We are thrilled to unveil Qwak Workspaces - managed Jupyter Notebooks on Qwak.
Qwak Workspaces combine the power of Jupyter Notebooks with the convenience of managed environments, allowing data science teams to innovate faster on their machine learning projects.
Provision a fully managed Jupyter Notebook in a few clicks on either GPU or CPU machines, freeing up valuable time to focus on what matters most.
Simplicity: Qwak Workspaces take the pain out of maintaining Jupyter environments.
Production-Ready: Experiment with models and deploy to production at any scale.
Collaboration: Collaborate and share notebooks with your team.
Customization: Select from a range of CPU and GPU instances tailored for your requirements.
Qwak Workspaces bridge the gap between research and production. By using a simple command, you can deploy your models at any scale on Qwak directly from your notebook.
Prediction timers help troubleshoot model prediction latency, measure prediction times, find bottlenecks, and optimize performance. Configure multiple timers to have more visibility on the Latency breakdown graph for your production models.
Read more in our official documentation ->
Updating historical data for features can be tough when dealing with large datasets. The new Backfill API makes this process much easier. Just pick the dates you need, run a Backfill job, and let the feature store handle the data update for you.
Say goodbye to complex MLOps from building, training and deploying models, at any scale you need.