Everyday millions of machine learning models are built by analysts and data scientists everywhere, and only a few of them make it to production, and continue to provide value over time. At Predera we have built AIQ, an MLOps & monitoring engine to help Data Scientists with the tedious job of deploying and managing ML, by collecting and leveraging the rich semi-structured data created in the entire machine learning development life cycle.
AIQ is language agnostic, plugs into most popular machine learning (ML) libraries across cloud / on-premise to continuously monitor data, track model quality and provide real-time insights to manage robust, high quality machine learning applications.
Based on our record, Amazon Machine Learning seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
There’s also the ML as a service (MLaaS) movement that lowers the barrier for common ML capabilities (eg image object detection and audio transcription). Basically, you use APIs. See: https://aws.amazon.com/machine-learning/. Source: over 1 year ago
Do you have questions about Data Science and ML on AWS - https://aws.amazon.com/machine-learning/. Source: about 3 years ago
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
Azure Machine Learning Studio - Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.
Lobe - Visual tool for building custom deep learning models
cnvrg CORE - Free ML Platform for everyone
Apple Machine Learning Journal - A blog written by Apple engineers
Dataiku DSS - Dataiku's single, collaborative platform powers both self-service analytics and the operationalization of machine learning models in production.