ML Ops

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 provide, and want to expand its use.

A machine learning app is an application that incorporates machine learning algorithms to perform specific tasks without being explicitly programmed to do so. Machine learning algorithms use statistical techniques to learn from data, identify patterns, and make predictions or decisions based on that learning.

Machine learning apps can be developed for a wide range of use cases, such as image and speech recognition, natural language processing, recommendation systems, predictive maintenance, fraud detection, and many more. These apps can be built using a variety of programming languages and frameworks, and can run on a range of platforms, including desktops, mobile devices, and the cloud.

Examples of machine learning apps include virtual assistants like Siri and Alexa, image recognition apps like Google Photos and Facebook’s face recognition feature, and recommendation systems like Netflix’s movie and TV show recommendations.

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

Model Building

Pre-packaged, self-service sandbox environments: Sandbox environments with any preferred data science tools- such as TensorFlow, Apache Spark, Keras, PyTorch and more to enable simultaneous experimentation with multiple ML or deep learning (DL) frameworks.

Hybrid Deployment

On-premises, public cloud, or hybrid. RTS ML Ops runs on-premises on any infrastructure, on multiple public clouds (Amazon® Web Services, Google Cloud Platform, or Microsoft Azure), or in a hybrid model, providing effective utilization of resources and lower operating costs.

Model Monitoring

End-to-end visibility across the ML lifecycle. Complete visibility into runtime resource usage such as GPU, CPU, and memory utilization. Ability to track, measure, and report model performance along with third-party integrations track accuracy and interpretability.

Model Deployment

Flexible, scalable, endpoint deployment. RTS’ ML Ops deploys the model’s native runtime image, such as Python, R, H2O, into a secure, highly available, load-balanced, and containerized HTTP endpoint. An integrated model registry enables version tracking and seamless updates to models in production.

Collaboration

Enable CI/CD workflows with code, model, and project repositories. Project repository and GitHub integration of RTS ML Ops provides source control, eases collaboration, and enables lineage tracking for improved auditability. The model registry stores multiple models—including multiple versions with metadata—for various runtime engines in the model registry.

Security and Control

Secure multi-tenant with integration to enterprise authentication mechanisms: RTS ML Ops software provides multitenancy and data isolation to ensure logical separation between each project, group, or department within the organization. RTS ML Ops integrates with enterprise security and authentication mechanisms such as LDAP, Active Directory, and Kerberos.

Model Training

Scalable training environments with secure access to Big Data. On-demand access to scalable environments- single node or distributed multi-node clusters, for development and test or production workloads.

Faster time-to-value

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.

Improved productivity

Data scientists spend their time building models and analysing results rather than waiting for training jobs to complete. RTS ML Ops helps ensure accuracy or performance degradation in multitenant environments. It increases collaboration and reproducibility with shared code, project, and model repositories.

Reduced Risk

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 auto scales clusters to meet the requirements of dynamic workloads.