MLOps | Ukant Jadia

Food Image Classification

Mon, 01 Jan 2024 00:00:00 +0000

This project tackles the challenge of applying computer vision to emphasize accurate food image classification. It compares Convolutional Neural Networks (CNN) with Traditional Machine Learning Models, leveraging CNN’s feature-rich capabilities and traditional models’ transparency and computational efficiency. This dual approach enhances food image classification’s precision and efficiency, impacting quality assessment, dietary analysis, and food inventory management

Used the 5K image dataset for classifying the food images and non-food images, Trained the neural network on the images for classification, and developed the model with streamlit application and achieved 80% accuracy. The dataset is taken from https://mmspg.epfl.ch/food-image-datasets and the name of the data set is 5K.

Dataset description: -. This dataset contains 2500 food and 2500 non-food images, for the task of food/non-food classification in our paper “Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model”. The whole dataset is divided into three parts: training, validation and evaluation. The naming convention is as follows: -. {ClassID}_{ImageID}.jpg -. ClassID: 0 or 1; 0 means non-food and 1 means food. -. ImageID: ID of the image within the class

Bloom's Taxonomy Level Detection

Wed, 13 Dec 2023 00:00:00 +0000

To know Bloom’s Taxonomy in Detail see this pdf This

This project aims to classify the given statement into accurate Bloom’s taxonomy levels out of six levels. This is an NLP-based project. This project is built with TF-IDF vectorizer and multinomial Naive Bayes techniques.

Bloom’s Taxonomy Levels: Bloom’s Taxonomy is a classification system that arranges learning objectives into six hierarchical levels, each representing a different cognitive skill.

The model is trained on 1800 augmented statements. Initially, I had 100 statements for each class, so a total of 600 statements for six classes. Then, this data is augmented three times using contextual word embedding augmentation, and the model used in the augmentation is BERT-based. An example of the augmentation methodology.

This project follows MLOps techniques well. I have divided this project into 6 different pipelines for easy implementation and scalability. I created a class in the pipeline for each class using different entities and components. In generating a configuration file from the user input in the YAML format.

In the end, I created a Docker image so it can be used by the Higher Education Institute(HEIs) for those who are using the OBE(Object Based Education) new curriculum technique. It just reduced the manual process of identifying each question and its root taxonomy level.

A MLOps Project- Wine Quality Prediction

Fri, 08 Dec 2023 00:00:00 +0000

This project demonstrates my ability to implement a highly scalable MLOPS project by following development techniques and project procedures. In this project, I divided the whole implementation part into six different pipelines and steps, which are as follows:

Data Ingestion: Collecting the data and setting it up for further use.

Data Validation: Checking the data type, format, and other conditions to see the validity and usability of the data.

Data Transformation: Performing preprocessing and splitting the data into test and train datasets.

Model Training: Selecting a suitable machine learning algorithm and creating a model by training.

Model Evaluation: Testing the data and verifying it with measurement/result metrics.

Model Prediction: Making predictions with user input.

Each pipeline step followed a particular workflow in order to complete the pipeline successfully. See the following:

Update config.yaml
Update schema.yaml
Update params.yaml
Update the entity
Update the configuration manager in the src config
Update the components
Update the pipeline
Update the main.py
Update the app.py

Follow the above workflow based on your current pipeline.

Follow this markdown link to generate the base structure of your root repository for your project in order to implement MLOPS.

Uses a mail flow for model registry and the model monitoring and model can be deployed from ML flow only

Here is the tracking URL of my Molde link