Pranjal Saxena
Python programming got the hype and the attention because of the popularity of data science. As of now, in 2021, we have two famous programming languages Python and R, for data science and analytics. But, if we talked about back in 2016, we were having a single famous programming language for data science modeling, Python.
Indeed, Python is also used for web development, and its Django & Flask framework is so much used in many industries. But, we have many other programming languages like Java, Javascript with its popular library React that can do that same web development efficiently. And, today we also have flutter that is getting much attention for web development tasks.
Python programming was never aimed for web development. The primary use of Python is for statistical work and data science. That’s why today, we have most of the data scientists who are using Python for their work. Even employees in Google use Python for their machine learning work. Yes, they use C++ also, but most of the time, Python is being utilized.
Today in 2021, Big tech industries like Facebook, Snapchat, Microsoft, and Google are now investing a reasonable amount of time building No-code automation systems. Some of the latest examples of building no-code machine learning applications are Google AutoML, MakeML, Fritz AI, Spark AR, and Lens Studio. Here, AutoML is from Google, Spark AR is from Facebook, and Lens Studio is from the Snapchat community.
The focus is now to make an environment where non-technical people can also build, train and utilize the complete machine learning models.
People will only need the business logic and the data to make their own model. They don’t need any prior programming experience in python to get started.
Python is not the programmers choice
Python is easy and user-friendly, but it is slower. And, we literally can’t do anything to make it as fast as C. And, the reason is that Python is an interpreted language, while C is a compiled language.
Interpreted code is always slower than direct machine code because it takes many more instructions to implement an interpreted instruction than to implement an actual machine instruction.
You might have taken part in many coding competitions and successfully built the logic part. Still, when you finally submit the code, you have exceeded the time limit, which is very frustrating.
So, it is clear that a good programmer who wants to be an excellent competitive coder will never prefer Python over C or C++.
The adaptation of the codeless environment
The future responsibility of the data scientists will be to understand the business and the data well. They will not worry about data cleaning, feature selection, model selection, and data modeling. The complete data science pipeline is going to be automated. Which algorithm is best, and which features can be the best fit for your algorithm? All of this will be the work of automated pipelines.
The work of a data scientist is going to understand the pipeline and provide better data based on a better understanding of the business needs and observe the output provided by the machine. The time we spent manually writing lines of python code and running them will no longer exist in upcoming years.
Let’s understand one scenario. When we talk about object detection, which is a part of deep learning. There to train a simple face mask detection, we have to follow the following steps:-
Gather face mask data
Labeling the image data
Applying image augmentation process
Configuring object detection model based on images and your machine configuration
Training model
Inferencing
Until now, we have to follow all these processes to build your first face mask detection model.
Today we have a tool provided by many companies like Fritz AI, Roboflow that can do the same task with the No-Code environment. The only thing that is required is the images. Just upload the sample images of the face mask and do the image label and get your custom trained model ready.
No comments:
Post a Comment