Emmiliese von ClemmEmmiliese von Clemm|

One year ago we were a team of 6 working to launch the first version of our product to a small number of beta users. Now, after a year spent building with and learning from the customers in our closed beta, growing our team to 15 people (and counting!), and making lots of product additions and improvements, we’re gearing up for a broader launch. More on that soon 🤫

Alex GillmorAlex Gillmor|

Learn how to use scaffolds, the technology that underpins the model serving experience on BaseTen. Use scaffolds to test and iterate on your model servers locally before deploying on BaseTen and so much more.

Pankaj GuptaPankaj Gupta|

Learn how a pre-trained model zoo model, wav2vec, was incorporated into a user-facing audio transcription application, currently being used by the content moderation team at a large consumer tech startup.

Phil HowesPhil Howes|

Get your project off to a flying start by using a state-of-the-art pre-trained model as your initial benchmark. Today, the BaseTen model zoo includes 17 state-of-the-art models that solve common image, text, and audio tasks and can be used in projects right away. You can deploy these models and start using them in applications in a matter of minutes.

Emmiliese von ClemmEmmiliese von Clemm|

Welcome to BaseTen’s very first monthly update. We’ll share these updates here on our blog and through our email newsletter—join our waitlist to get the latest product improvements, blog posts, and company updates delivered directly to your inbox.

Amir HaghighatAmir Haghighat|

Working closely with users in our closed beta, we’ve repeatedly heard that users need to label data to train their models, often before they even have a model. BaseTen helps here too! Data scientists can use BaseTen to quickly build an API and craft UI for a user-facing data-labeling app, without first deploying a model with BaseTen. In this post, we’ll build a simple image labeling app using BaseTen.

Alex GillmorAlex Gillmor|

BaseTen supports deploying most types of machine learning models; in this post, we’ll go over deploying a simple fast.ai model.

Pankaj GuptaPankaj Gupta|

Since BaseTen launched, most of our users have been deploying machine learning models through our model deployment service because of its ease-of-use, extensibility, and scalability. However we’ve heard from some people that they are already locked into SageMaker for model deployment, yet still want to use BaseTen to create user-facing applications for their SageMaker models. Today we’re making that possible by launching a native SageMaker integration in BaseTen.

Tuhin SrivastavaTuhin Srivastava|

Over the last decade, our team has used machine learning to solve problems across organizations big and small, all the way from framing problems and ideating to putting the power of models directly in the hands of users. We’ve seen amazing ML-driven outcomes in healthcare, the creator economy, and business operations, and become convinced that machine learning is poised to have a profound impact on the way we live and work.