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 […]
Deploy Serverless Machine Learning Model to AWS
In this post, we will deploy a serverless machine learning model to AWS using Serverless. The set-up of Serverless is discussed here. Let’s create a directory Windows 10, we can create a virtual environment and activate it as follows: Install Virtualenv In your VS Code command shell prompt type Start virtualenv Activate virtualenv On Windows, […]
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 […]
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. […]
Udacity Data Engineering Capstone Project
Project Summary The project follows the follow steps: Step 1: Scope the Project and Gather Data Step 2: Explore and Assess the Data Step 3: Define the Data Model Step 4: Run ETL to Model the Data Step 5: Complete Project Write Up Step 1: Scope the Project and Gather Data Scope The project is […]
Classical & Statistical Time Series Modelling of United Health Group’s Stock Price
Time series is different from a regular regression problem because it is time dependent. The basic assumption of a linear regression that the observations are independent doesn’t hold in this case. Along with an increasing or decreasing trend, most time series have some form of seasonality trends, i.e. variations specific to a particular time frame. […]
Highlight Text using Weights
Texts tend to have a hierarchical structure and the importance of words and sentences are highly context dependent. This post is a short tutorial to highlight text using sample weights. The text is displayed in Jupyter notebook. The weights can be from a model such as Logistic regression or Attention model. The different intensity of […]