How To Become A Machine Learning Engineer?
How To Become A Machine Learning Engineer:
I’m sure you guys are curious about how you can become a machine learning engineer, right? After reading this blog on ‘How to become a Machine Learning Engineer’, you will end up with some detailed insights about this amazing career path that you can take.
These are the following concepts we will be looking at in this ‘How to become a Machine Learning Engineer’ blog:
- Introduction to Machine Learning
- Who is a Machine Learning Engineer
- What does a Machine Learning Engineer do?
- Machine Learning Engineer Roles and Responsibilities
- Machine Learning Engineer v/s Data Scientist
- Skills needed to become a Machine Learning Engineer
- Companies Hiring Machine Learning Engineers
- Future of Machine Learning
- Salary and Trends of a Machine Learning Engineer
Introduction To Machine Learning:
In my opinion machine Learning is one of the most recent and exciting technologies there is. You probably use it dozen of times a day without even knowing it.
You’re wondering how right?
There are 2 major things that come to my mind when I think of machine learning. YouTube recommendations and Facebook image recognition.
With YouTube, let’s say you’re watching Edureka’s newly launched python tutorial video. As soon as that’s done you will probably get the statistics for data science using python video as a recommendation.
So how does YouTube know what it should recommend to you? Well, it's really complex what YouTube does but it analyses everything from what you’ve watched previously to the keywords in the video that you watched. This is amazing, right?
Similarly, consider this — You and your friends went on a vacation. You clicked a lot of pictures and you want to upload them on Facebook. And you did. But now, wouldn’t it take so much time just to find your friend's faces and tag them in each and every picture. Well. Facebook is intelligent enough to actually tag people for you.
Machine learning has been so subtly integrated into our lives so much already that we do not even know its presence.
Machine Learning is basically a type of artificial intelligence itself.
As you can see from the above picture, Deep Learning and Machine learning branch out from Artificial intelligence. Machine learning is the subset of artificial intelligence and deep learning is the subset of machine learning, as simple as that.
So to sum it up machine learning provides computers with the ability to learn. The ability to learn without being explicitly programmed at all.
So, how does Machine Learning work?
It is pretty simple, first, we have some training data. It can be anything that acts as the data-set. Consider for example a set of images of cats and dogs where you want the machine to tell you which is a cat and which image is of a dog.
So once the data set is established we train the algorithm iteratively by providing it the input and teaching it to attain better accuracy.
Next up would be actually model the input data and by this step, the machine is trained.
We’ll provide new input data as well and let the algorithm check if its similar to our existing data and make predictions based on the same.
If predictions are correct then our model was successful in performing this task of comparison for us. If it failed, then the input doesn’t match the data-set enough, or it's something different or it might need more training.
Generalization:
But, what will happen when we do not provide proper input to the model?
Will it break? Will everything be fine?
By generalization, we make sure to produce a reasonable output even for the inputs the model has never seen before. So, we will not end up on an error case for most of the time but we will be providing a reasonable output.
Machine Learning Example:
Well who here doesn’t watch TV Shows right? I am sure Netflix just reminds us of a tub of popcorn and the weekend, but did you know Netflix has so many complex algorithms? Everything from suggestions to automated content checking. So here’s a case I have for you:
It all starts out with a film crew providing us with the dataset which gets turned into a movie or a TV Show. Let’s generalize and say content. The content is encoded into its respective format and the inspections which are needed for the same are done automatically (Yes, by machines and not by humans)
Here is where our machine learning model steps in and does an automatic screening of the content for us. If it passes then the content is said to be as per norms. If the model attains a fail state then intervention by manual quality control is done and lastly, it goes live on the Netflix site.
Here, I just simplified a very complex process for you but it is as straightforward and simple as this.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out who a Machine Learning Engineer actually is.
Who Is A Machine Learning Engineer?
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
So, let’s simplify that.
They are just enthusiastic computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take actions without being specifically directed to perform those tasks.
Now let’s talk about your goals for a second.
Whenever I give my sessions I always get a lot of questions afterward from developers who want to get started in machine learning but feel stuck. Usually, the only thing holding them back is a self-limiting belief.
- My computer is not good enough to build Machine Learning applications on.
- I’m just a student now
- I’m not a very good programmer
- I’m too busy with work
- I don’t have enough time
- I don’t have enough experience
There are just self-limiting reasons. Take on small things and do not be overwhelmed with it. Machine learning is really simple.
So for now, we’ve established the goals of a machine learning engineer and that of a learner as well.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out what a Machine Learning Engineer actually does.
