MLOps is not only about Training Building, Deploying, Monitoring a Model but also understanding its Ecosystem

When you are learning #MLOps it's very important to know understand about the different terminologies and its ecosystem, for ex., while learning DevOps, few of the terminologies/concepts are important to know about such as Infra provisioning, Code, Build, Deploy, Test, Monitor Automate, Pipeline, etc., In similar following are the important to know about while learning MLOps or ML Projects or working with ML/AI

Data Verification: Ensuring the accuracy and integrity of data used in machine learning models through validation processes.

Machine Resource Management: Optimizing and allocating computational resources efficiently for machine learning tasks.

Monitoring: Continuous observation and analysis of machine learning systems to ensure performance, reliability, and adherence to defined metrics. Configuration: Setting up and adjusting parameters and settings to optimize the performance and behavior of machine learning pipelines.

Data Collection: Gathering and organizing relevant data from various sources to train and improve machine learning models. Serving Infrastructure: Infrastructure and systems designed to deploy and serve machine learning models to end-users or other systems.

ML Code: Software code specifically designed for implementing machine learning algorithms and models. Analysis Tools: Tools and software used for exploring, visualizing, and interpreting data in the context of machine learning projects.

Feature Extraction: Process of identifying and selecting relevant features or attributes from raw data for use in machine learning models. Process

Management Tools: Tools for managing and automating various stages of the machine learning lifecycle, from data preprocessing to model deployment.

Will continue with examples and tools for above terminologies..