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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Driven by significant advancements in computing technology, everything from mobile phones to smart appliances to mass transit systems generate and digest data, creating a big data landscape that forward-thinking enterprises can leverage to drive innovation. However, the big data landscape is just that.

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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

Model Observability: To be effective at monitoring and identifying model and data drift there needs to be a way to capture and analyze the data, especially from the production system. We have implemented Azure Data Explorer (ADX) as a platform to ingest and analyze data. is modified to push the data into ADX.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. Delta Lake Delta Lake is an open-source storage layer that provides reliability, ACID transactions, and data versioning for big data processing frameworks such as Apache Spark.

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MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

AWS Machine Learning Blog

The proposed architecture for the batch inference pipeline uses Amazon SageMaker Model Monitor for data quality checks, while using custom Amazon SageMaker Processing steps for model quality check. Model approval After a newly trained model is registered in the model registry, the responsible data scientist receives a notification.