Production-grade MLOps Platforms to help organize Machine Learning Life Cycles
Production-grade MLOps Platforms to help organize Machine Learning Life Cycles


Recent years have seen tremendous advances in state of the art machine learning models in domains such as speech, text & images. These deep learning models are extremely demanding on computational resources & require a new set of tools and processes for companies to take advantage of. Success depends on the scale of experimentation — running more experiments equals better results & teams need to be able to iterate across training these large demanding models and do so in a reproducible fashion. Remco is AE at Paperspace working on Gradient, which is a serverless platform that makes it simple and fast to run machine learning and deep learning workloads of any scale and complexity on any infrastructure. He will outline the workflow of productive machine learning organizations so they can decrease time to market in implementing AI-enabled solutions across the enterprise.


Worked at Intel in Gdańsk on Hadoop clusters
Worked at Waymo in San Jose on Self driving car projects helping with setting up pipelines for ML scientists
Clusterone Co-Founder and Tech Lead - Created the architecture and implemented initial POC CEO
Lead Developer and architect of Edziennik Application for Polish Bar Association.
MS. Computer Science
Kubernetes expert

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google