Gartner estimates that through 2023, 50% of IT leaders will struggle to move their AI projects past proof of concept (POC) to a production level of maturity. This is largely due to the complexities involved in the handoff between data scientists and data engineers, life cycle management of ML models and presence of multiple models in production.
Sigmoid’s MLOps enables the operationalization of the end-to-end pipeline that supports the continuous delivery and continuous integration of models in a production environment. We help enterprises deploy machine learning models into production quickly and effortlessly, thus facilitating faster return on your ML investment. Our dedicated team of data engineers work with data scientists from the start, to overcome prevalent challenges with coding practices, data quality and large discrepancies in training and testing data sets ensuring that models are smoothly deployed to production environments without any snags.