At Baseten, the company co-founder started to solve issues faced by data science teams building machine learning models in companies of all sizes. They realized that despite everyone being on board with machine learning, most teams were ill-equipped due to skills, resources, and credibility issues. Baseten aimed to build a general-purpose toolkit to leverage data scientists' skills and turn them into full-stack shippers by providing modular pillars for deploying models, setting up backend infrastructure, and building UIs. The company quietly announced its public beta after 18 months of building, with the help of early users, and is now launching it to allow teams to build full-stack applications without needing to become engineers. Baseten offers a set of building blocks for each step of the journey, including model deployment, integration, design, and shipping, which can lower the cost of using machine learning. The company has raised over $20 million in funding led by Sarah Guo from Greylock Partners and is actively hiring for roles across engineering, design, and go-to-market to amplify and accelerate the impact of machine learning.