Rapid AI Deployment with Data Machines
Qualetics is pleased to announce the launch of “Data Machines”, a solution designed to streamline building, testing, deploying, and monitoring AI solutions. Data Machines combine access to a library of world class AI models with a graphic user interface to configure and test your own AI solutions. The Data Machines can consist of simple single model solutions or complex multi-model configurations where the analyzed result is one of the feeds as the input of the next.
So, let’s review some of the AI capabilities of Data Machines.
Combining a model library and standardized connections with a graphic user interface makes it possible to build, test, and deploy AI solutions faster, and to monitor their performance better.
Each data machine is an intelligent engine composed of one or more AI models efficiently working with each other to handle inputs and outputs in a coordinated manner to perform a task. The Data Machine example below represents the graphical user interface used to select, arrange, and guide the model(s) and how they are intended to function in the AI solution you want to create.
Data Machines continuously improve the more you use them.
Many models used in Data Machines are self-learning and become more intelligent the more you use them. Below are self-learning models ready for use from the Qualetics AI Model Library. For the models below, an integration with Qualetics streaming is required using the SDK provided or through a no-code integration with Zapier or WordPress.
This model understands the intent and context of a query then delivers results based on your data without relying on keyword matching.
Intelligent Question & Answering (Chatbot)
An extension of Semantic Search, this model operates as a conversational agent responding to queries in the context of previous questions and answers posed in the same session, with training based on your data.
Exception Severity Detection
Detects the severity of exceptions (Major/Minor/Critical exceptions) based on the sequence of events, usage and user activity.
This model identifies anomalies occurring when transmitted as a series of events. The anomaly detection can be enabled on specific parameters with user defined thresholds.
This model identifies duplicate information from a master dataset. The analyzed result includes the master data and input data strings that are matched along with a confidence score for the match.
This model analyzes the patterns of user engagement and product usage to predict the likelihood of churn based on the user behavior within an app.
Based on the user activity, this model can predict recommended content or products for users.
No need to engineer memory support in order to create intelligent AI solutions.
Each model in a Data Machine can retain information between the steps enabling intelligent sequencing similar to how humans process inferences from preceding steps.
In the Data Machine example below the first step checks to identify the language a user entered capturing it as the “detected language”. This value is retained until it is included as a “Model Input” in the last step when the summarized text is translated back to the user’s language.
Total flexibility and ease of integration, deployment, or sharing of your AI solution’s analyzed output expands the reach and impact of your AI efforts.
Data Machines provide an integration friendly deployment experience with API-based connectivity, No Code connectivity to over 6,000 SaaS applications through Zapier, and automated notification and socialization features for analyzed output.
No need to find a model designed for your specific domain, Data Machines adapt to you, you don’t have to adapt to them.
Data Machines can be built and trained to support any business such as education, healthcare, marketing services, retail, research and all others.
The self-learning Q&A Data Machine below analyzes a question posed and provides the answer based on data provided to it. It includes language translation to reply with an answer in the language of the original question.
Being aware of how your AI is performing allows you to monitor the deviation from desired results and provides for proactive risk mitigation.
Once deployed every AI solution must be monitored for accuracy and fitness for purpose, Data Machines deliver built-in observability allowing you to monitor utilization and accuracy. These governance capabilities are particularly important where live analytics are concerned.
The example below shows an activity dashboard reflecting several Data Machines that have been deployed.
This elevates informed decision-making across your enterprise and accelerates action that is based on up-to-the-second facts and data.
Data Machines are deployed in an environment rich in dashboard, reporting, and user provisioning features that enable sharing analyzed results and insights across an organization along with communication and sharing features to empower group collaboration elevating the decision-making power of your enterprise.
Data Machines embed security features into your development and deployment experience.
Authenticated requests, encryption in transmission, multi tenancy and tenant isolation even within the same account, all ensure no leakage to unauthorized entities.
Start building your AI journey today
The Data Machines solution is now live and ready for you to start your AI journey by building AI powered automations. Please go to our Plans and Pricing page at www.qualetics.com/pricing and you can begin right away with a free 7-Day trial.
We are also offering for a limited time, personalized onboarding sessions to help as you start your free trial building your own Data Machines. To book a personalized session today, schedule a slot using our Calendly link – https://calendly.com/qualetics/data-machines-introduction.