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Machine learning techniques are becoming increasingly common and widely used due to lower processing power, easier access to training models, and developing knowledge of how and where they might be used. However, when it comes to implementing, integrating, and deploying machine learning models for access to a broader range of users, developers and organizations need a lot of technical knowledge and expertise, which slows down the process.
Baseten is solving this problem with its data science and machine learning team’s ML Application Builder. It makes it easier to set up backends, frontends, and MLOps to utilize ML models sooner. Baseten enables serving machine learning models, integrating with custom business logic, and designing powerful web apps for business customers simply, all without requiring any infrastructure or requiring the usage of React or JS.
It can provide full-stack apps in a matter of minutes. It allows one to deploy models fast, construct API endpoints automatically, and quickly design UI using drag-and-drop components. One wouldn’t need to be a DevOps engineer to get models into production. With Baseten, anyone can serve, manage, and monitor models in seconds using only a few lines of Python.
It can also compose business logic around a model and synchronize data sources without infrastructure difficulties. Baseten takes care of the tool-building process, allowing us to focus on our core competencies of modeling, measurement, and problem-solving.
Baseten has raised $20 million in seed and Series A funding. The company will use these funds for the expansion of its engineering and go-to-market teams.