Cerebrium is a platform to deploy machine learning models to serverless GPUs with sub-5 second cold-start times. Customers typically experience 40% in cost savings when compared to using traditional cloud providers and can scale models to more than 10K requests per minute with minimal engineering overhead. Simply write your code in Python and Cerebrium takes care of all infrastructure and scaling. Cerebrium is being used by companies and engineers from Twilio, Rudderstack, Matterport and many more.
Michael is a South African entrepreneur that has founded multiple businesses across ML & A.I, Blockchain, Retail, and consumer marketplaces. Previously he was the CTO of OneCart which sold to Walmart/Massmart for an undisclosed sum. Currently, he is a co-founder at Cerebrium, a machine learning platform that makes it easier for businesses to train, deploy and monitor ML models in production.
Co-founder & CTO of Cerebrium 8+ years experience as a Javascript developer including a lot of time spent in a lead developer role. BComm undergrad and Finance Honours at the University of Cape Town. Data Science at Tilburg University.
Cerebrium is a machine learning framework that makes it easy to train, deploy and monitor ML models. You can:
Hi Everyone! We’re Michael and Jonathan and we are so excited to introduce you to Cerebrium!
Implementing machine learning based applications in a business remains challenging. You have multiple infrastructure concerns, such as expensive GPU’s, scaling issues, fragmented tooling while additionally also struggle to find machine learning talent and keep up with the latest research and techniques. These barriers to entry prohibit smaller companies from implementing machine learning into their business sooner.
Cerebrium helps teams easily train, deploy and monitor machine learning models in production. Using our framework, you can train and deploy models (custom built or LLM’s) to serverless CPU’s/GPU’s using one line of code. You are only charged for inference time and we automatically handle the scaling and the versioning of models.
When it comes to training and/or inference, we use the latest research techniques to reduce training and inference times by 20-40%. You just focus on adding value to your use case and we will take care of the rest!
At our previous business, machine learning made a huge impact across various teams but it took months of building, testing and experimenting to get it there. We wanted to make the same sort of impact accessible to companies who don’t have the resources or know-how - and of course make it available in a fraction of the time