Made with cnvrg.io

Browse through real world examples of machine learning workflows, pipelines,
dashboards and other intelligent applications built with cnvrg.io.

Train and deploy using NVIDIA deep-learning containers

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.

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

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.

Train hundreds of models and show model comparison dashboard

Train hundreds of models and show model comparison dashboard

Train and track hundreds of ML models. Use cnvrg.io to automatically track all models in real-time. Useful for research and iterative development workflows, and also for production model comparison.

Deploy trained models with Canary Rollout and 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

Train multiple models and select the best one to deploy to 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

Build a recommendation system with Deep Learning and Kubernetes

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

Manage resources and increase utilization with CPU/GPU dashboards

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

Track & monitor predictions in production and trigger alerts/retraining

Track & monitor predictions in production and trigger alerts/retraining

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

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

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 cnvrg.io dashboard

Announcing CORE, a free ML Platform for the community to help data scientists focus more on data science and less on technical complexity

Download cnrvg CORE for Free

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