Using Google Cloud Functions (Part-3)

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Photo by Trà My on Unsplash

This is going to be the last article in this series where we will explore Google Cloud Functions as our third option of choice when deploying machine learning models. In the previous articles, we explored Tensorflow-serving and AI-Platform. Before we dive into setting up our cloud function let’s first briefly have a glance at what cloud functions are.

Cloud functions are triggered by events such as an HTTP call and used to invoke some other service or give immediate response without having to manage infrastructure. Cloud functions automatically scale based on the load and you only pay for what you use. One thing to mention here is that cloud-functions were not built specifically for machine learning tasks but as a multi-purpose solution so they might not be suitable for complex workflows but can serve well as an intermediate layer sitting between the client and the deployed model which is responsible for data pre-processing before passing it to a tensorflow-serving instance for the actual prediction. …

Using Google AI-Platform (Part-2)

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Photo by Cookie the Pom on Unsplash

In the previous article, we explored serving our tensorflow models using tensorflow-serving and how we can use Google Cloud to deploy our models in production using Docker and Kubernetes. If you haven’t explored the part-1 of this series I suggest you do, to get familiar with these terminologies and have a better understanding for following this guide.

In this article, we will focus on AI-Platform to deploy our model and then consume it in our application. AI Platform is basically a complete set of services that caters the whole ML-Pipeline from ingesting data till deployment. It covers data pre-processing, validating data, building ML-models, and finally deploying them, just to add further it doesn’t stop here, it also offers MLOps for implementing CI/CD for Machine-Learning Systems. …

Machine Learning Models beyond Jupyter-Notebooks.

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Screenshot of MNIST-Database plotted by the Author

This article focuses on deploying machine learning models using mnist handwritten digit recognition as a base example implemented in tensorflow-2. In the end, we will be cooking up a small web app in React to test our model. If you are a machine learning enthusiast then you already know that mnist digit recognition is the hello world program of deep learning and by far you have already seen way too many articles about digit-recognition on medium and probably implemented that already which is exactly why I won’t be focusing too much on the problem itself and instead show you how you can deploy your models and consume them in production. …

Fun weekend project — writing code to scrape memes from r/ProgrammerHumor

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Photo by Kon Karampelas on Unsplash

For a long time, I used to think scraping data from the internet was too boring, inspecting the webpage in DevTools, finding the DOM nodes of your interest — it seemed too much of a hassle to me.

Until, one day, I tried it using Beautiful Soup and was really inspired seeing how easy it is to play with the parsed dom and gather data of your interest.

Since then, I have been exploring the world of scraping and recently came across PRAW, which is the Python Reddit API Wrapper and makes it very easy to access Reddit data.

After exploring the package for a while, I really wanted to do a fun little weekend project and what’s better than writing code to scrape memes from r/ProgrammerHumor. …


asjad anis

Software Engineer | ML Enthusiast | Creative Programmer

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