Home > APEX > Compute & HCI > White Papers > Optimize Machine Learning Through MLOps with Dell APEX and cnvrg.io > Executive summary
The Dell APEX portfolio of services provides the flexibility to meet growth demand and full control of security and regulatory requirements in the OPEX cloud model. APEX services include an unparalleled speed of deployment in the industry and the trusted relationship of Dell as your partner. The Dell APEX Private Cloud offering with the AI/ML optimized option is ideal for leveraging GPU accelerated workloads. By using Dell Technologies' APEX cloud model, organizations can meet any scale challenge with predictable costs and performance to optimize their analytics and AI jobs. APEX Data Storage Services capitalizes on Dell's highest performing and market leading storage products to deliver a data location for big data and analytics with submillisecond latency.
Intelligence—the ability to comprehend and react—is demonstrated both as natural intelligence in humans and animals and as artificial intelligence (AI) in computers and systems. AI can deliver significant benefits to organizations by better enabling the categorization and analysis of massive amounts of data collected through mechanisms like sensors, networks, and always-connected devices.
Organizations today use AI to support progress towards their business intelligence goals and to solve problems. Machine learning (ML) is the most critical enabler of AI, employing algorithms and learning models to parse large datasets. Image and speech recognition are two examples of applications where employing ML can improve AI far faster than human analysis can. But data scientists working with ML spend an inordinate amount of time designing, configuring, and testing ML platforms. These highly trained specialists spend too little time working with the data and models, which is their primary specialty. MLOps (Machine Learning Operations) is a process inspired by the popular DevOps model for application development. By using MLOps, organizations investing in ML can more easily automate the continuous training and deployment of ML models at scale. MLOps adapts DevOps principles and practices to the ML workflow.
Combining development and operations in DevOps creates a more standardized and accelerated methodology for application development and deployment. Similarly, by automating the complexity and variability of the ML process, MLOPs helps lead to far more reproducible, testable, and evolvable ML environments.
Standardized, predictable, and manageable ML deployments can drive the launch of new capabilities, discoveries, and services, enabling an organization to derive more insights and value from the data it collects. These insights and values are the goal of MLOps.