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A better managed workflow for hosting MLFlow on AWS
Dev-kit's managed services for hosting MLFlow sets up all required infrastructure to run MLFlow within your AWS account. You provide your AWS account and then can run and monitor your MLFlow service from the MLFlow UI, or programmatically through the MLFlow REST api.
User Interaction: The user interacts with the MLflow server via a web interface or API. This could be for creating new runs, logging parameters, or retrieving experiment details.
Metadata Management with RDS: When an experiment is created or updated, the metadata gets stored in RDS. This ensures that all experiment details, run histories, and parameters are quickly accessible.
Artifact Storage in S3: All artifacts, like trained models or datasets, are stored in a secure S3 bucket. This ensures high availability and durability of your crucial data.
Scalability MLFlow is hosted on ECS, allowing easy-to-use scaling as your needs grow.
Deploy MLFlow using scalable AWS Services
See what others are saying
With dev-kit's managed MLFlow, we've found our silver bullet! The easy-to-use interface means even our junior team members can navigate through complex tasks with confidence. Safety? Absolutely uncompromised. We've experienced bulletproof security, ensuring our data's integrity at every step. And here's the big part – the scalability. If you're looking for a solution that's new, easy, safe, and scalable, look no further.
As a machine learning engineer, I've encountered numerous tools and platforms throughout my career. But the Managed MLflow service by Dev-kit stands out. It's streamlined, efficient, and takes away all the infrastructural headaches I used to face. Now, I can focus purely on refining algorithms and improving models, knowing that the backend is taken care of. A game-changer for professionals in the ML space.
Have additional questions about MLFlow and the roadmap? Check out our support portal