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Made with Intel® Tiber™ AI Studio

Browse through real world examples of machine learning workflows, pipelines,
dashboards and other intelligent applications built with Intel® Tiber™ AI Studio.

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Train and deploy using NVIDIA deep-learning containers
  • S3
  • GitHub
  • NVIDIA GPUs
  • Python

Train and deploy using NVIDIA deep-learning containers

Load data from S3 object storage, train with both TensorFlow and PyTorch deep-learning containers on NVIDIA GPUs, pick champion model and deploy to a production endpoint.

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Train models on versioned data and create GitHub PR with champion model
  • PostgreSQL
  • GitHub
  • CPU + NVIDIA GPUs
  • Docker

Train models on versioned data and create GitHub PR with champion model

Load and version data from PostgreSQL. Run through a preprocessing pipeline using Apache Spark on Kubernetes, train with multiple models with hyperparameter optimization for each. Compare metrics, pick top performing model and open a GitHub Pull-Request.

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Train hundreds of models and show model comparison dashboard
  • NVIDIA GPUs
  • Python
  • Dashboards

Train hundreds of models and show model comparison dashboard

Train and track hundreds of ML models. Use Intel® Tiber™ AI Studio to automatically track all models in real-time. Useful for research and iterative development workflows, and also for production model comparison.

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Deploy trained models with Canary Rollout and A/B testing
  • Azure Blob Storage
  • GitHub
  • CPU
  • A/B Testing

Deploy trained models with Canary Rollout and A/B testing

Automatic ML pipeline that trains models continuously based on new data snapshots, uses Canary Rollout to build a Champion/Challenger mechanism with auto traffic routing, validation and advanced conditions and rollback option for safe deployment

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Train multiple models and select the best one to deploy to SageMaker
  • PostgreSQL
  • GitHub
  • NVIDIA GPUs
  • AWS SageMaker

Train multiple models and select the best one to deploy to SageMaker

Build a pipeline that reads data from PostgreSQL, enrich it with external data sources (weather, holidays) and train two models with hyperparameter optimization - select the top model based on custom metrics and deploy it as to production using SageMaker

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Build a recommendation system with Deep Learning and Kubernetes
  • PostgreSQL
  • GitHub
  • NVIDIA GPUs
  • Python

Build a recommendation system with Deep Learning and Kubernetes

Combine data from S3 and BigQuery, preprocess using Spark on Kubernetes and use TensorFlow Ranking/XGBoost to build a recommendation system and deploy it as a REST API on Kubernetes

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Manage resources and increase utilization with CPU/GPU dashboards
  • PostgreSQL
  • GitHub
  • NVIDIA GPUs
  • Python

Manage resources and increase utilization with CPU/GPU dashboards

Connect all your GPUs, CPUs and compute resources to a single, unified environment (with cloud-bursting built-in) - monitor and see in-depth analysis of usage, utilization and consumption of compute in your ML workloads

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Track & monitor predictions in production and trigger alerts/retraining
  • GitHub
  • Cloud Bursting
  • Python

Track & monitor predictions in production and trigger alerts/retraining

Automatically log all predictions in a scalable and Kubernetes-based environment, use Intel® Tiber™ AI Studio to monitor each sample; both input and prediction. Identify anomalies, monitor model decay, data correlation and trigger retraining/alerts automatically

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Launch OpenMPI, Horovod and distributed deep-learning jobs in a single click
  • OpenMPI
  • NVIDIA GPUs
  • Python

Launch OpenMPI, Horovod and distributed deep-learning jobs in a single click

Launch OpenMPI jobs on any multi-node Kubernetes cluster (cloud/on-prem) in a single click. Use the built-in Kubeflow MPI operator to run your Horovord / TensorFlow distributed training and track performance in real-time using the Intel® Tiber™ AI Studio dashboard

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