While a vast majority of research efforts today are preoccupied solely with ML models and algorithms, the data itself tends to be secondary and is treated as fixed. This claim is potentially detrimental.
“The hardest thing to understand in the world is the income tax.” This quote comes from the man who came up with the theory of relativity – not exactly the easiest concept to understand. That said, had he lived a bit longer, Albert Einstein might have said “AI” instead of “income tax.” Einstein died in […]
From Oracle, to NoSQL databases, and beyond, read about data management solutions from the early days of the RBDMS to those supporting AI applications.
Introduction The word community has become a buzzword across the globe. Businesses have realized the power of community-led growth and are heavily invested in building and continuously giving to the audience. Well..guess what? At Analytics Vidhya, the community has been at the forefront since its inception with aim of building the best AI ML ecosystem […]
This article was published as a part of the Data Science Blogathon. This is the 2nd blog of the MLOps series. Introduction This article is part of an ongoing blog series on Machine Learning Operations(MLOps). In the previous article, we have gone through the introduction of MLOps. We have seen differences in traditional software development in […]
Feature Engineering on text data using Natural Language Processing Techniques. This article focuses primarily on text data feature engineering. Within the same process, we will be going over the following techniques and processes: Lemmatization / Stemming Count Vectorizer One Hot Encoding Train Test Split Principal Component Analysis Some general text cleaning and null value imputation […]
This article was published as a part of the Data Science Blogathon. Overview Machine learning (ML) has a lot of potential for increasing productivity. However, the quality of the data for training ML models should be excellent to provide good results. Any ML algorithm provides excellent performance only when there is huge and perfect data fed […]
This article was published as a part of the Data Science Blogathon. This article will provide you with a hands-on implementation on how to deploy an ML model in the Azure cloud. If you are new to Azure machine learning, I would recommend you to go through the Microsoft documentation that has been provided in the […]
This article was published as a part of the Data Science Blogathon. Table of contents Overview Traditional Software development Life Cycle Waterfall Model Agile Model DevOps Challenges in ML models Understanding MLOps Data Engineering Machine Learning DevOps Endnotes Overview: MLOps According to research by deeplearning.ai, only 2% of the companies using Machine Learning, Deep learning have […]
This article was published as a part of the Data Science Blogathon. Introduction AutoML is a relatively new and upcoming subset of machine learning. The main approach in AutoML is to limit the involvement of data scientists and let the tool handle all time-consuming processes in machine learning like data preprocessing, best algorithm selection, hyperparameter tuning, […]
