Cloudera’s Applied Machine Learning Prototypes (AMPs) move data science projects from concept to reality with pre-built solutions that provide single-click access to proven machine learning applications that address common business use cases.
- Fast: Provide starting point to build, deploy and monitor business-ready machine learning applications.
- Customizable: Complete machine learning framework enables models to be trained using customized datasets making it easy to go from idea to production.
- Reliable: Applied best practices and thorough testing and review with our hardware partners ensure our AMPs deliver trusted, reliable results.
Applied Machine Learning Prototypes (AMPs) are changing the future of machine learning.
AMPs are fully-developed prototypes based on common use cases that have built-in best practices and rigorously tested code with the ability to retrain or create applications unique to your organization. By shortening the time it takes to get from A to Z by starting at Y, see how AMPs will forever change how your ML projects are built and delivered.
An easier way to deploy machine learning use cases for faster delivery, greater scale and higher success rate.
Applied Machine Learning Prototypes (AMPs) change the way machine learning projects are built and delivered. These fully-developed prototypes provide a head start to solving common industry challenges.
Deep learning for anomaly detection
Continuous model monitoring
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Power your machine learning projects with a single click
Apply modern, deep learning techniques for anomaly detection to identify network intrusions.
Build a scikit-learn model to predict churn using customer telco data
Interact with a blog-style Streamlit application to visually unpack the inference workflow of a modern, single-stage object detector.
Build a semantic search application with deep learning models.
Perform topic classification on news articles in several limited-labeled data regimes.
Explore an emerging NLP capability with WikiQA, an automated question answering system built on top of Wikipedia.