As you’ve advanced the development of your Large Language Models or other generative models, have you run into infrastructure limitations? Scaling generative AI can be resource-intensive, requiring significant compute capacity and advanced data architecture.

Mission Cloud has worked with several leading companies who are creating their own generative models using text, audio, and images. With our deep AWS expertise, we’ve helped them to design the proper AWS infrastructure for their models and to handle training datasets in excess of 50 terabytes.

How We Work With You

We work backward with you to design your infrastructure based on AWS best practices and utilize common tooling like Amazon FSx for Lustre, AWS Sagemaker, AWS Sagemaker Pipelines, MLFlow, and others. We can also expedite the journey of your models from on premises or another cloud provider into the AWS ecosystem.

From there, we’ll develop a best-fit architectural approach and design everything from model stores, experimentation, endpoint deployment, pipelines, and the data infrastructure. And as a part of every engagement, we document this system from top to bottom and conduct extensive knowledge transfer so that your team can maintain it into the future.