Model Deployment

Training a model on existing data is the first step towards harnessing the power of data for predictive and intelligent ML driven solutions. A well-trained model is key. It consumes 80% of the effort and plays a significant part in the success of the ML initiative. You need us to do it right.

Model testing and validation for data privacy, fairness and regulatory compliances is also increasingly becoming very critical for organizations deploying ML powered workflows and products. A poorly deployed model carries the risk of reputation loss, fines and legal troubles.

Usually, a ML driven product uses multiple models with inter-dependencies which also need to be managed for frictionless data flow and proper impedance match. The system needs to be tweaked to handle latency issues or graceful backoffs for each failure points adding further to the complexity of ML back application deployment and maintenance.

We help you navigate this highly complex technical and fast changing engineering discipline for using ML driven systems in production.

Data Engineering for Deployment

Taking a model to production and integrating it with an application requires expertise on multiple tracks such as setting up data pipelines, selecting model deployment architectures, setting up docker configurations to fine tuning cost efficiency trade-offs, pruning models for acceptable response times, defining business and technical performance metrics, setting monitoring services. Implementing proper CI/CD and MLOps infrastructure and practices.

We bring multi cloud expertise to architect a solution best suited for your needs avoiding expensive vendor lockins and premature optimizations.