Tag: pytorch

End to End Deep Learning: A different perspective

Himanshu Dutta

Whenever there is an article on an end-to-end deep learning project, it consists of training a deep learning model, deploying a Flask API, and then making sure it works or it extensively consists of creating a web demo using Streamlit or something similar. The problem with this approach is that it talks about a straight-forward and typical path that has been tried and tested. It merely takes replacing a single piece of the puzzle with an equivalent, such as a sentiment analysis model with a classification model, etc, and a new project can be created, but the wireframe remains mostly the same.

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Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning

Aishwarya Verma

Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling and training and TorchScript for serving.

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Why ML in production is (still) broken and ways we can fix it

Hamza Tahir

By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.

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Complete Guide to AutoGL -The latest AutoML Framework for graph datasets

Aishwarya Verma
Photographer keys hanging

Creating algorithms is difficult and time-consuming. This specific problem has inspired researchers to develop some productivity tools to help young members in this domain. This has given birth to a revolutionary field in Data Science called Auto Machine Learning(AutoML). AutoML provides methods and processes to make Machine Learning available to non-Machine Learning experts, to improve efficiency of Machine Learning and speed up the research.

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