MLOps
Stably manage the machine learning lifecycle
Solving the challenges of machine learning development and operation
MLOps stands for machine learning operations and is also referred to as machine learning infrastructure.
Operational issues arise in the development and operations of systems that incorporate machine learning, such as detecting when abnormal data has been fed in as training data and building a mechanism to automatically relearn the data.
DATUM STUDIO provides a system where machine learning can actually run stably in your business operation using cloud services, thereby contributing to the growth of your business.
Total support for construction, deployment, and monitoring of machine learning systems
“DATUM STUDIO automates the entire machine learning process from model creation, accuracy management, deployment to the actual environment, monitoring, and updating. We support our clients from the decision-making process to the actual implementation of machine learning and help them extract business value in a stable manner.
Automating the operation of the machine learning system allows data scientists to focus on higher value-added tasks and build an environment where they can make advanced use of data, such as discovering new insights.”
Services
DATUM STUDIO with Amazon SageMaker
Support for MLOps infrastructure building using Amazon SageMaker
DATUM STUDIO with MLflow
Building an MLOps infrastructure using the open source platform MLflow
DATUM STUDIO with Microsoft
Leveraging Microsoft data analytics and AI services for MLOps
DATUM STUDIO with Google Cloud
Achieving MLOps with Google Cloud best practices.
Google Cloud is a trademark of Google LLC.
Team comprising over 100 data scientists Proven track record across industries and sectors
DATUM STUDIO has a team of more than 100 data scientists and a proven track record of using AI in the resolution of management issues for companies in a broad range of industries and sectors. To help you achieve your business goals, we can flexibly respond to needs from problem identification to planning for optimal data utilization, proof of concept (PoC), infrastructure building, AI model construction, continuous integration (CI), continuous delivery (CD), and continuous training (CT).
