Python has been the leader in Machine Learning for several years. Will it last ? Let’s analyze the breakthrough of the Javascript language.
Since March 2018, it is possible to use the Machine Learning library Tensorflow in Javascript.
Python being an uncontested pillar in the field… is it possible that it loses its place of leader in AI?
In this article, we propose you to answer this question, but especially to understand if you should add the Javascript string to your bow !
Will Python keep its number 1 position?
Machine Learning and Python
Let’s start with some context, what is Machine Learning ?
It consists in teaching a machine to solve a task.
To do this, you first need to have data on which the Machine can train.
This training is not done alone.
It requires a Python developer (or R in some cases) who will take the time to train the Machine Learning model.
He will test it, change the settings… probably several times.
To finally get a functional Machine Learning model that can be reused in other algorithms.
Javascript
Let’s contextualize here too.
Javascript is a web language for creating applications.
It allows to fluidify the interface between the user and the Machine.
The main idea behind Javascript is to allow everyone to use the features of a web application.
Javascript makes life easier for users !
And Javascript for Machine Learning ?
Machine Learning leads to AI algorithm.
Javascript leads to web app.
The two used together enables to build applications for AI training and creation.
This concept has the potential to make life easier for thousands of developers!
But between you and me… should we develop AI creation applications that only developers will use; or on the opposite, develop AI augmented applications that anyone would use?
At Inside Machine Learning, we don’t look at Javascript as another language to train AI but as a language that FINALLY ease the use of AI !
In this context, Python would keep its usefulness.
The interfaces of Python notebooks are clear and already adopted by most Data Scientists. Why change it ?
Once the models are trained in Python, we can easily make them accessible in Javascript.
Yes, Python has its strengths but when it comes to production, it is not ideal… we recall the study conducted by VentureBeat, according to which 87% of Data Science projects are never produced.
This is where Machine Learning in Javascript comes in !
Therefore Python and Javascript, instead of being competitors, could be complementary, each addressing the other one’s flaws !
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Now we can get back to what I was talking about earlier.
So… who is Javascript Machine Learning for?
Below you’ll find our advices according to the branch which corresponds you :
Novices
For novices who want to learn Machine Learning, the most efficient solution is Python.
Indeed, Jupyter notebooks are ideal to learn.
They offer a legible approach to Machine Learning, easy debugging of its code and, on top of that, they’re already a reference in the field.
Machine Learning Engineers
For Machine Learning Engineers whose goal is no longer to learn but to build a Machine Learning algorithm, we also recommend the Python language.
Indeed, ML Engineers have to focus on the structure of their code, often changing the parameters.
In fact, if Machine Learning was a car, ML Engineer would be the guy who builds the engine.
To ensure that, they need a clean garage to be able to work well. And this is what Python offers for Machine Learning.
MLOps
MLOps has a very different goal.
Indeed, there goal is to create an easy-to-use product.
If ML Engineers build car engines, MLOps assemble the parts together to create a reliable and easily usable car.
Javascript is therefore a totally legitimate option.
With it, you can build applications and deliver them quickly on the cloud.
Thus, we advise MLOps developers to keep a close eye on the Javascript option and… for the most Pythonic of them, to at least take a look at it ! 😉
Anyhow, if Python MLOps have new competitors with Javascript, others should beware of Auto ML.
With this technique it is possible to build a Machine Learning algorithm in only 3 lines of code ! More info in this article 🔥
sources :
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- Plan your training
- Structure your projects
- Develop your Artificial Intelligence algorithms
I have based this program on scientific facts, on approaches proven by researchers, but also on my own techniques, which I have devised as I have gained experience in the field of Deep Learning.
To access it, click here :
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