Hi, we’re Eduardo and Ido, the founders of Ploomber. We’re launching Ploomber Cloud today, a service that allows data scientists to scale their work from their laptops to the cloud. Our open-source users (https://github.com/ploomber/ploo...) usually start their work on their laptops; however, often, their local environment falls short, and they need more resources. Typical use cases run out of memory or optimize models to squeeze out the best performance. Ploomber Cloud eases this transition by allowing users to quickly move their existing projects into the cloud without extra configurations. Furthermore, users can request custom resources for specific tasks (vCPUs, GPUs, RAM). Both of us experienced this challenge firsthand. Analysis usually starts in a local notebook or script. Whenever we wanted to run our code on a larger infrastructure, we had to refactor the code (i.e., rewrite our notebooks using Kubeflow’s SDK) and add a bunch of cloud configurations. Ploomber Cloud is a lot simpler; if your notebook or script runs locally, you can run it in the cloud with no code changes or extra configuration. Furthermore, you can go back and forth between your local/interactive environment and the cloud. Users only need to declare their dependencies via a requirement.txt file, and Ploomber Cloud will run all experiments. Our framework makes it simple to run hundreds of experiments simultaneously. Since data science experiments are iterative, we keep track of source code changes. If it hasn’t changed for a pipeline task, we use cached artifacts and skip redundant computations, severely cutting costs, especially for pipelines that require GPUs. You can sign up to Ploomber Cloud for free and get started quickly. We made a significant effort to simplify the experience (https://docs.ploomber.io/en/late...). There are three plans (https://ploomber.io/cloud/): the first is the Community plan, which is free with limited computing. The Teams plan has a flat $50 monthly and usage-based billing, and the Enterprise plan includes SLAs and custom pricing. We’re thrilled to share Ploomber Cloud with you! So if you’re a data scientist who has experienced these endless cycles of getting a machine and going through an ops team, an ML engineer who helps data scientists scale their work, or you have any feedback, please share your thoughts! We love discussing these problems since exchanging ideas sparks exciting discussions and brings our attention to issues we haven’t considered before! You may also reach out to me at eduardo@ploomber.io