Extended's pre-training workflow, focusing on data validation and transformation for robust machine learning pipelines. Learn key components and best practices for efficient ML development.
Scaling AI at Autodesk with Ray and Metaflow
Explore a 15-minute conference talk from Ray Summit 2024 where Thomas Gale and Peter Meltzer from Autodesk, alongside Savin Goyal from Outerbounds, demonstrate how Autodesk leverages Ray and Metaflow for large-scale 3D generative AI operations. Learn abo…
Lessons Learned From Machine Learning Pipelines in Production
Explore practical insights on implementing machine learning pipelines in production environments, focusing on challenges, solutions, and best practices using Kedro framework.
MLOps MLFlow: Mlflow Projects - Databricks and MLFlow Pipelines
Aprende a crear pipelines de ejecución con MLflow Projects, utilizando Databricks como repositorio de artefactos y servidor de seguimiento para registrar toda la actividad.
MLOps MLFlow: Creating Pipelines with MLflow Projects - Part 1 (Español)
Aprende a crear pipelines de ejecución con MLflow Projects para optimizar tus flujos de trabajo en ciencia de datos y aprendizaje automático.
Managing Spatiotemporal Data Fusion at Scale Using The Geodesic Platform
Explore managing large-scale spatiotemporal data fusion using the Geodesic Platform. Learn about cloud-native solutions, data ingest challenges, and leveraging Pachyderm for traceability and pipeline management.
Data Microservices in Apache Spark Using Apache Arrow Flight
Explore efficient machine learning pipelines using Apache Arrow Flight in Spark, enabling faster data transport and simplified microservices for improved ML model production.
The Azure Machine Learning Service - Introduction
Explore Azure ML Service's features for scaling, automating, and deploying machine learning pipelines. Learn how it enhances the data science process and addresses key challenges.
Building ML Pipelines in JupyterLab Using Elyra - Without Writing Code
Learn to create ML pipelines visually in JupyterLab with Elyra, exploring pipeline properties, components, runtime environments, and remote execution options.
Transforming Jupyter Notebooks to Reproducible Machine Learning Pipelines with Kale
Learn to convert Jupyter notebooks into scalable Kubeflow pipelines using Kale, enabling seamless deployment of ML workflows on Kubernetes for improved reproducibility and efficiency.
Scheduling Jupyter Notebooks Using Airflow on Kubernetes
Learn to schedule Jupyter notebooks using Airflow on Kubernetes, enabling scalable data pipelines and machine learning workflows with enhanced security and resource management.
Building AI Pipelines with Hugging Face and Kedro - From ChatGPT to Production
Discover how to build robust AI systems using Hugging Face's open models and Kedro pipelines, moving beyond commercial AI to create customizable and transparent machine learning solutions.
Optimizing LLM Pipelines with DsPy - Part 2
Dive into optimizing LLM pipelines by implementing multi-hop question-answering with DsPy framework, featuring Baleen pipeline and Bootstrap Few Shot algorithm optimization techniques.
Towards Observability for Machine Learning Pipelines
Explore end-to-end observability for ML pipelines. Learn about a system that enhances visibility, executes tests, tracks data flow, and enables post-hoc analysis of pipeline health.
A Visual Introduction to Modern and Scalable Machine Learning Pipeline
Visual guide to modern, scalable machine learning pipelines: from data prep to deployment. Covers key stages like training, validation, inference, feature stores, and model registry. Ideal for understanding ML infrastructure evolution.
Defense Against the Dark Arts
Overview of adversarial example research in machine learning, exploring attacks, defenses, and future directions for enhancing AI security and robustness against malicious inputs.
Building Real-Time ML Pipelines the Easy Way
Learn to build real-time ML pipelines for fraud prediction, predictive maintenance, and customer churn prevention. Explore challenges, solutions, and best practices for handling high-velocity data and triggering actions in seconds.