IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

Big Data 278

Big Data DevOps Automation Machine Learning 278

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.

Data Drift 124

Data Drift Machine Learning Data Scientist DevOps 124

ODSC - Open Data Science

JULY 19, 2023

A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production. The typical score.py

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 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

AWS Machine Learning Blog

APRIL 26, 2023

Commonly, the work is split up between the following workstreams: Cloud engineering (infrastructure and security) – Focuses on verifying that the AWS accounts and infrastructure are set up and secure ahead of EBA. Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler.

ML 116

ML Machine Learning Data Science Data Drift 116

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

AUGUST 29, 2023

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker , Amazon EventBridge , AWS Lambda , Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD.

Data Scientist 85

Data Scientist Data Quality Python ML 85