Changelog

You can now import Sagemaker models into BaseTen and use them in your applications. Head over to the Import model dialog on Models page to browse your deployed models on SageMaker and import the one you want.

For more details, please refer to this blog post.

The Filepicker component in the View Builder now supports uploading multiple files! Just select everything you’d like to upload when you click on Browse and watch the magic happen ✨

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Filepicker component in the View Builder

Note: files uploaded using the Filepicker are uploaded to S3 as public files and are therefore visible to anyone with the URL.

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We’ve incorporated an onboarding checklist into Baseten’s home screen for all users! If you’re a veteran Baseten user (thanks for the support, we ❤️ you), you can easily get rid of the checklist for good. But if you’re new to Baseten, fear not! This handy checklist has come to your rescue!

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Baseten checklist

Embedded links in each checklist item navigate you to key aspects of Baseten such as Applications, Models, and Views, making it easy to familiarize yourself with the product. And once you complete the onboarding process in your checklist, you can rejoice in being a certified Baseten wizard 🧙 and continue exploring all that we have to offer!

We've added BigQuery to the growing list of 3rd party data sources that are natively supported within BaseTen. To allow your BaseTen apps to read from and write to your BigQuery tables, first create a BigQuery data connection and provide it with a GCP service account in JSON:

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Edit data connection

Now that the data connection is created you can interact with BigQuery within your apps in a few different ways:

Within your worklet code you can:

• Execute SQL against BigQuery

• Get a bigquery.Client object and do everything the official BigQuery Python library allows

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Baseten and BigQuery

Within the query builder UI you can directly query BigQuery:

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Baseten and BigQuery

We've added a new Plotly charting component in the view builder. The component uses the Plotly JSON chart schema that can be modified in the view builder itself or can be populated from a worklet. The Plotly python library has been added to all environments. Build basic bar, line, and pie charts configuration or complex, customized visualizations.

We've added the ability for worklets to be invoked at a scheduled cadence. These scheduled runs work very similarly to cron and can be invoked hourly, daily, or weekly in whatever timezone you choose.

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Previously, when you logged into Baseten, you would land on the Applications page. While Applications will be one of the most common resources you visit, landing here made it nearly impossible to understand what else you could do within Baseten.

To provide a better understanding of what resources have been created and what’s possible in Baseten, we’ve introduced a new landing page. Here you will find your most recently created applications, models, and database tables, as well as shortcuts to create additional ones. We’ve also included a few quick links below, such as how to update your account information, add API keys, and more.

Please let us know if there’s something you find yourself doing frequently and we’ll do our best to add it!

To keep sensitive data like API keys and passwords secret, instead of including those in plaintext in code you can now use the secret manager to store them and retrieve them in code only when needed. To do so, open the secret manager from the settings menu

Once the secret data is there and given a name, you can retrieve the secret in your code using context.get_secret('my_secret_name').

Your Baseten applications and machine learning models are restricted to your organization — no one outside of your Baseten organization can access them.

However you can now explicitly share an application with the outside world. You do so through the Share UI on the application header.

Note: once you make an application public, all its views, worklets, and queries are publicly accessible.

Allowing you to write Python code without worrying about its execution environment is at the core of Baseten — no dockerfiles, no Kubernetes deployments, no Flask app boilerplate.

However this doesn't come at the expense of control. You now have full control over the Python environment that executes your code, whether you want to install a 3rd party package from PyPi or install a custom wheel.

See it in action below and read about it on our docs.