We help our customers to build for their users a wide range of solutions that learn and form conclusions with imperfect data.
Empower your customers with solutions that interpret the meaning of data, including text, voice, and images.
We enable our customers our customers with solutions that interact with people in natural ways and delivering all the functionality across channels.
The following steps summarizes the high-level phases that lead to the creation of useful predictive AI model. For a detailed look at the model building process, please contact our technical support team.
In the Prepare Data phase, the data is collected from sources and prepared for use in training the model. During this phase, the data may be cleaned and de-duplicated, the contents of the data is understood and the data that is the most informative in predicting the outcome is selected.This work is often referred to as data wrangling. Typically the data wrangling is performed by either the data developer or data scientist who have wrote the programs that collect and prepare the data.
Once our data looks fine, the Build Model phase begins, where a subset of the prepared data (which contains both the input and the outcome) is fed into a machine learning or deep learning algorithm to train the model. Then performance of the model is measured against another subset of the prepared data (referred to as the test or evaluation data set), and the model is evaluated on how well it performed in predicting the outcomes described in the test data set.
Assuming the model performed adequately, then the model is saved to a file ready for deployment. In the deploy model phase, the created model file is typically copied to a location where it can be used by the AI application for making predictions. This step is typically performed by one or more of our developers, or more specifically, DevOps engineers who are responsible for making sure the model is always deployed correctly into production and ready to be consumed.
We help our customers build, connect, deploy, and manage intelligent bots to naturally interact with your users on a website, app, Cortana, Microsoft Teams, Skype, Slack, Facebook Messenger, and more.
Item-to-Item Recommendations. This is the "Customers who liked this product also liked these other products" scenario. Increase the discoverability of items in your catalog by showing relevant products to your customers.
Personalized Recommendations. By providing the recent history of transactions for a given user, the SAR algorithm can return personalized recommendations for that user.
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