Store up to 100Unlimited Runs
- Public Hub, Private Runners
- Bring Your Own Compute
- 100GB Storage
- Run Tracking
- Provenance Graph
- Metric Explorer
- Community Office Hours
At a time when remote working is becoming the new norm, we are opening up the SaaS version of our MLOps platform for free. Our collaboration tools can help ML teams be more effective when working remotely. If you need to host it yourself, get in touch.
Do good by your friends, family and loved ones. And then do good MLOps from home.
— with ♥ from the Dotscience team.
Setting up your development environment, keeping data in sync across a bunch of machines, reproducing a colleague's result; lots of time is wasted in typical ML projects.
Data scientists often avoid collaborating because it's difficult with current tools, but the benefits of avoiding siloes are well documented.
If you are tracking results of experiments, or mappings from data to models in a spreadsheet, you're likely to make mistakes and not capture the full picture.
By overwriting data or emailing notebooks you're creating future risk of not being able to fix a model that is buggy.
Without automated tests it's easy to accidentally deploy broken models into production, as described in What's your ML test score?
If you're manually deploying models into production by copying files, you risk losing track of what's running where and how it was created.
If you're not testing model drift and statistical behavior in production, your models could go haywire and you might not notice until real damage has been done.
As Google describes in The High Interest Credit Card of Technical Debt, ML systems are particularly susceptible to new and interesting forms of tech debt.
These issues make ML projects fail and create financial and reputational risk. Solving them promises more productive, effective AI teams and better and safer models.
“The processes and tools for collaborating and maintaining ML projects at industrial scale is not yet as mature as for traditional software projects. The ML workflows pose several additional challenges that doesn’t perfectly fit into Software DevOps processes. I am excited to work with Dotscience to tackle these challenges in our upcoming project, as they are actively focused on making collaboration structured and centralised so that it scales to much larger team and project sizes.”Anders Åström, Datascience Manager at a global Technology Consulting firm