Remove Automation Remove Data Drift Remove ML Engineer
article thumbnail

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

IBM Journey to AI blog

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 266
article thumbnail

How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. These challenges are typically faced when we implement ML solutions and deploy them into a production environment.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Artificial intelligence (AI) and machine learning (ML) offerings from Amazon Web Services (AWS) , along with integrated monitoring and notification services, help organizations achieve the required level of automation, scalability, and model quality at optimal cost.

article thumbnail

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. This is made possible by automating tedious, repetitive MLOps tasks as part of the template. Alerts are raised whenever anomalies are detected.

article thumbnail

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. They should also offer version control capabilities to manage the changes and revisions of ML artifacts, ensuring reproducibility and facilitating effective teamwork.

article thumbnail

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

article thumbnail

Importance of Machine Learning Model Retraining in Production

Heartbeat

Once the best model is identified, it is usually deployed in production to make accurate predictions on real-world data (similar to the one on which the model was trained initially). Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But this is only sometimes the case.