Aditya Bindal

Foundation models with hundreds of billions of parameters activate new applications for machine learning. Models such as OPT-175B, BLOOM, Jurassic, GPT-3, and DALL-E demonstrate exciting new use cases for text and image generation, but training and serving these models for inference poses new challenges.

Amazon SageMaker offers the fastest and easiest methods for training large deep learning models and datasets. Using partitioning algorithms, SageMaker’s distributed training libraries automatically split large deep learning models and training datasets across AWS GPU instances in a fraction of the time it takes to do manually.

Advances in deep learning have led to use cases such as computer vision and natural language processing models, where training time can create a bottleneck. To speed up training, developers can use data parallelism to train the model in parallel on shards of data on a cluster of GPU instances.

Until recently, data scientists have spent much time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs work well together. In addition, data scientists needed to design and build end-to-end machine learning (ML) pipelines to orchestrate complex ML workflows for deploying ML models in production.

Until recently, data scientists have spent much time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs work well together. In addition, data scientists needed to design and build end-to-end machine learning (ML) pipelines to orchestrate complex ML workflows for deploying ML models in production.