Fusion of ML, DevOps and MLOps in software Devlopment

·?????What is Machine Learning?

Machine learning is a field of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed. It is based on idea that system learn from data, identify pattern and make decision with minimal human

Intervention.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for?building mathematical models and making predictions using historical data or information.

Types of machine learning

·??????Supervised machine learning: In supervised machine learning the model is provided with labelled data on basis of the data model predicts the output. the aim of a supervised learning algorithm is to?find a mapping function to map the input variable(x) with the output variable(y). Data is classified as X=input features and Y=label.

·??????Unsupervised machine learning: In unsupervised machine learning the model is trained using unlabelled data, model observes the data group the data according to similarities.This method has tow approach Clustering(grouping) and association(finding relationships between items in large database)

·??????Reinforcement learning: Reinforcement Learning (RL) is a subfield of machine learning and artificial intelligence that deals with how an agent can learn to make decisions through trial and error while interacting with an environment.

·??????What is DevOps?

DevOps combines of Dev(Development) and Ops(operations).DevOps is used to unite people, process and technology in application planning, development, delivery and operation. DevOps enables coordination and collaboration between roles like development, IT operations, quality engineering and security.

Because of the continuous nature of DevOps, practitioners use the infinity loop to show how the phases of the DevOps lifecycle relate to each other. Despite appearing to flow sequentially, the loop symbolizes the need for constant collaboration and iterative improvement throughout the entire lifecycle.

·??????What is MLOps?

MLOps is an extension of DevOps specifically tailored for Machine Learning projects. It brings together data scientists, ML engineers, and operations teams to create workflow for the development, deployment, and monitoring of ML models. MLOps fosters collaboration and ensures ML models are deployed reliably, securely, and at scale.

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·?The?Integration of ML,DevOps,MLOps

The Integration of ML, DevOps, MLOps helps in developing a scalable, robust and efficient solutions to meet demands of customer. Throughout the development the solutions including ML concepts doesnot remain static so it is necessary to ensure that algorithms are updated eveytime when changes take place this process is basically known as version control.

In Devops we basically use process called continuous Integration and continuous Deployment it helps in continuous development of new versions of software.CD/CI helps in improving software delivery and helps in developing code faster by automating CI(build, test, merge) and CD(deployment, monitoring, feedback).

By integrating ML, DevOps, and MLOps, organizations can accelerate their machine learning development lifecycle, reduce deployment friction, and deliver more reliable and maintainable ML applications

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