Powerful tools to improve work efficiency.

For Data Scientists

Everything required by data scientists to successfully deliver their work is provided by EpisenseAI – from Data cleaning, Data visualization, Automated Data Labelling, Data Enrichment and Data pre-processing to Cloud Compatibility, Continuous Model Integration & Deployment, and Feedback Looping to models.

Features

Ease of Use

EpisenseAI platform empowers data scientists with a suite of plug-and-play products that can be easily stitched and customized to create the required analytics pipeline. With this, all smaller steps in analytics such as data cleaning, data preparation, feature extraction etc. are reduced to just picking up the required tools and using them on an interface requiring no coding from the users. With the ease and increased pace of performing analytics tasks, data scientists have the flexibility to test and iterate more proposed solutions than ever, thus increasing the efficiency and efficacy of your work.

Data Preparation

Data scientists face the huge challenge of getting the data in order before they could proceed with any modelling on the data. Episense understands this huge challenge and provides data scientists with a powerful platform to clean the data, annotate it, prepare and process it so that it is in usable form the analytics pipelines. And we deliver all this on a platform with an interface which is easy to use and intuitive to the core.

Data Infrastructure

EpisenseAI provides end-to-end data analytics infrastructure along with part-product tools that can fit perfectly as a plug-and-play tool in your existing data infrastructure. This makes the implementation of analytics projects a clean set-up, without any clutter, while you get and pay only for features that you want. Episense provides users with end-to-end data analytics infrastructure such as cloud compatibility, Flexible Computing Power, Continuous Integration & Deployment, and Model Monitoring so that users don’t have to think of peripheral issues such as cloud, data storage, computing resources etc. and can focus on the core analytics, leaving out the thorny implementation issues for us.

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