Overview
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 Cloudera Fast Forward Labs and 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.
Use cases
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.
Churn modeling
Deep learning for anomaly detection
Continuous model monitoring
Churn modeling
Proactively identify customers that are attrition risks with churn modeling applied machine learning prototype.
Enterprises across telecommunications, financial services and other sectors rely on strong customer retention. Cloudera’s Churn Modeling off-the-shelf Applied Machine Learning Prototype helps data scientists identify high attrition risk customers from which companies can devise strategies to reduce churn and drive customer lifetime value.
Taking machine learning from research to results
Learn about Applied Machine Learning Prototypes (AMPs) and how these fully developed prototypes offer data scientists a running start to bring machine learning applications from concept to reality. AMPs provide high-quality, professionally developed code and the ability to learn from and modify prototypes for faster results.
Deep learning for anomaly detection
Deliver valuable insights on what should and should not be with with deep learning for anomaly detection applied machine learning prototype.
Fraud detection, medical diagnosis and predictive maintenance all rely on identifying outliers and anomalies in very large datasets.Cloudera’s Deep Learning for Anomaly Detection off-the-shelf Applied Machine Learning Prototype allows data scientists to get models into production and deliver valuable insights faster.
Taking machine learning from research to results
Learn about Applied Machine Learning Prototypes (AMPs) and how these fully developed prototypes offer data scientists a running start to bring machine learning applications from concept to reality. AMPs provide high-quality, professionally developed code and the ability to learn from and modify prototypes for faster results.
Continuous model monitoring
Ensure production models continue to provide accurate information over time.
Even the best machine learning models are subject to drift in production environments putting enterprise forecasts, product safety and defect identification capabilities at risk. Cloudera’s Continuous model monitoring off-the-shelf Applied Machine Learning Prototype adds functionality that helps data scientists ensure data and numerical drift don’t compromise models’s accuracy and reliability.
Taking machine learning from research to results
Learn about Applied Machine Learning Prototypes (AMPs) and how these fully developed prototypes offer data scientists a running start to bring machine learning applications from concept to reality. AMPs provide high-quality, professionally developed code and the ability to learn from and modify prototypes for faster results.
Jumpstart your project
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.