AWS Machine Learning Blog
Category: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
How VMware built an MLOps pipeline from scratch using GitLab, Amazon MWAA, and Amazon SageMaker
This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) […]
Integrate Amazon SageMaker Data Wrangler with MLOps workflows
As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including […]
Orchestrate XGBoost ML Pipelines with Amazon Managed Workflows for Apache Airflow
July 2023: This post was reviewed for accuracy. The ability to scale machine learning operations (MLOps) at an enterprise is quickly becoming a competitive advantage in the modern economy. When firms started dabbling in ML, only the highest priority use cases were the focus. Businesses are now demanding more from ML practitioners: more intelligent features, […]