Unite.AI

JUNE 24, 2024

MLOps , or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. What is MLOps?

Data Drift 124

Data Drift Machine Learning Data Scientist DevOps 124

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

Big Data 278

Big Data DevOps Automation Machine Learning 278

ODSC - Open Data Science

JULY 19, 2023

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.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

DataRobot Blog

SEPTEMBER 6, 2022

Data science teams cannot create a model and “throw it over the fence” to another team. Everyone needs to work together to achieve value, from business intelligence experts, data scientists, and process modelers to machine learning engineers, software engineers, business analysts, and end users.

Data Scientist 52

Data Scientist Data Drift Machine Learning Data Science 52

Towards AI

AUGUST 16, 2023

Monitoring Models in Production There are several types of problems that Machine Learning applications can encounter over time [4]: Data drift: sudden changes in the features values or changes in data distribution. Model/concept drift: how, why, and when the performance of the model changes.

Machine Learning 82

Machine Learning Software Development Data Drift DevOps 82

Bugra Akyildiz

MARCH 12, 2023

Uber wrote about how they build a data drift detection system. Make sure your vision is aligned with the power customers. It’s important to align the vision for a new system with the needs of power customers. In our case that meant prioritizing stability, performance, and flexibility above all else.

Explainability 52

Explainability Data Drift Software Engineer Data Science 52

AWS Machine Learning Blog

APRIL 26, 2023

Improve model accuracy: In-depth feature engineering (example, PCA) Hyperparameter optimization (HPO) Quality assurance and validation with test data. Monitoring setup (model, data drift). Data Engineering Explore using feature store for future ML use cases. Deploy to production (inference endpoint).

ML 116

ML Machine Learning Data Science Data Drift 116