Connecting Data Scientists to Production with the Tempo Python SDK
Prepare and test machine learning models locally, then deploy them to production using Tempo Python SDK. Bridge the gap between data scientists and DevOps for faster, more reliable model deployment.
Productionize with Kubeflow Orchestration and Feast in GCP - Workshop
Develop a machine learning framework using Kubeflow, Feast, and Kafka in GCP. Learn to manage data features, serve endpoints, and monitor model performance with Grafana for real-time and batch predictions.
Creating Machine Learning Retraining Pipelines with Kubeflow and Kubernetes
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
From Concept to Production: Template for the Entire ML Journey - Part 1
Develop a production-ready ML pipeline from concept to deployment using Python. Gain hands-on experience with data prep, model training, and deployment on Google Cloud Platform.
From Concept to Production: Building a Production-Ready ML Pipeline - Part 2
Develop a production-ready ML pipeline from concept to deployment using Python, Kubeflow, and Google Cloud Platform. Master data prep, model training, and orchestration techniques.
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.
Parallelizing Your ETL with Dask on Kubeflow
Leverage Dask's parallelism capabilities on Kubeflow for advanced ETL processing. Learn to use the Dask Operator to optimize resource utilization in interactive Jupyter sessions and pipeline workflows.
Continuously Improving Large Scale Recommenders with MLOps Tools and Practices
Leverage MLOps tools and practices to operationalize and continuously improve recommendation systems at scale using NVIDIA Merlin and Kubeflow on Google Kubernetes Engine.
Scale and Accelerate Distributed Model Training in Kubernetes Clusters
Orchestrate and accelerate distributed deep learning workloads across multiple GPUs and nodes using Kubernetes, Kubeflow PytorchJob, RDMA, RoCE, and SRIOV for near-linear performance scaling.
Accelerating Deployment of AI with MLOps Orchestration
Explore MLOps orchestration to simplify AI deployment using Kubernetes and open-source tools. Learn to build efficient ML pipelines, from data preparation to model monitoring, accelerating business value across various use cases.
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.
MLOps on Highly Sensitive Data - Enhancing Security and Compliance
Enhance security and compliance in MLOps for sensitive data using Kubernetes, blockchain tokenization, and confidential computing. Learn from a life sciences case study.
Distributed TensorFlow - TensorFlow at O'Reilly AI Conference, San Francisco '18
Learn distributed TensorFlow training using Keras APIs, Kubeflow, and Kubernetes. Explore TensorFlow's architecture, set up clusters, and distribute models for efficient multi-GPU and multi-node performance.
Vivo's AI Computing Platform on Kubernetes
Vivo's AI platform leverages Kubernetes for efficient resource scheduling and high GPU utilization in large-scale model training and serving, addressing challenges in distributed AI computing.
Invisible Infiltration of AI Supply Chains: Protective Measures from Adversarial Actors
Explore protective measures against adversarial actors infiltrating AI supply chains. Learn to secure data, models, and pipelines, implement security controls, and mitigate risks in AI systems.
Introduction to Kubeflow and Machine Learning on Kubernetes
Comprehensive introduction to Kubeflow 1.3, covering its new features and guiding users through running data science workloads on Kubernetes. Includes hands-on examples and practical deployment techniques.
Introducing Kubeflow for Machine Learning and AI on Kubernetes
Explore Kubeflow for seamless machine learning model deployment, from laptop to production. Learn about its components, scalability benefits, and integration with tools like TensorFlow and Apache Spark.
Introducing Kubeflow with TensorFlow and Apache Spark
Explore Kubeflow's role in streamlining machine learning workflows, from model development to production deployment on Kubernetes, with insights on scalability and integration with TensorFlow and Apache Spark.
Build a Reproducible ML Workflow with Kubeflow Pipelines
Learn to create reproducible machine learning workflows using Kubeflow Pipelines, enhancing efficiency and scalability in ML projects with Google's open-source platform.
Tower of Babel - Making Apache Spark, Kubeflow, and Kubernetes Play Nice
Explore how Apache Spark, Kubeflow, and Kubernetes can work together to process big data matrices, denoise medical images, and create reproducible ML pipelines for COVID research and healthcare applications.
Jet Energy Corrections with GNN Regression using Kubeflow at CERN
Explore how Kubeflow enables graph neural network-based jet energy corrections at CERN's Large Hadron Collider, improving particle physics research through scalable machine learning workflows.