Creating a Scalable Machine Learning Pipeline

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Creating a scalable machine learning pipeline is a complex task. I break down the pipeline into manageable pieces. Utilizing Google Cloud Services to automate where and what we can, so you can get back to creating custom models.

What you’ll learn

  • The course will focus on what to build once you have a Machine Learning Model. Allowing you to improve and monitor your deep learning model in production.

Requirements

  • Have some familiarity with Javascript, HTML and CSS
  • No Machine Learning experience needed

Description

I show you you everything you need to start using your tflite and tensorflow.js machine learning models in production. Create a website that allows users to upload images, get predictions from your custom machine learning model and review the performance of the model in real time.

Whether you already have a computer vision model or not I show you how to easily create one and ultimately use and deploy it to production. Learn how to use your own custom models with tensorflow.js,  allowing users to upload images and get predictions back on that image.

We create an entire pipeline that allows you to improve and monitor your machine learning model’s over time. Allow users to upload new images for predictions, saving those predictions and then using the new images as training data to improve our custom models performance.Who this course is for:

  • Software Developers, Data Scientists, Machine Learners, Entrepreneurs

We create an entire pipeline that allows you to improve and monitor your machine learning model’s over time. Allow users to upload new images for predictions, saving those predictions and then using the new images as training data to improve our custom models performance.

A machine learning pipeline is used to help automate machine learning workflows. They operate by enabling a sequence of data to be transformed

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