ML6 presents a recommended CI/CD pattern for promoting ML model code across environments using Amazon SageMaker.
This blogpost deep-dives into MLOps practices, highlighting the need for reliable and efficient model deployment. By focusing on code promotion rather than the model itself, ML6 streamlines ML workflows, accelerates time-to-market, and enhances overall MLOps capabilities. The post includes insights, architectural details, tech stack, CI/CD workflow, and a practical example of fine-tuning a pre-trained model for medical transcription classification.
Through collaboration with AWS, ML6 offers an innovative solution, leveraging Terraform and SageMaker Pipelines, to enable users to adopt this deployment pattern effectively. By implementing these practices, organizations can achieve reproducibility, reliability, and efficiency in ML model deployment.
Read the full blogpost on our Medium channel.