Co-Founder and CTO at BilberryDB

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Ravinthiran Partheepan • Peerlist

Why did we build this product?With BilberryDB, you don’t need to be a highly skilled developer or even a technical professional.We built this product so that anyone, even without technical knowledge, can easily build their own AI engine.What's unique in our Tech Product?- Data is expensive, and without data, you can’t build AI models.Typically, if you want to build a domain-specific machine learning or deep learning model to solve a problem, you need a large amount of data to help the model learn complex patterns and find accurate solutions.In our case, we solve this huge need for large datasets and cut down data collection costs, because with BilberryDB, you can build a useful AI solution using just a few data samples.- Building a data solution or data pipeline for an AI system usually involves many steps such as data preprocessing, ML/DL implementation, data warehousing, CI/CD, full-stack development, and more.In our case, you can skip all these time-consuming and tedious steps with our no-code builder.Simply integrate your data, be it 3D models, video, audio, tabular, or text and our app wizard will take care of everything, from data preprocessing to full-stack app readiness.Then, you just click “Deploy as an App” and that’s it! You’ll instantly have your own AI tool that answers questions based only on your multimodal data including 3D, video, audio, tabular, and text modalities.

Bilberrydb

Course Link: https://www.udemy.com/course/automating-ml-pipelines-for-song-recommendation-system/

What you’ll learn:

Understand the Math Behind ML Algorithms: You will learn the mathematical concepts that underlie popular machine learning algorithms.

Implement Machine Learning Algorithms: You will gain hands-on experience in coding and applying various machine learning algorithms.

Design and Build MLFlow Tracking: You will learn how to use MLFlow for tracking and managing machine learning experiments effectively.

Implement Microservices with Docker: You will learn how to create and manage microservices for automating machine learning pipelines using Docker.

Automate Model Training and Evaluation: You will learn to use Airflow triggers to automate the process of training and evaluating machine learning models.

Set Up Git CI/CD for a Song Recommender App: You will learn how to implement CI/CD for a song recommendation web app.

Udemy - Automating ML Pipeline for Song Recom

Who is this Book For?

Aspiring Data Scientists: Lay a strong foundation for your data science career by mastering the mathematical principles that drive data analysis.

Data Analysts and Professionals: Enhance your analytical skills and take your career to new heights by gaining deeper insights into the mathematics of data science.

Educators and Students: Use this book as a comprehensive resource for teaching and learning the mathematical aspects of data science.

Buy Now: https://a.co/d/2RTZ7Oo

Vertez - Where Data Science and Math Create a

ArkGAN - An open-source Generative Adversarial Networks toolkit written in Rust

ArkGAN - An open-source Generative Adversaria

Won Award: 4000€ prize from the Ministry of the Environment of Finland (Ympäristöminist)

Circomy - Accelerating the transition towards

Link: https://2021.spaceappschallenge.org/challenges/statements/mapping-space-trash-in-real-time/teams/disneys-pluto/project

NASA Space App Challenge (2021) - Space Debri

Simplify complex scientific computations with Vertez's intuitive library.

pypi.org/project/vertezml/

VertezML (Py): Open source ML Library

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[IEEE] Cognitive Health: EEG analysis of huma

Link: https://www.researchgate.net/publication/352830774_Augmented_Reality_for_Construction_Sector_in_Desktop_and_Smartphone_Application

[KTU IVUS 2020] Augmented Reality for the con

Simplify complex scientific computations with Vertez's intuitive library.

https://www.npmjs.com/package/vertezml

Veretzml (Typescript): An open source ML Libr

Copyright Number: https://copyright.gov.in/frmstatusgenuser.aspx [(Diary No: 10659/2020-CO/L) ]

Link: https://www.google.co.in/books/edition/Technical_Aspects_of_PB_CUP_Learner/Y4gHEAAAQBAJ?hl=en&sa=X&ved=2ahUKEwj6k53DiaH8AhUcSGwGHTGnDPUQiqUDegQIAxAC

PB-Cup Neural Learner (Inspired From Artifici

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[ICASISET 2020]Breast Cancer Prognosis with D

Link: https://link.springer.com/chapter/10.1007/978-3-030-84337-3_3