Training an AI model and making it work in a live environment are two different things. While model estimation can be done in a static environment, making a model available in real-time involves a complex pipeline of data processing, batch jobs, model updates, testing, cloud engineering, and performance tuning. Our expert data, Python and cloud engineers take care of these so that you can focus on what really matters - your AI model.
Complex database design and engineering
Data cleaning and validation
Derived calculations and feature engineering
Data scraping and acquisition
We are familiar with: databases of all types and brands, including SQL, noSQL, vector, time-series and flat file DBs; streaming, queue, distributed storage and orchestration systems; performant batch jobs which run daily or in real-time; and proper security and access protocols.
We work in AWS, Azure, GCP and/or on-premises hypervisor systems to create robust pipelines for our clients.
Setup and configuration of all cloud services
Containerization and depoyment
Network and security configuration
Complex production workflows involving a multitide of services
Continuous Integration and Delivery
Optimization of production processes to ensure that they run in a timely and reliable manner
Creation of performant, production ready API endpoints to serve both internal and external users
Monitoring, logging, dashboard and alert systems
We specialize in AI Model Operations and Deployment, employing engineers with experience in and specifically for this purpose. Leadership have deep experience in the technology, finance, and industrial sectors.
Our unique blend of onshore and offshore resources allows a comparatively large team to be assigned for each client and project. Costs are optimized by smart allocation of resources depending on the task.
Productionizing AI models can take a disproportionate amount of time, energy and money. Our service lasers in on the production process and allows you to focus on what really matters - the AI development process.