Introduction to ML,DevOps,MLOps.

Hello #connections Rohit Utekar here! This was my first day of learning ML,DevOps and MLOps with Kushal Sharma

First, let us see the meaning of Machine learning , it 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.

Why Machine Learning? @machine learning is important because it gives enterprises a view of trends in customer behavior and operational business patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations.

Working of ML

History of ML

Classification of ML

There are three Learning types:

1)Supervised Learning Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

for e.g we have a dataset of different types of shapes which includes square, rectangle, triangle, and Polygon. Now the first step is that we need to train the model for each shape.

If the given shape has four sides, and all the sides are equal, then it will be labelled as a Square.

If the given shape has three sides, then it will be labelled as a triangle.

If the given shape has six equal sides then it will be labelled as hexagon.

Now, after training, we test our model using the test set, and the task of the model is to identify the shape.

The machine is already trained on all types of shapes, and when it finds a new shape, it classifies the shape on the bases of a number of sides, and predicts the output.

2)Unsupervised Learning  Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

for e.g

3)Reinforcement Learning Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

• for e.g Your cat is an agent that is exposed to the environment. In this case, it is your house. An example of a state could be your cat sitting, and you use a specific word in for cat to walk.
• Our agent reacts by performing an action transition from one “state” to another “state.”
• For example, your cat goes from sitting to walking.
• The reaction of an agent is an action, and the policy is a method of selecting an action given a state in expectation of better outcomes.
• After the transition, they may get a reward or penalty in return

DevOps

Definition. DevOps (a portmanteau of “development” and “operations”) is the combination of practices and tools designed to increase an organization's ability to deliver applications and services faster than traditional software development processes.

Some phases of DevOps

MLOps

Till now we just learnt the pre-requisite knowledge we need before learning MLOps. MLOps is the concept which integrates both the concepts of Machine Learning and DevOps.

MLOps, short for "Machine Learning Operations," refers to a set of practices and methodologies that combine machine learning (ML) and artificial intelligence (AI) with DevOps principles to streamline and automate the end-to-end machine learning lifecycle. 

It aims to facilitate collaboration, communication, and integration between data scientists, ML engineers, and operations teams to effectively develop, deploy, monitor, and manage machine learning models in production environments.

MLOps LifeCycle

Thank you Kushal Sharma sir for wonderful session and guidance.

Thank you AISSMS Institute of Information Technology for organizing this value addition course.

#machinelearning #mlops #devops