The AWS Machine Learning Framework: A comprehensive guide to bringing models to production
Data scientists develop a functioning model using a machine learning framework and some data. The developed model has a low error rate and a set of hyperparameters optimized. But when companies decide to apply this model to a production environment, they realize model building as the first step. There is a greater challenge. Converting functioning […]
Discover AWS Serverless way to Deploy Food Recipe Application in 7 Easy Steps
This post shows AWS serverless architecture to deploy a machine learning application. I will deploy the food recipe application as discussed here using AWS Fargate. Fargate is a service provided by AWS Serverless computing that removes the need to provision and manage servers. You can specify and pay for resources per application and improves security […]
How to Quickly Save a Trained Machine Learning Model
This blog post discusses how to quickly save a trained machine learning model that suggests recipes based on the user’s data, and nutritional value using Joblib and Pickle. The reason I decided to work on this is that the domain of food is varied and complex and presents many challenges to developing a recommender system. […]
7 ShortListed Free and Decisive MLOps Tools for Food Recipe Application
This article discusses Machine Learning Model Operationalization Management (MLOps) tools to develop and deploy a food recipe application. It lists the tools that are framework-, platform-, and infrastructure-agnostic using Python for development. To find the ideal tool for the MLOps task, a decision-making and analysis process is employed. The goal is to design an end-to-end […]
Data-driven Analytics Support for E-commerce
This post details how to develop data-driven analytics support for e-commerce. E-Commerce is a fast-growing and highly competitive space. Businesses need to continue testing and iterating to improve business operations, stand out from the competition and ensure that it is moving in the right direction. The GitHub repository has code for a web application that […]
Full-stack Deep Learning Application Using AWS Fargate Serverless Infrastructure
This post showcases full-stack Deep Learning Application Using AWS Fargate Serverless Infrastructure. The project we optimise a model for Utilisation of hospital beds – COVID-19. It is motivated by the overcrowding of hospital beds due to COVID-19. Another reason is to avoid the repeat of the events which happened at the beginning of the pandemic. […]
AI for Healthcare
This post provides a link to my Github repository for my submissions for Udacity’s AI for Healthcare Nanodegree Program. I learned to build, evaluate, and integrate predictive models that have the power to transform patient outcomes and uses AI for Healthcare. I started by classifying and segmenting 2D and 3D medical images to augment diagnosis and then moved on to modelling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, I build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion. Applying AI to 2D Medical Imaging Data I learnt the fundamental skills needed to work with 2D medical imaging data and how to use AI to derive clinically-relevant insights from data gathered via different types of 2D medical imaging such as x-ray, mammography, and digital pathology. In this project, I analyzed data from the NIH Chest X-ray dataset and trained a CNN to classify a given chest X-ray for the presence or absence of pneumonia. First, I curated training and testing sets that are appropriate for the clinical question at hand from a large collection of medical images. Then, I created a pipeline to extract images from DICOM files that can be fed into CNN for model training. Lastly, I wrote an FDA 501(k) validation plan that formally describes my model, the data that it was trained on, and a validation plan that meets FDA criteria in order to obtain clearance of the software being used as a medical device. AI for Healthcare is used in the project. Applying AI to 3D Medical Imaging Data I learnt the fundamental skills to work with 3D medical imaging datasets and frame insights derived from the data in a clinically relevant context. In this project, I went through the steps to create an algorithm that will helps clinicians assess hippocampal volume in an automated way and integrated this algorithm into a clinician’s working environment. Hippocampus is one of the major structures of the human brain with functions that are primarily connected to learning and memory. The volume of the hippocampus may change over time, with age, or as a result of the disease. In order to measure hippocampal volume, a 3D imaging technique with good soft-tissue contrast is required. MRI provides such imaging characteristics, but manual volume measurement still requires careful and time-consuming delineation of the hippocampal boundary. Applying AI to EHR Data I learnt the fundamental skills to work with EHR data and build and evaluate compliant, interpretable models. In this project, I worked with real, de-identified EHR data to build a regression model to predict the estimated hospitalization time for a patient and select/filter patients for the study. I analyzed an EHR dataset, transform it to the right level, build powerful features with TensorFlow, and modelled the uncertainty and bias with TensorFlow Probability and Aequitas.Applying AI to Wearable Device Data I learnt how to build algorithms that process the data collected by wearable devices and surface insights about the wearer’s health. […]
Novelty, Anomaly and Segmentation Discovery using Matrix Profile
In this notebook, novelty and anomaly and segmentation discovery using Matrix Profile. We are using Stumpy for time series data mining tasks. We’ll examine a data set containing daily opening values for the United Health Group from 2016 up to present day. UnitedHealth Group Incorporated is an American for-profit managed health care company based in Minnetonka, […]
Developing a Web Application for a Machine Learning Model
This post describes developing a web application for a machine learning model and deploying it so that it can be accessed by anyone. The web application is available at: https://arrear-model.herokuapp.com/ The process of deployment consists of transferring all flask application files from a local computer to the web server. Once completed the web application can […]
Jobs which are most susceptible to automation
Throughout history, the technological advances have raised fears that traditional jobs will become obsolete. In this post, I find out the jobs which are most susceptible to automation. Elon Musk told the National Governors Association: “There certainly will be job disruption. Because what’s going to happen is robots will be able to do everything better […]