Sep 17, 2020
Buiding Models is exciting, putting them to use can be very brain-tasking. For people who have mastered model deployment, usually this involves just a few tweaks to the code to make it work for a new problem and situation. For beginners like you and me, the task of scaling models for prediction for use in different environment requires a lot of information gathering, software set-up and a little bit of technical know-how. In this article, my aim is to simplify this process for you and show you the methods and steps you can immediately put to use in your projects.
These are the steps guiding us through this article:
As a beginner, usually you spend most of your time building models by leveraging blog posts tutorials, kaggle data-science community or follow-up from courses you take. At least this was what most of my time has been spent doing.
The beauty about model deploment and prediction is that you can see how your modelperforms in a real-world situation which is an important step that develops a very useful skill every Machine Learning Enginner should possess. No-one wants an enginner that cannot solve problems with their models because it remains sitting on google colab or on thier machine.
There is one quality every smart ML Enginner or Data-Scientist possesses and that is “the ability to know the best procedure for every unique task or problem”. I say this because, in deep learning no two project is the exact same thing, there are always a few tweaks that must be made to fit a certain unique problem. In model deployment, this same fact holds true and later in the article, an example would be given that portrays how two problems has different routes that ensures the best results. That said, it is necessary to understand the various methods of how you can make scalable predictions from your deep learning models.
It is assumed that you have some practise building DL models, at least you have been doing this for a month upwards. This way, and at this point you would know the process of building models with CNNs, RNNs, ML Algorithms and extended libraries and also saving models in your preferred format. Just in case this is not the case for you or perhaps you just need some brush up summary, I would dive briefly explaning how to build deep learning models.
In this article, I refer to DL models built with Tensorflow, on top of Keras. I use this for two main reasons. One is that, I have the best practise uilding models with Tensorflow and Two, I believe that keras has very easy steps to build models and there exist many methods for deployment and scalable predictions.
Keras is a high-end library for building ML and DL models. It is built on top of Tensorflow and utilizes it’s many tools, functions and libraries for data loading,visualization,pre-processing and the wholw process involved in model building.
Most ML and DL task can be carried out in Keras like Computer Vision, Natural Language Processing, Style Transfer, Transfer Learning, Gans, pretty much any DL task can be carried out using keras. If you want some folllow up tutorials for Dl in keras, I suggest you visit Tensorflow oficial website here.
Making predictions with models is a successding step of model deployment. In a way, it can be used interchangably. “I want to deploy my model”, for what?,”to make predictions”.
This section holds the main content of this article, where I would give you the methods, merits/demerits and all the necessary steps you would need to take to apply any of the scalale predictions and deployments routes below. Before we get to that, I want to take a moment to answer some questions beginners usually ask. If you don’t consider yourself a beginner, go ahead and move to the next section.
“Is model deployment necessary to a beginner in ML and DL” - No, this should not be something that you spend time on trying to implement because this is going to waste a lot of your time that could be better invested in practising with simple projects
“Can I deploy a model that doesn’t solve a very worthy real-world problem”- I absolutely believe that you can do this if you have the time and resources for it because it serves as a learning process and if you start building models solving simple problems, you would soon start building advanced ones if you persist in it.
“What is the main point of model deployment”- The main point is to provide the function of your deep learning model to a wide audience of people and that it should be easily accessile from various devices.
“As a beginner, what would be the point of deploying a model aside from feeling good with myself”- Models are meant to solve problems, so yhh if you can build a model channeled to solve or simplify a particular problem, you can distribute this and have people use it as a paid or free service. This is very logical, and what most business do just in advanced applications.
These were questions that troubled me when I got to know what deployment is all about. I hope that they could be of some help to you. Below I am going to take you through the process of model deployment and how you can go about it. Using this guide, you should get through in a day what would naturally have taken upwards of a week.
That’s it, with these guidelines, the whole process of deplotyment should be less intimidating to you. As a deep learner, this is a very important part of the process, and it is helpful to get accustomed to the process earler on.
Thanks for learning about deployment with me today, it is understandable if you have a few questions on your mind after taking in this huge pile of guidelines and directions, don’t hesitate to leave me a comment and I’d be sure to get back to you. Chel_