AI & Machine Learning

This article explores how to train Gaussian Mixture Models with the Expectation-Maximization Algorithm and implemente it in Python to solve unsupervised and semi-supervised learning problems.

One of the main limitations of the current state of Machine Learning and Deep Learning, is the constant need for new data.

This blog will speculate on how enterprise knowledge graphs (EKGs) will evolve to contain specialized functions and specialized subgraphs.

Graphs emerge in the context of users’ interactions with products in e-commerce platforms and as a result, there are many companies that employ GNNs for product recommendation.

AI technology powered wearable devices are continually making pathways to make a foray across untapped industries like telecommunication, finance, etc. The digital cosmos is pushing its boundaries every day to amplify user experience for a better living.

Enterprises drowning in the implications of data need an end-to-end management platform. Managing data on several public and private clouds can be too much to handle. Know what a good end-to-end data management system should include.

Learn how to combine weak learners to build a stronger learner to reduce bias and variance in your ML model

Semi-supervised learning is a brilliant technique that can come handy if you know when to use it. You can use semi-supervised learning, a machine learning technique that can automate the data-labeling process with a bit of help.

AI data storage systems can recognize patterns in arrays and stacks to predict storage issues and help solve them.

Data engineer is a team player who is working with data analysts, scientists, infrastructure engineers and other stakeholders. So do not forget about soft skills like empathy, understanding a business domain, open-mindedness etc.

There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.