50+ days of Machine Learning

 Hello :) Today is Day 196! A quick summary of today: Today I found this great resource for MLOps. Below I will summarise the posts I read A better pic of the above is on my repo . I learned about the above 'ML Canvas' concept from the below resources. Motivating MLOps Why MLOps? Machine learning (ML) models are increasingly being used in production environments, but their development and deployment are often disconnected from traditional software development and operations practices. This disconnection leads to various pain points, such as: Lack of collaboration: Data scientists, engineers, and operators work in silos, leading to inefficiencies and errors. Inconsistent workflows: Ad-hoc processes and manual interventions hinder reproducibility, scalability, and maintainability. Inadequate infrastructure: Insufficient infrastructure and tools lead to difficulties in deploying, monitoring, and updating ML models. Motivation for MLOps To address these challenges, MLOps aims to b

 Hello :) Today is Day 195! One of my lab mates sent me a few papers to skim through to help for his team's project related to predicting bank deposit subscriptions. Thanks to ChatGPT skimming is very easy now. Below are the outputs from ChatGPT on the five papers I got. Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions Introduction: Direct marketing is essential for personalized client communication in banking. Predictive analytics and machine learning offer new opportunities for refining marketing strategies. The research aims to enhance direct marketing's effectiveness by applying sophisticated analytical models. Literature Review: Examines eight studies on machine learning and data mining in banking. Highlights methodologies like the S_Kohonen network, Improved Whale Optimization Algorithm, META-DES-AAP, and various machine learning models. Emphasizes the importance of time deposits, customer credit products, and