SeedCI is designed to make the machine learning pipeline process faster, more deterministic, and easier to integrate in to your existing Git-based workflow.
Focus on building models spend less time managing infrastructure with our easy-to-use and familiar interface and CLI.
SeedCI provides effortless infrastructure and a software stack for model development, collaboration, and deployment.
Load and explore data, develop models, and run experiments with Jupyter Notebooks and web interface. Install the CLI and our Python SDK for more advanced model development.
Train models on a single instance or scale up with distributed training. Run individuals jobs or a hyperparameter sweep using our CLI or python SDK.
Store and catalog your models in an easy-to-use interface. Log and graph your model metrics such as loss and accuracy. Track your model performance over time.
Easily deploy your models as API endpoint in seconds. Scale your deployment to respond to request volume. Deploy on GPUs or CPU instances.
Control your data, from datasets to artifacts Easily version control and track the complete evolution of your models with datasets, hyperparameters, data sources, and code.
Train, tune, and deploy models 10x faster Run, rack and visualize your work across experiments, notebooks, saved models, deployments (model serving) faster and with greater confidence.
Unprecedented visibility & control of your compute resources Train in parallel and scale deployed models, no DevOps required. Manage your cloud, on-prem, or hybrid compute resources as a single environment.
Start working with SeedCI to automate your ML pipeline.
Simply add our .config file to your github repository and change the .yml file and customize your pipeline.
An open-source command-line tool for executing Jobs from Windows, Mac, or Linux.
SeedCI makes it easy to deploy your trained model into production so that you can start generating predictions for real-time or batch data. Just specify the type of instance and the autoscaling behavior and SeedCI takes care of the rest. SeedCI will launch the instances, deploy your model, and set up the secure HTTPS endpoint for your application.
One of the most important reasons to use SeedCI is that you can scale instantly. Train in parallel and scale deployed models without any DevOps required. 1-click distributed training and hyperparemeters makes is so much easier and cost efficient to automate ML infrastructure.
Yes, We support most popular CI CD services like JenkinsCI and CircleCI to provide the most conviniect experience to our customers. You can simply integrate our service with your already existing CI CD services.
Simple, flexible pricing. Save time and money with SeedCI. Our service would be free of charge for open source projects and university students