AI & Analytics Engine VS Predera

The PI.EXCHANGE AI & Analytics Engine (the Engine) is a Data Science and Machine Learning (ML) platform that empowers everyone, even novice users, to affordably build high-performance ML applications in minutes or hours, not weeks or months.

The easy-to-use connected toolchain provides everything you will need to go from raw data to predictions and insights within a single pipeline. Manual and repetitive machine learning tasks are automated, and the Engine's intelligent features help guide the user end-to-end. So, whether you are building a small pilot project with no dedicated data science resources, or are deploying large-scale enterprise ML systems, you can equip your existing team with the right tool to build meaningful solutions, fast. The Engine gives users the flexibility to customize their ML pipeline from scratch for classification, regression, time-series, or clustering problems or to select an ML solution template to develop their ML application. While both ML development options are guided and require no-coding experience, the latter requires only articulation of business requirements and problem context via a few key steps - everything else is taken care of.

Notable AI solutions include: Customer Churn Prediction Leveraging your manufacturing data to build predictive maintenance strategies Predict online fraudulent transactions and reduce false positives and; Optimize logistics decision-making

Everyday millions of machine learning models are built by analysts and data scientists everywhere, and only a few of them make it to production, and continue to provide value over time. At Predera we have built AIQ, an MLOps & monitoring engine to help Data Scientists with the tedious job of deploying and managing ML, by collecting and leveraging the rich semi-structured data created in the entire machine learning development life cycle.

AIQ is language agnostic, plugs into most popular machine learning (ML) libraries across cloud / on-premise to continuously monitor data, track model quality and provide real-time insights to manage robust, high quality machine learning applications.