Utilize DevOps speed and agility for machine learning. Overcome the challenges of operationalizing ML models.
DevOps speed and agility for machine learning
The ability to apply artificial intelligence (AI) and machine learning (ML) to unlock insights from data is a key competitive advantage for businesses today. Today’s modern enterprises understand the benefits machine learning can provide, and they want to expand its use.
However, as organizations attempt to operationalize their ML models, they encounter last mile problems related to model deployment and management. RTS’ ML Ops provides DevOps-like speed and agility to the ML lifecycle and empowers large enterprises to overcome barriers in deploying and operationalizing AI/ML across the organization.
Much like pre-DevOps software development, most data science organizations today lack streamlined processes for their ML workflows, causing many data science projects to fail. Consequently, this inhibits model deployment into current business processes and applications.
It may seem like a straightforward solution to use DevOps tools and practices for the ML lifecycle. However, ML workflows are very iterative in nature and off-the-shelf software development tools and methodologies will not work.
RTS’ ML Ops addresses the challenges of operationalizing ML models at enterprise scale. Public cloud service providers offer disjointed services, and users are required to cobble together an end-to-end ML workflow.
Also, the public cloud may not be an option for many organizations with workload requirements that require on-premises deployments due to considerations involving vendor lock-in, security, performance, or data gravity. RTS’ ML Ops helps businesses overcome those challenges with an open-source platform that delivers a cloud-like experience combined with pre-packaged tools to operationalize the machine learning lifecycle, from pilot to production.
RTS’ ML Ops Solution
Security and Control
Patented innovations provide highly performant training environments—with compute and storage separation—that can securely access shared enterprise data sources on-premises or in cloud-based storage.
Manage and provision development, test, or production environments in minutes as opposed to days. Also, instantly onboard new data scientists with the preferred tools and languages without creating siloed development environments.
Data scientists spend their time building models and analyzing results rather than waiting for training jobs to complete. RTS’ ML Ops helps ensure no loss of accuracy or performance degradation in multitenant environments. It increases collaboration and reproducibility with shared code, project, and model repositories.
It provides enterprise-grade security and access controls on computer servers and data. Lineage tracking provides model governance and auditability for regulatory compliance. Integrations with third-party software provide interpretability. High availability deployments help ensure critical applications do not fail.
Flexibility and Elasticity
Deploy on-premises, cloud, or in a hybrid model to suit your business requirements. RTS’ ML Ops autoscales clusters to meet the requirements of dynamic workloads.