MLOps

Introduction to Machine Learning Lifecycle

Reading Time: 3 minutes Building a machine learning model is an iterative process. For a successful deployment, most of the steps are replicated several times to achieve optimal results. The model must sustain after deployment and adapted to changing environment. Let’s look at the details of the lifecycle of a machine learning model. What is machine learning lifecycle? The machine learning lifecycle is the process of developing, deploying, and Continue Reading

TensorFlow Recommenders (TFRS): An Overview

Reading Time: 4 minutes Hey Guys, Aren’t you surprised, when you watch any video on youtube or any movie on Netflix or look for any product on an E-Commerce website?You start receiving similar kinds of videos, movies, and products suggestion on respective platforms.So, how do platforms do that?.Well, they use recommender systems, an important application of machine learning, surfacing new discoveries and helping users find what they love.In this Continue Reading

K-Means-Algorithm

Reading Time: 3 minutes Machine Learning has gained popularity in the last couple of years and has witnessed an exponential rise in its usage. It gives a computer/machine to act without being explicitly programmed. Unsupervised learning is a technique to model the underlying structure or distribution in the data. It enables us to learn more about the data without providing any pre-assigned labels or scores for the training data. Continue Reading

Getting Familiar with Activation Function and Its Types.

Reading Time: 7 minutes Hey Folks, In this blog we are going to discuss activation function in Artificial Neural Networks and their different types. Before going there, let’s get some idea about what is an artificial neural network? Artificial Neural Network(i.e., ANN) Artificial Neural Network refers to a biologically inspired sub-field of Artificial Intelligence modeled after the brain. ANN is a computational network based on a biological neural network Continue Reading

Migrating MLFlow Server To Cloud: Part 2

Reading Time: 4 minutes In my previous blog, I had discussed the first two phases of migrating MLFlow server to cloud. In this blog, I’ll be discussing the deployment of MLflow tracking server on Google Cloud Platform and migration of the existing data to the process. Also, I’ll be talking about optimizing the overall environment in the process. Deployment Step 1: Copy Contents from Disk Go to this link Continue Reading