We work with clients to identify their business needs and design a customized machine learning infrastructure that aligns with their goals.
We evaluate the available options and recommend the best platform to meet the client's needs, such as AWS, Google Cloud, or Microsoft Azure.
Our team sets up the infrastructure and implements the necessary tools and technologies, such as Kubernetes, Docker, and Apache Airflow.
We build end-to-end data pipelines that enable clients to collect, preprocess, and transform data for use in machine learning models.
We deploy machine learning models to production and provide ongoing support to ensure they are running effectively.
We monitor the infrastructure and models to identify issues and improve performance, using techniques such as automated testing, logging, and error reporting.
We ensure that the infrastructure is secure and compliant with relevant regulations and standards, such as HIPAA or GDPR.