Archives for model deployment

15 Jun

FastAPI vs Flask: Comparison Guide for Data Science Enthusiasts

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image-23573

Data Science being a multidisciplinary area, is not only restricted to creating problem-specific models. One of the challenges faced by people working in this field is deploying any ML model. But nowadays, it is pretty straightforward to deploy or test your machine learning model at the production level. This is an essential step because not…

The post FastAPI vs Flask: Comparison Guide for Data Science Enthusiasts appeared first on Analytics India Magazine.

14 Mar

Guide to TensorFlow Extended(TFX): End-to-End Platform for Deploying Production ML Pipelines

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image-20840

Ever since Google has publicised Tensorflow, its application in Deep Learning has been increasing tremendously. It is used even more in research and production for authoring ML algorithms. Though it is flexible, it does not provide an end-to-end production system. On the other hand, Sibyl has end-to-end facilities but lacks flexibility. Google then came up…

The post Guide to TensorFlow Extended(TFX): End-to-End Platform for Deploying Production ML Pipelines appeared first on Analytics India Magazine.

28 Jan

Workshop Alert! Hands-on Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday. Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare,…

The post Workshop Alert! Hands-on Deep Learning Model Deployment & Management appeared first on Analytics India Magazine.

20 Oct

Complete Guide To Model Deployment Using Flask in Google Cloud Platform

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image-16927

In real-world, training and model prediction is one phase of the machine learning life-cycle. But it won’t be helpful to anyone other than the developer as no one will understand it. So, we need to create a frontend graphical tool that users can see on their machine. The easiest way of doing it is by deploying the model using Flask.

In this article, we will discuss how to use flask for the development of our web applications. Further, we will deploy the model on google platform environment.

The post Complete Guide To Model Deployment Using Flask in Google Cloud Platform appeared first on Analytics India Magazine.