Ayush Singh

Ayush Singh

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Co-founder
Bengaluru, Karnataka, India

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Jobs verified_user 0% verified
  • Sbl so
    Co-Founder
    Sbl so
    May 2023 - Current (3 years 6 months)
  • Replayed
    Data Scientist
    Replayed
    Jun 2022 - Sep 2023 (1 year 4 months)
    - Responsible for developing a recommender system that could match a user's style preferences and input it into their outfit.
    - Collaborated with production engineers to deploy and test the system, ultimately improving the real-world usage of the product.
    - After doing several experiments to see which type of editor fit our creators best, we consulted with a tech lead at Meta to develop a matching system and hired interns to build the system.
    - Was a key factor in the company receiving a government grant for research and development, as well as recommending new hire candidates.
  • ZenML
    MLOps Engineer
    ZenML
    Feb 2022 - Jun 2022 (5 months)
    - In-charge of several production grade machine learning projects, including my work on reinforcement learning, computer vision and business problems.
    - Participated and provided suggestions in several core decisions while development of mvp and collaborated with several engineers to complete development of ZenML integrations.
    - Reviewed all of the code for several PR's, and Taken live sessions on Fundamentals of MLOps on ZenML.
  • Artifact
    Data Scientist
    Artifact
    Aug 2020 - Feb 2022 (1 year 7 months)
    - Utilised ML techniques to implement supervised learning techniques to build a model for identifying data issues in a fragment and developed the data pipeline to retrieve data from google cloud using SQL.

    - Applied specific best practices when creating our data processing, feature engineering, and model building pipelines and used precision, recall and a ROC to evaluate and re-iterate the model.

    - Formulated and developed an abstractive question & answering model in which we fine-tuned T5 Transformer from Hugging Face on SquadV2 dataset to build question & answering system using evaluation techniques like F1 Score and Jaccard index.
  • Artifact
    Data Scientist
    Artifact
    Aug 2020 - Feb 2022 (1 year 7 months)
    - Utilised ML techniques to implement supervised learning techniques to build a model for identifying data issues in a fragment and developed the data pipeline to retrieve data from google cloud using SQL.

    - Applied specific best practices when creating our data processing, feature engineering, and model building pipelines and used precision, recall and a ROC to evaluate and re-iterate the model.

    - Formulated and developed an abstractive question & answering model in which we fine-tuned T5 Transformer from Hugging Face on SquadV2 dataset to build question & answering system using evaluation techniques like F1 Score and Jaccard index.
  • ANTERNDATA SOLUTIONS
    Co-Founder
    ANTERNDATA SOLUTIONS
    Jan 2020 - Current (6 years 10 months)
    - Built the Team of 12+ members and created a consistent workflow of tasks and projects and kept the employee motivation in initial days.
    - Became one of the fastest growing AI-EdTech startup and touched 6 Figure recurring revenue every month.
    - Developed several strategies for marketing with 0 INR and filled the gaps in the edtech space, and working on achieving 80% placement rates of students.
    - Created a brand and a huge distribution through content creation on various social media like Youtube, Linkedin, twitter, Instagram.
    - Built strategies to defeat competitors in this huge crowd of "EdTech" and on the mission to change students lives.
    - Built and designed the Tech Infrastructure for the company with c
Education verified_user 0% verified
  • The Coding School
    Quantum Computing, Quantum Computing
    The Coding School
    Jan 2021 - Dec 2022 (2 years)
    Semester 1 Topics
    What is quantum computing?
    Classical and quantum computing stacks
    Introduction to quantum mechanics
    Qubits, quantum gates, quantum circuits
    Quantum measurements
    Programming quantum circuits with Qiskit
    Math for quantum states, gates, and measurements
    Introduction to quantum algorithms and protocols
    Quantum Key Distribution

