Create optimized user experiences
Boost conversion and engagement, and add real-time relevance to product recommendations, with reinforcement learning–based capabilities available only through Azure. Select hero content, optimize layouts, and personalize offers with two API calls. Use Personalizer, part of Azure Cognitive Services, as a standalone personalization solution or to complement existing ranking engines—with no machine learning expertise required.
Improve recommendations, next best actions, and content offers
Use apprentice mode to validate whether Personalizer can match the results of your existing solution
Monitor and adjust the learning loop according to your parameters and KPIs with the user-friendly interface
No machine learning expertise required
Maximize business results with real-time learning
Get up and running quickly
Easily gauge how it's working
Comprehensive security and compliance, built in
Microsoft invests more than $1 billion annually on cybersecurity research and development.
We employ more than 3,500 security experts who are dedicated to data security and privacy.
Take charge with flexible pricing
Pay only for what you use, with no upfront costs. With Personalizer, you pay as you go based on number of transactions.
Get started with an Azure free account
Start free. Get $200 credit to use within 30 days. While you have your credit, get free amounts of many of our most popular services, plus free amounts of 55+ other services that are always free.
After your credit, move to pay as you go to keep building with the same free services. Pay only if you use more than your free monthly amounts.
After 12 months, you'll keep getting 55+ always-free services—and still pay only for what you use beyond your free monthly amounts.
Frequently asked questions about Computer Vision
Personalizer learns from the collective behavior of users, through the features or attributes about them that you send to the service. It uses this information to train a shared model that’s updated with information from every interaction, improving personalization outcomes for all users.
Personalizer works best when a rank call has 50 or fewer items. To personalize a choice from a larger list or catalog, reduce the number of items by using a recommendation engine or sorting technique.
Personalizer works with or without user sign-in but delivers richer, more relevant interactions for signed-in users.
Yes. Personalizer learns in the background, in real time, via apprentice mode. When the service has learned enough from users' actions to approximate your existing personalization system, you can use Personalizer in production with confidence to let it learn by interacting with your users and support a positive user experience.