Mantel Group offers MLOps with Databricks on AWS to help organizations overcome challenges in operationalizing machine learning projects. With expertise in MLOps, Databricks, and AWS, Mantel Group guides clients in ensuring ML projects become integral parts of their business processes, not just experiments. The complexities of transitioning ML projects to production, such as code, data, and model management, are addressed by Mantel Group through a streamlined process that integrates DevOps, DataOps, and ModelOps practices.
Central to Mantel Group’s offering is a data-centric workflow that merges DevOps, DataOps, and ModelOps best practices. This approach fosters collaboration among data teams and simplifies the management of ML pipelines, from data ingestion to model deployment. Mantel Group also integrates with Databricks Feature Store, enabling the co-production of models and features. This feature allows data scientists to develop models with a deeper understanding of necessary features, easing the implementation process for ML engineers.
The commitment to open standards is a priority for Mantel Group, as seen in their integration with tools like Git for DevOps, Delta Lake for DataOps, and MLflow for ModelOps. This focus on openness ensures the platform can adapt to the unique requirements of organizations and evolve in line with industry-wide standards. Mantel Group’s approach extends DevOps principles to ML, providing clear semantics for “go to production” and enabling existing tools and CI/CD processes to manage ML pipeline code alongside data pipelines.
In summary, Mantel Group’s MLOps with Databricks on AWS offering addresses the challenges organizations face in operationalizing machine learning projects. By providing a simplified process that integrates key practices and tools, Mantel Group ensures that ML projects are seamlessly integrated into business processes. This approach, rooted in a data-centric workflow and adherence to open standards, streamlines the operational processes for ML projects and enables efficient collaboration between data teams.
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