    Semester 2 Topics
    Multi-qubit circuits and the CNOT gate
    Entanglement and the Bell states
    Superdense coding
    Grover’s Algorithm
    Near-term, hybrid algorithms
    Quantum chemistry and finance
    Quantum hardware
    Experimental metrics and implementation
  • St Karens High School
    Class 9, Computer Science
    St Karens High School
    Jan 2020 - May 2026 (6 years 5 months)
Projects (professional or personal) verified_user 0% verified
  • W
    Will they stay or will they go? Building a Customer Loyalty Predictor
    For a given customer's historical data, we are asked to predict whether a customer will stop using a company's product or not. We will be using the Telco Customer Churn dataset for building an end to end production-grade machine learning system that can predict whether the customer will stay loyal or not.
  • P
    Predicting how a customer will feel about a product before they even ordered it
    For a given customer's historical data, we are tasked to predict the review score for the next order or purchase. We will be using the Brazilian E-Commerce Public Dataset by Olist. Video: https://youtu.be/L3_pFTlF9EQ This dataset has information on 100,000 orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allow viewing charges from various dimensions: from order status, price, payment, freight performance to customer location, product attributes and finally, reviews written by customers. The objective here is to predict the customer satisfaction score for a given order based on features like order status, price,
  • C
    Creators-Editors Recommender System
    Responsible for developing a recommender system that could match a user's style preferences and input it into their outfit. Collaborated with production engineers to deploy and test the system, ultimately improving the real-world usage of the product. After doing several experiments to see which type of editor fit our creators best, we consulted with a tech lead at Meta to develop a matching system and hired interns to build the system.
  • A
    Atari - Solving Games with AI 🤖
    I will be using ZenML to build a model that can solve Atari games using reinforcement learning. I will be using the Atari 2600 game environment. I will be using the Deep Q-Learning algorithm to solve the game. will be using OpenAI Gym which is a toolkit that provides a wide variety of simulated environments (Atari games, board games, 2D and 3D physical simulations, and so on), so you can train agents, compare them. I will be using the BreakoutDeterministic-v4 environment from OpenAI Gym.
Awards verified_user 0% verified
  • Microsoft
    Microsoft Certified Azure Data Scientist
    Microsoft
    Apr 2021
  • Microsoft
    Microsoft Certified Azure AI Engineer Associate
    Microsoft
    Feb 2021
Publications verified_user 0% verified
  • ZenML
    Predicting how a customer will feel about a product before they even ordered it
    ZenML
    Apr 2022
    We built an end to end continuous deployment pipeline using ZenML for a customer satisfaction model that uses historical data of the customer predict the review score for the next order or purchase.
  • ZenML
    Solving Atari Games with Reinforcement Learning (AI)
    ZenML
    Mar 2022
    In the real world, building reinforcement learning applications can be challenging so I will be using ZenML (an MLOps framework) which allows for the deployment of models which can be used across the organization to build a reinforcement learning agent that learns to solve Atari Games.
  • Analytics Vidhya
    Image Classification Using CNN -Understanding Computer Vision
    Analytics Vidhya
    Aug 2021
    In computer vision, we have a convolutional neural network that is very popular for computer vision tasks like image classification, object detection, image segmentation and a lot more. Image classification is one of the most needed techniques in today’s era, it is used in various domains like healthcare, business, and a lot more, so knowing and making your own state of the art computer vision model is a must if you’re in a domain of AI.
  • Analytics Vidhya
    Hyperparameter Tuning Of Neural Networks using Keras Tuner
    Analytics Vidhya
    Aug 2021
    In neural networks we have lots of hyperparameters, it is very hard to tune the hyperparameter manually. So, we have Keras Tuner which makes it very simple to tune our hyperparameters of neural networks. It is just like that Grid Search or Randomized Search that you have seen in machine learning.
  • Analytics Vidhya
    Understand Machine Learning In Production for Data Science
    Analytics Vidhya
    Jun 2021
    MLOps are the must-know skills that machine learning engineers should know about. MLOps is a process of taking your model into the production system. A vast amount of work was done in achieving model accuracy in this era. But, if you’re building your product it’s not just about models. In this article, I discussed about MLOps, from Scoping a problem to deployment, Monitoring, and Maintaining the system.
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