Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 88

Machine Learning Software Development Data Drift AI 88

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 ML 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 88

ML Machine Learning Data Science Data Drift 88

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Some popular data quality monitoring and management MLOps tools available for data science and ML teams in 2023 Great Expectations Great Expectations is an open-source library for data quality validation and monitoring.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

AUGUST 29, 2023

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.

Data Scientist 72

Data Scientist Data Quality Python ML 72

The MLOps Blog

MAY 9, 2023

Data validation This step collects the transformed data as input and, through a series of tests and validators, ensures that it meets the criteria for the next component. It checks the data for quality issues and detects outliers and anomalies. Is it a black-box model, or can the decisions be explained?

ML 98

ML Machine Learning Metadata Automation 98