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Building ML Powered Web Applications using Python, TensorFlow Hub & Gradio
Description
With ML becoming more mainstream, the need for reinventing the wheel has decreased & there is very little entry barrier for creating ML powered applications. TensorFlow Hub is an open repository and library for reusable machine learning. The repository provides many pre-trained models: text embeddings, image classification models, TFjs/TFLite models and much more. The repository is open to community contributors. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! In this session, I'll demonstrate how one can use Python, TensorFlow Hub & Gradio to create a fully functional ML powered web application.
Table of contents
In this session, I'll demonstrate how one can use Python, TensorFlow Hub & Gradio to create a fully functional ML powered web application.
TensorFlow Hub - an open repository and library for reusable machine learning
Gradio - fastest way to demo your machine learning model
Duration (including Q&A)
30 mins
Prerequisites
No response
Speaker bio
I am Bhavesh Bhatt - I am a Google Developer Expert (GDE) in Machine Learning. I am a Data Scientist based out of Mumbai, India. My primary interests include Computer Vision, Machine Learning, Deep Learning. I have designed & deployed multiple machine learning & deep learning models that have had a significant business impact.
I have also been awarded the prestigious 40 Under 40 Data Scientist award.
I am humbled to share that I have been recognized by GitHub as a GitHub Star.
I am also extremely humbled to have more than 1700+ followers on GitHub.
I have worked with multiple EdTech startups like Great Learning, GreyAtom and upGrad in delivering sessions & developing their machine learning course curriculum. I have also closely worked with Data Science aspirants to help them transition into a Data Science Career.
In order to give back to the community from which I learnt so much I started creating videos on YouTube & currently I have close to 320 videos with 3 Million views & 40k+ subscribers.
The talk/workshop speaker agrees to
Share the slides, code snippets and other material used during the talk
Title of the talk
Building ML Powered Web Applications using Python, TensorFlow Hub & Gradio
Description
With ML becoming more mainstream, the need for reinventing the wheel has decreased & there is very little entry barrier for creating ML powered applications. TensorFlow Hub is an open repository and library for reusable machine learning. The repository provides many pre-trained models: text embeddings, image classification models, TFjs/TFLite models and much more. The repository is open to community contributors. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! In this session, I'll demonstrate how one can use Python, TensorFlow Hub & Gradio to create a fully functional ML powered web application.
Table of contents
In this session, I'll demonstrate how one can use Python, TensorFlow Hub & Gradio to create a fully functional ML powered web application.
Duration (including Q&A)
30 mins
Prerequisites
No response
Speaker bio
I am Bhavesh Bhatt - I am a Google Developer Expert (GDE) in Machine Learning. I am a Data Scientist based out of Mumbai, India. My primary interests include Computer Vision, Machine Learning, Deep Learning. I have designed & deployed multiple machine learning & deep learning models that have had a significant business impact.
I have also been awarded the prestigious 40 Under 40 Data Scientist award.
I am humbled to share that I have been recognized by GitHub as a GitHub Star.
I am also extremely humbled to have more than 1700+ followers on GitHub.
I have worked with multiple EdTech startups like Great Learning, GreyAtom and upGrad in delivering sessions & developing their machine learning course curriculum. I have also closely worked with Data Science aspirants to help them transition into a Data Science Career.
In order to give back to the community from which I learnt so much I started creating videos on YouTube & currently I have close to 320 videos with 3 Million views & 40k+ subscribers.
The talk/workshop speaker agrees to
Share the slides, code snippets and other material used during the talk
If the talk is recorded, you grant the permission to release
the video on PythonPune's YouTube
channel
under CC-BY-4.0
license
Not do any hiring pitches during the talk and follow the Code
of
Conduct
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