We’ll start with an introduction to Machine learning, responsibilities of a Machine learning engineer, Machine learning engineer salary ,Skills of a Machine learning engineer.
What is Machine learning.
Machine learning is the study of algorithms and data models that computer systems utilize to perform specific tasks without using instructions relying on previous patterns.
If we have to build a robot with human like intelligence, it should be able to learn for itself with previous experiences.
This is where the role of Machine learning engineer comes in, so who is a Machine learning engineer.
Who is a Machine learning engineer.
A Machine learning engineer is someone who performs the following tasks develops and implements production ready algorithms and methods.
This is a very difficult challenge, because we talked about production ready, that’s a key word in here.
This is something that’s going to be pushed out to end users to be able to use, whether it’s a bank doing their finances, or your new cell phone, where it’s doing some kind of image adjustment.
And adding in virtual images or stamps on to the picture, Cody bells, Machine learning solutions with data scientists and engineers.
So you can be working as a team, the data scientists are going to be developing this and then you’re going to be implementing it provides technical guidance to product teams on the choice of Machine learning approaches appropriate for the task,
Machine learning engineers will design implement,and ship new algorithms provides architectural guidance on changing prototypes to high performance production models, and provides feedback on tools and new features required to send it back to the development teams.
So let’s dig a little deeper into the responsibilities of a Machine learning engineer.
Responsibilities of a Machine learning engineer
- Study and transform data science prototypes
- Design Machine learning systems
- Implement Machine learning algorithms
- Develop Machine learning applications
- Select appropriate data sets
if you’ve ever seen the evolution of any of the current AI’s out there, from Google Voice to all the different picture programs and everything, there’s a continual retraining of these systems going on, and they continually improve.
So they’re not just rebuilding the whole thing from the bottom up. Sometimes they’re just retraining them to get a better response.
Salary Of a Machine Learning Engineeer
What’s most exciting about this, and what most people are in here for is going to be salary trends of a Machine learning engineer.
The salary trends of a Machine learning engineer, we look at the average base pay, this is for the US, is $121,707.
And once you have that high end expertise, now you’re looking at 162 K, so it’s got a really nice salary to it.
Once you get your foot in the door, your salary just shoots up with you into the upper end Machine learning engineer jobs
And we’re going to this is just a pull off of India’s best jobs in the US and comparing average base salary to percentage growth in job postings 2015 to 2021.
And certainly the Machine learning engineer is just shot up because it’s one of the primary growing fields and it’s not stopping.
Again, that’s not going to stop, that’s just going to keep rising because that is where a lot of the actual work is right now, stuff is being pushed out.
They’ve got it working. Now they need a Machine learning engineer to go in there and do all that retraining and fine tuning.
And that’s a huge job. And it’s a growing industry, it’s just going to continue to take off.
Top companies hiring for Machine learning engineers
These are all the big tech companies. I So across the board, companies are hiring Machine learning engineers, now we look at the skills of a Machine learning engineer.
Skills of a Machine learning engineer
As we start looking forward Machine learning engineer should have the following skills,
1. Programming skill
Pick a program, Master, if you don’t already, you should have at least two solid programs under your belt in Machine learning.
The top program right now is Pthon with a close following of Java, there’s also scale is very big. While we’re talking about a lot of this, in any of these programs,
And again, right now Python just topped the chart for data science Java’s way up there, see play, any of these are really hardcore.
If you have a master in any of these great, you should know something and be able to just glance into Python and probably glance a little bit into ours important too, because those are the current data science programs being used. So you can actually look at this and be able to know what to pull and what to push,
You must have basic understanding of math. And this is becoming less and less important. But it’s still you need to have some fundamental knowledge of your mathematics.
Applied mathematics understanding of matrix is that’s that’s probably the thing that trips a lot of people up in mathematics, even people who are pretty solid in mathematics start looking at matrixes.
And they just start looking at columns and rows and eyes go dilated, so understanding your matrix isn’t being able to manipulate them derivatives and integrals is necessary.
So you have an understanding of your basic derivative and what that means statistical concepts like mean standard deviation is required.
You really need to know those terminologies and what the standard deviation means what square means any of those concepts, median versus mode versus any of those things.
Those are so important when you’re doing applied mathematics. And you’re looking at data science, and in this case, the engineering side of it.
3. Data modeling and evaluation
Data modeling is a process of finding the hidden structure of a given data set and finding patterns such as correlations and clusters.
A key part of the process is evaluating how good a given model is and choosing an appropriate accuracy error measure and an evaluation strategy.
Now, one of the things I find interesting in the field is that people start looking at the data modeling and you can write a script that goes through all the different models.
And then you can Take some of those models and change all the different underlying dimensions for that model that takes up a lot of processing.
4. Machine learning algorithms and libraries
You need a firm understanding of algorithm concepts like linear regression support vector Machine.
And understanding how these algorithms work. And so this is kind of a rehash of some of the other things we saw earlier. And this is a vocabulary, you need to know this vocabulary,
you need to know when you’re looking at a linear regression model versus just a regression model.