Machine learning is rapidly becoming as ubiquitous as data itself. Quite literally wherever there is an abundance of data, machine learning is somehow intertwined. After all, what utility would data have if we were not able to use it to predict something about the future? Luckily there is a plethora of toolkits and frameworks that have made it rather simple to deploy ML in Python. Specifically, Sklearn has done a terrifically effective job at making ML accessible to developers.

Many Data Science / Engineering enthusiasts out there have that dream of successfully deploying there Machine or Deep learning models into a usable Application. I also had this dream and I still do for many of my models yet to be deployed. This then prompted me to learn and map out the necessary pathways to be followed to successfully deploy a Machine Learning model. One other reason for this article is to prevent people from facing some issues I encountered during the course of deploying my model, one of which is the 17-times crashing of the model before the final deployment. This article contains the pathway and important links to articles, free books and videos to aid your model deployment.

If you are a data scientist or data science student, you know how machine learning works. You know the details of the algorithms, which libraries to use, and perform diagnostics. Let's say that in a business environment, you have the perfect model in your hands, with excellent results and ideal everything. It is indeed tempting to suggest that we just hand over the code to the relevant stakeholders and ask them to run it if they want to see results. But that is not how a business environment works.

Much of the anti-adversarial research has been on the potential for minute, largely undetectable alterations to images (researchers generally refer to these as "noise perturbations") that cause AI's machine learning (ML) algorithms to misidentify or misclassify the images. Adversarial tampering can be extremely subtle and hard to detect, even all the way down to pixel-level subliminals. If an attacker can introduce nearly invisible alterations to image, video, speech, or other data for the purpose of fooling AI-powered classification tools, it will be difficult to trust this otherwise sophisticated technology to do its job effectively. This is no idle threat. Eliciting false algorithmic inferences can cause an AI-based app to make incorrect decisions, such as when a self-driving vehicle misreads a traffic sign and then turns the wrong way or, in a worst-case scenario, crashes into a building, vehicle, or pedestrian.

At some point, we all have struggled in deploying our trained Machine Learning model and a lot of questions start popping up into our mind. What is the best way to deploy a ML model? How do I serve the model's predictions? Which server should I use? Should I use flask or django for creating REST API? Don't worry, I got you covered with all of it!!:) In this tutorial, we will learn how to train and deploy a machine learning model in production with more focus on deployment because this is where we all data scientists get stuck.

This video will get you up and running with your first ML app in just 7 lines of Python. The app will be able to recognize Iris flowers. This is the first in my new application-focused machine learning series. The goal is to avoid anything math-heavy and focus on building things with machine learning libraries. I recently created a Patreon page.