After more and more buzzing facts about MLOps and DevOps, let’s examine something deeper about the MLOps operation in production. Making more sense of the information, allow us to see the precise definition of the MLOps.
The sequential process of integrating an experimental Machine Learning model into a production system is known as MLOps. The term could be a combination of “Machine Learning” and “DevOps,” which is a continuous development practice in the software industry. “In isolated experimental systems, machine learning models are tested and developed.”
These operations add a loop to develop a workflow as shown within the above MLOps data flow, Some processes and pipelines are listed below,
- model deployment
- model training
- model monitoring
- Model life cycle management
- Model governance
So, deep knowledge about these processes will help us to successfully deploy a machine learning model with more scalability and efficiency. Each process is explained below, the maximum amount because the process is more simplified.
Data engineers use a spread of languages, frameworks, tools, and IDEs. ML teams may use MLOps to generate models using the interface, framework, and language that makes the most sense for the task at hand. For example, switching from a prototype model to a large-scale hyperparameter sweep in a Jupyter Notebook should be simple.
Machine learning training is a computationally expensive and time-consuming process. To iterate quicker throughout the training phase, data scientists want on-demand access to computation and storage resources. The training phase of MLOps is infrastructure agnostic, scalable, and simplifies things for the data scientist.
MLOps builds monitoring systems specifically for machine learning. To monitor and compare the performance of deployed models, critical model-specific metrics must be tracked. It’s also crucial to get real-time alerts on significant indications like model drift.
Model life cycle management
Models aren’t one-dimensional objects. They’re retrained on new data regularly, and their efficiency improves over time. As teams grow in size and begin to manage many models and contributors, tooling these upgrades becomes a hassle. As new models are generated and rolled out to production, MLOps offers a structured platform for tracking their history and performance.
Data, models, and other resources must be tightly regulated to prevent unwanted modifications and ensure regulatory compliance, if necessary. MLOps provides centralized access control, traceability, and audit logs to reduce risk and maintain regulatory compliance.
So, These are some operational infrastructures of MLOps that help data scientists rapidly deploy ML models to standardize the method for elevating models from development to production that are missing or flawed.