Integration of Machine learning, DevOps and MLOps

Hello everyone,

As we all know that MLOps is very important aspect regarding any project nowadays. MLOps creates a bridge between data scientists and production teams. It is a practice that combines DevOps with Machine Learning. So basically this article talks about MLOps.

Now we will see what machine learning and DevOps is.

MACHINE LEARNING

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Firstly, in machine learning there are two words machine and learning here machine means the machine on which the algorithm is applied and it is defined as capability of a machine to imitate intelligent human behavior and the learning means observation and understanding of the data.

Machine learning (ML) allows software applications to become more accurate at predicting outcomes. Machine learning algorithms use data as input to predict new output values. Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images. When companies today deploy artificial intelligence programs, they are most likely using machine learning so much so that the terms are often used interchangeably, and sometimes ambiguously.

“In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done,” said MIT Sloan professor Thomas W. Malone, the founding director of the MIT Centre for Collective Intelligence. “So that's why some people use the terms AI and machine learning almost as synonymous … most of the current advances in AI have involved machine learning.”

There are 4 types of machine learning: -

1) Supervised learning: This machine learning type got its name because the machine is “supervised” while it's learning, which means that you’re feeding the algorithm information to help it learn. The outcome you provide the machine is labelled data, and the rest of the information you give is used as input features.

Categorization of Supervised machine-learning: 

-  Classification: When inputs are divided into two or more classes, the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are “spam” and “not spam”.

-  Regression: Which is also a supervised problem, A case when the outputs are continuous rather than discrete.

2) Unsupervised learning: While supervised learning requires users to help the machine learn, unsupervised learning doesn't use the same labelled training sets and data. Instead, the machine looks for less obvious patterns in the data.

-  Clustering: When a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.

3) Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Machine is provided with mostly labelled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.

4) Reinforcement learning: Reinforcement learning is the closest machine learning type to how humans learn. The algorithm or agent used learns by interacting with its environment and getting a positive or negative reward. In this user don’t have any type of guidance. In this type of learning two tables are maintained i.e., state table and memory table.                                                    

In state table every state is stored and in memory table information related to every state is maintained in it. Agent learns from previous state and behaves accordingly.

DevOps

The term DevOps is a combination of two words namely Development and Operations. The development and operation teams collaborate on tasks to achieve faster software delivery in the DevOps Lifecycle, which comprises a set of phases.

DevOps is a practice that allows a single team to manage the entire application development life cycle, that is, development, testing, deployment, and operations. The aim of DevOps is to shorten the system’s development life cycle while delivering features, fixes, and updates frequently in close alignment with business objectives.

The DevOps lifecycle (sometimes called the continuous delivery pipeline, when portrayed in a linear fashion) is a series of iterative, automated development processes, or workflows, executed within a larger, automated and iterative development lifecycle designed to optimize the rapid delivery of high-quality software.

phases of DevOps life cycle : -

1)     Requirement gathering

2)     Planning

3)     Development & Testing

4)     Deployment

5)     Monitoring

6)     Feedback

 MLOps

Machine Learning Operations (MLOps) is a collection of tools and best practices for improving communication across teams and automating the end-to-end machine learning life cycle to improve continuous integration and deployment efficiency. It is a concept that refers to the merger of long-established DevOps methods with the growing science of Machine Learning.

MLOps encompasses more than model construction and design. It includes data management, automated model development, code generation, model training and retraining, continuous model development, deployment, and model monitoring. Incorporating DevOps ideas into machine learning offers a shorter development cycle, improved quality control, and the ability to adapt to changing business needs.

MLOps is a useful approach for the creation and quality of machine learning and AI solutions. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and increase the pace of model development and production, by implementing continuous integration and deployment (CI/CD) practices with proper monitoring, validation, and governance of ML models. It combines DevOps with machine learning.

 So that’s it about the introduction of MLOps and further we will continue MLOps in deep in the next article.

Thank You Kushal Sharma Sir for this wonderful session. Thank you AISSMS Institute of Information Technology for organizing this value added course.

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