UbiOps, leading platform for deploying and scaling Artificial Intelligence (AI) and Machine Learning (ML) models, is proud to introduce advanced functionality for training AI models in the cloud. This development allows businesses to manage even more of their AI development lifecycle on the UbiOps platform and also leverage Generative AI faster.
Training and fine-tuning AI models is critical to data science, AI, and ML development. However, it requires significant computing power and large amounts of data. Previously, companies had to set up complex infrastructure to run training and inferencing workloads in the cloud. Now, with the growing popularity of computing intensive Large Language Models (LLMs), access to specialized hardware is of crucial importance. However, with UbiOps’ newly added functionality, training and deploying models on the platform has become more accessible than ever.
“We are proud to announce that UbiOps now also offers the ability to train AI models in the cloud, enabling companies to streamline their development cycle and operate more efficiently,” said Victor Pereboom, CTO of UbiOps. “Our platform now makes it possible not only to manage and run existing AI models live but also to train and develop new models, all in one place.”
Managing AI models in the field, known as Machine Learning Operations (MLOps), is a critical step in the development process of AI solutions. Training AI is an essential part of this. For example, before a model like ChatGPT is fully functional, it first needs to process large amounts of historical data. UbiOps makes this process faster by running training jobs on powerful cloud hardware, making it easier for businesses to scale and get results faster.
This new functionality has direct benefits for many companies developing AI applications, including those based on Foundational Models, LLMs and Generative AI. Training and improving models based on massive amounts of global data is essential to businesses across many industries like Healthcare, Agriculture, Finance, and Manufacturing.
“It is often difficult for many companies to move large AI workloads to the cloud because of complex infrastructure, limited availability of accelerator hardware like GPUs, and high costs. We can now help these teams with a turn-key solution which saves tons of development time and cloud costs,” adds Victor Pereboom. “This innovation enables companies to build AI solutions more efficiently and effectively in a world where AI is increasingly important.”
