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AI-Powered Image Retrieval With Vector Databases
Kamalesh Kalirathinam
Kamalesh Kalirathinam
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AI-Powered Image Retrieval With Vector Databases

Develop an AI-driven application that uses machine learning models and vector databases to retrieve relevant images based on visual queries

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Apply now
Fridays
 at
7:00
P.M.
 ET /
4:00
P.M.
PT
8 weeks, 2-3 hours per week
Intermediate
No experience required
No experience required
Some experience required
Degree and experience required

Description

Efficient image retrieval systems are crucial across multiple sectors, including media, e-commerce, and education. This Build Project offers a gateway into the essential AI and machine learning work that underpins advanced technology careers, particularly in firms that are leading the innovation in digital content management and user interface technologies.  

During this Build Project, you will step into the role of a Data Scientist, tasked with developing an AI-powered image retrieval system using state-of-the-art technologies and methodologies. Guided by a seasoned industry professional, you will gain skills to conduct research and explore machine learning frameworks and develop software using vital production tools such as Python, PyTorch, FAISS, and Docker.

Session timeline

  • Applications open
    January 16, 2024
  • Application deadline
    February 18, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    March 10, 2025
  • Project end date
    Week of

What you will learn

  • Develop and implement an image retrieval application using Python, PyTorch, Streamlit, and Docker enabling image search capabilities using visual inputs.
  • Apply data cleaning and visualization techniques like t-SNE, PCA, and feature engineering to clean the training and validation datasets.
  • Identify and mitigate overfitting and underfitting of machine learning models by employing techniques such as regularization and data augmentation, ensuring that the model performs well not only on the training data but also on unseen data.
  • Evaluate neural networks using statistical performance measurements such as precision, recall, f1-score, and top-k accuracies for ensuring that the developed systems meet industry standards for accuracy and reliability.
  • Integrate the machine learning model with a vector database using FAISS to create an efficient and scalable image retrieval system allowing rapid querying and retrieval of image data

Project workshops

1
Project Environment Setup & Introductions
2
Data Exploration and Feature Extraction
3
Data Cleaning Techniques
4
Training Neural Networks
5
Hyperparameter Tuning
6
Vector Database Integration
7
Model Deployment
8
Final Presentation

Prerequisites

  • Intermediate exposure to Python: You should be comfortable with writing functions, using loops, conditions and importing libraries.
  • Basic knowledge of mathematical concepts like linear algebra, differential equations, probability and statistics.
  • Basic understanding of Linux, bash scripts, Jupyter notebooks and libraries like NumPy, Matplotlib, Pandas, scikit-learn and PyTorch.
  • Comfortable with installing packages along with reading, understanding, and debugging code for the project.

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About the expert

Kamalesh Kalirathinam is a Data Science Build Fellow at Open Avenues, where he works with students leading projects in Data Science.

Kamalesh is a Data Scientist at RadiusAI, where he focuses on developing and optimizing complex neural networks that can identify thousands of products in real-time, assisting cashiers with an AI solution in the retail space.

Kamalesh has over 3 years of experience in the Data Science field.  His expertise spans computer vision, natural language and deep learning, with a particular focus on real-world applications of AI technology.

He holds a Master’s Degree in Artificial Intelligence and Robotics.

A fun fact about Kamalesh is that he loves playing guitar and builds robots in his free time.

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Kamalesh
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