Microsoft Azure offers a myriad of services and capabilities. Building an end-to-end machine learning pipeline from experimentation to deployment often requires bringing together a set of services from across
Tag: IT AI ML
New for Amazon SageMaker – Perform Shadow Tests to Compare Inference Performance Between ML Model Variants
SageMaker now automatically deploys the new variant in shadow mode and routes a copy of the inference requests to it in real time, all within the same endpoint. Once you complete a shadow test, you can use the
Ingest streaming data to Apache Hudi tables using AWS Glue and Apache Hudi DeltaStreamer
You can create AWS Glue Spark streaming ETL jobs using either Scala or PySpark that run continuously, consuming data from Amazon MSK, Apache Kafka, and Amazon Kinesis Data Streams and writing it to your
Run Apache Spark with Amazon EMR on EKS backed by Amazon FSx for Lustre storage
As shown in the following diagram, the FSx for Lustre CSI driver plugin is deployed to an Amazon EKS cluster to dynamically provision the FSx for Lustre file system with a given PVC. The Spark application driver and
Build a pseudonymization service on AWS to protect sensitive data, part 1
The pseudonymization service is built using AWS Lambda and Amazon API Gateway . The request response model of the API utilizes Java string arrays to store multiple values in a single variable, as depicted in the
Machine learning operations (MLOps) v2
The data and model monitoring and event and action phases of MLOps for NLP are the key differences from classical machine learning. Classical machine learning: Time-Series forecasting, regression, and classification