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Machine Learning Operations (MLOps)
Machine Learning Operations (MLOps) is a set of practices that provide determinism, scalability, agility, and governance in the model development and deployment pipeline.
Simplified model deployment - Data scientists use a variety of languages, frameworks, tools, and IDEs. With MLOps, ML teams can develop models using the interface, framework, and language that makes the most sense for the task at hand. For example, transitioning from a prototype model in a Jupyter Notebook to a large-scale hyperparameter sweep should be trivial.
Accelerate model training - The machine learning training process is computational complex and time intensive. Data scientists need access to on-demand compute and storage resources so they can iterate faster in the training phase. With MLOps, training phase is infrastructure agnostic, scalable, and minimizes complexity for the data scientist.
Purpose-built model monitoring - Tools and techniques used to monitor traditional software are not suitable for machine learning. MLOps provides purpose-built monitoring systems designed for machine learning. The key model-specific metrics must be tracked to gauge and compare performance of deployed models. Realtime alerting on important signals like model drift is also critical.
Model life cycle management - Models are not static assets. They are constantly retrained on new data and improved over time. Tooling these updates become a burden as teams scale up and begin to juggle multiple models and contributors. MLOps provides a unified hub for tracking the lineage and performance as new models are developed and rolled-out to production.
Model governance - Data, models, and other resources need to be tightly controlled to prevent undesirable changes and to ensure regulatory compliance where applicable. MLOps provides centralized access control, traceability, and audit logs to minimize risk and ensure regulatory compliance.
Enterprises are increasingly opting for a multi-cloud approach to leverage various resources across environments and decrease vendor lock-in. They require enhanced development capabilities with greater flexibility across multiple cloud and on-prem environments to suit their specific workflow needs.
Developing and deploying machine learning models turns out to be a slow process. The tools lack automation, collaboration is difficult, and workflows are difficult to scale. Ultimately, the time it takes to move from concept to production and deliver business value is a major hurdle in the industry. That’s why we need good MLOps that are designed to standardize and streamline the lifecycle of ML in production.
DevOps as a practice ensures that the software development and IT operations lifecycle is efficient, well documented, scalable, and easy to troubleshoot. MLOps incorporates these practices to deliver machine learning applications and services at high velocity. This new paradigm is useful as a way for enterprises to overcome the many challenges of training and deploying models into production.
Gradient from Paperspace offers agile ML tooling and methodology across multi-cloud, on-premise, and hybrid environments — without the need for DevOps or any manual configuration.
Gradient abstracts infrastructure and provides robust reproducibility and determinism. For the first time, data scientists can operate without a dependence on SREs or software teams to handle tasks like managing a Kubernetes cluster, ingesting data, and pipelining. When ML teams can operate with full autonomy and own the entire stack, they are much more efficient and agile.