MLOps platform to train, track, deploy, and monitor machine learning models
iko.ai
iko.ai
We are a boutique conultancy that has profitably carried out machine learning projects in different sectors and industries. We initially started building iko for internal because our projects took a toll on us.
- No-setup, fresh, notebook environments with the most popular libraries pre-installed.
- Real-time collaborative notebooks to see your teammates' changes live. Pair program, troubleshoot, and prototype together.
- Multiple notebook versions
- Leverage GPUs and schedule long-running notebooks that survive closed browsers and network disruptions. Watch your notebook's output as it runs from multiple devices.
- Automatic experiment tracking to detect your models, parameters, and metrics and saves them without you remembering to do so or pollute your notebook with tracking code. Know which parameters produced which model on which data.
- Easily deploy your model and get a "REST endpoint" so data scientists don't tap on anyone's shoulder to deploy their model, and developers can use the models without being dragged into the ML realm. You also can invoke it by entering data or uploading a CSV file.
- Build a Docker image for your model and push it to a registry (DockerHub or GitLab for now) to use it wherever you want
- Monitor your models' performance on a live dashboard and know if your model is losing its predictive power.
- Publish notebooks as AppBooks: automatically parametrize a notebook to enable clients to interact with it without being overwhelmed by code, or exporting as PDF, or building an application, or mutating the notebook.
More on our roadmap. We're only focusing on actual problems we have faced serving our clients, and problems we are facing now. This is not a "startup idea"; we're building what we need but we'd love to hear your thoughts and problems you have faced we may not be familiar with.