Contains notebooks prepared for ML Bootcamp organized by Google Developers Launchpad.
Contains notebooks prepared for a Machine Learning Bootcamp organized by Google Developers Launchpad.
Accompanying deck link: http://bit.ly/mlb-sayak.
Typical machine learning steps:
Problem understanding
Data collection and understanding
Data preprocessing
Model building and training
Hyperparameter tuning, revaluate model and repeat
Model deployment
Serving prediction and model monitoring
Optionally, you might want to optimize your model with respect to its target deployment environment and then deploy it. In this Bootcamp, we are going to take a look at each of the above steps. We will be using TensorFlow (2.0) as our main library for doing machine learning - we will be doing deep learning which is another form of machine learning only. Specifically, we will be using TensorFlows high-level Keras API for model building and training.
We will be using the Flowers-17 dataset. So, for a given flower an ML model needs to predict the most likely category of the flower. In machine learning and computer vision literature, this problem is commonly referred to as an image classification problem and it kind of dominates the kind of applications that have been made possible by the intersection of machine learning and computer vision.
We will also be using several Google Cloud Platform (GCP) services like AI Platform, Google Cloud Storage. But note that all of the typical machine learning steps we saw earlier can be performed on your local computer. So why use cloud services and incur that additional cost? That is because your enterprise does not operate on a local computer. It is most likely to serve tens of thousands of end-users. Your local machine might not have that bandwidth to support this kind of scaling whereas cloud providers like GCP offer you off-the-shelf and specific things to achieve whatever scale you want to cater to. Typical cloud functionalities that come to aid you in this regard are -
You might want to autoscale your infrastructure to be able to speed up the inference time of your ML model.
You would really want to add a strong authentication layer just before your model is consumed as a REST API.
You would want to orchestrate the version controlling of your models.
You dont have the on-premise infrastructure to support large training workloads of your model. So, you might want to go for Cloud-based VMs that support GPUs easily.
And much more.
AI Platform helps you achieve all of the above in a pretty customizable way as we will see in this Bootcamp. It does not encourage just one click deployments but it provides you with enough tools to tweak the novel bits necessary for your deployment and prototyping purposes.
Problem: Given an image of a flower, the system would predict the category to which the flower is most likely gonna belong to.
Data available to represent the problem space? - Yes, Flowers-17 dataset.
Data collection
Data preprocessing
Data input pipeline
ImageDataGenerator
ImageDataGenerator
tf.data
to speed things uptf.data
Model building and training
Tuning the learning rate hyperparameter and faster training
Model deployment
Serving predictions
We will be using TensorFlow 2.0, Google Cloud Platform and Weights and Biases and Python 3 (of course!). Let's get started!