The Complete AI Pipeline

June 8, 2021 | Debiprasad Banerjee

It is such a delight to be working with domain specialists, tech nerds, and data scientists eager to build cutting-edge solutions that translate data insights into business value. With our vision to foster technological capabilities that create seamless yet impactful experiences for clients, we launched Veltris Insight.

It is a platform that accelerates the end-to-end AI deployment process and makes scaling AI solutions easy, harnessing the true potential of AI and what it can do for enterprises at scale. This end-to-end AI pipeline can manage data, train, evaluate, and deploy models, run inferences, and monitor them. The system is totally model agnostic and supports traditional ML models, time series forecasting, computer vision & deep learning, NLP/Language-based models, etc. Built based on our experience gathered from real-world use cases, it leverages industry-standard open-source technologies and our expertise to build robust solution accelerators.

How will Veltris Insight help businesses?

I realized that businesses need an approach where they see immediate Benefits & ROI and use that solution to enhance productivity. With the Insight framework, our team can analyze massive amounts of data (audio, image, video, text, and graphical data) with real-life applications in understanding objects, mapping images, ranking user preferences, predicting outcomes based on time series data, ad placement recommendations, machine translation, automating enterprise processes and workflows and so many more such use cases. Organizations can use machine learning techniques and models to identify previously undetected patterns and anomalies in data and streamline repetitive tasks to alleviate productivity bottlenecks and reduce costs.

Thus, my team and I started this exciting journey with a holistic view of the end-to-end pipeline that is the key to successful deployment and scaling of enterprise-wide AI systems. We designed the Insights framework to help accelerate and simplify this journey. It gives organizations a head-start on their AI initiatives, but more importantly, it provides a tangible ROI on all their AI investments.

The Architecture:

Any large enterprise will typically have its data distributed across records in multiple systems, residing in various departments with strict ownership, access, and usage rights. Owing to the large datasets, the AI model training may need to be done in a distributed GPU environment with the orchestration of complex workflows. The trained model and the training parameters are stored in a model repository and then deployed. Veltris Insight easily and efficiently enables all the above. Typically, it is left to the engineers to reinvent the wheel for monitoring, debugging, and visualizing model performance, and that is where Insight helps tremendously with built-in tools for logging, monitoring, and hardware-specific accelerations. Finally, an API interface for hooking into the enterprise applications makes this an end-to-end pipeline for all AI applications.

The end to end AI pipeline

Veltris Insight is a unique framework that helps implement the above pipeline using a mix of open-source software modules and some crucial components built in-house. All open-source components are mature modules, and we fork, customize, and contribute back as needed. The framework uses cloud and on-premises infrastructure to compute, process, and store data. It also has built-in monitoring and management tools integrated into the framework. The framework can handle essential industry use cases and data models, such as detection & classification, semantic segmentation, time series prediction, different aspects of NLP, etc.


I sincerely believe that Veltris Insight can bring profound changes to various industries when they enter the stage of AI implementation at scale. While it may look like a huge investment of time and resources to build and deploy a platform like this, the truth is we can build it step-by-step and deploy our first few models into production.

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