What Does A Machine Learning Engineer Do?
Well, we already know that the data science team is always full of ideas. You have to make sure that no technology is limiting them. As good and customizable as the current ML frameworks are, sooner or later your teammates will have an intriguing use case that is not achievable with any of them. Well, not with standard APIs.
But when you dig into their internals, tweak them a little and mix in another library or two, you make it possible. You abuse the frameworks and use them to their full potential. That requires both extensive programming and machine learning knowledge, something that is quite unique to your role in the team.
And even when the framework provides all you need programming-wise, there still might be issues with the lack of computation power. Large neural networks take a large amount of time to train. This precious time could be reduced by an order of magnitude if you used GPU frameworks running on powerful machines. You are the one to scout the possibilities, see the pros and cons of various cloud options and choose the most suited one.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out how a Machine Learning Engineer differs from a Data Scientist.
Machine Learning Engineer v/s Data Scientist
When a company or organization has an issue or question they need to solve by gathering data, they hire a data scientist.
These professionals meet with the stakeholders and leaders of the study to learn the economic, efficient, or customer goals. Using this information, data scientists develop computer programs using Java and other computer languages. Software providing complex algorithms is able to help these business-savvy techs find patterns in large sets of data. This data is then used to learn more about viewership, customer engagement, sales, workflow, and other issues.
Job responsibilities of a data scientist include:
- Removing errors from data sets to avoid skewed results
- Looking for only the pertinent numbers
- Analyzing the data using statistical methods and writing a report the stakeholders can use to inform changes
- Creating graphs, charts, and other visual displays of the data
Well, Machine learning engineers are creators of the algorithms that allow a machine to find patterns in its own programming data, teaching it to understand commands and even think for itself. The artificial intelligence seen in automatic vacuums and self-driving cars are the ‘thought children’ of these engineers.
Highlights from a machine learning engineer’s job include:
- Researching new technologies and implementing them in machine learning programs
- Finding the best design and hardware to use when building the robot or computer
- Developing tangible prototypes to show stakeholders
- Putting the machines through various tests to ensure they function as planned.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the various roles and responsibilities of a Machine Learning Engineer.
Roles And Responsibilities Of A Machine Learning Engineer
We’ve already discussed the roles but here is everything generalized in just 3 ways.
The first and the most important role is to create artificial intelligence products for the team. Well this is achieved when we’re able to create machine learning models of our own, right?
What’s more important is that we need to build efficient applications. Efficiency plays a really big role here.
These are some of the responsibilities of a Machine Learning Engineer:
- We need to be able to study some prototypes and then transform them into applications.
- We have to be able to design and build our own machine learning systems.
- We have to be in a position where we put in some research to find the appropriate algorithms and tools necessary.
- And yes, we’ll be developing machine learning applications based on what’s required.
- Also what’s important is to select the right dataset and to find the correct data representation methods.
- We also need to run machine learning tests and experiments to keep improving on our implementation for the use case.
- And lastly, we need to train the systems for top-notch accuracy. But sometimes you will have to retrain them again based on the changes in the requirement.
Well, this sure does seem like a lot for now but it is really not as complex as it sounds. Once you start learning and begin cracking the basics, it’s very simple.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the skills needed to become a Machine Learning Engineer.
Skills Needed To Become A Machine Learning Engineer
Well, to begin with, it definitely has to be the fundamentals and programming skills.
You will require some basic knowledge on data structures such as stacks, queues, multi-dimensional arrays, trees, graphs, and some basic algorithms like searching, sorting, optimization, dynamic programming, etc.
You will need to know a little bit about memory, cache, bandwidth, deadlocks, and all these simple concepts.
1. Probability and statistics:
Well here as well guys, some basics on conditional probability, independence, and all that.
Machine learning will require a few techniques such as Bayes nets, hidden Markov models, and all these concepts.
And then statistics is really simple, right? Mean, median, variance, and all. Even distributions like normal, binomial, what else, yeah, poison, and even uniform distribution.
2. Data Modeling and Evaluation:
Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns such as correlations and clusters.
A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy measure like log-loss for classification, sum-of-squared errors for regression.
3. Applying Machine Learning Algorithms and Libraries:
We have a lot of packages, libraries, and APIs like Scikit learn, Theano and Tensorflow. But applying them effectively involves choosing a suitable model, a learning procedure to fit the data, and understanding hyper-parameters and all.
4. Software Engineering and system design:
At the end of the day, a Machine Learning engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services. You need to understand how these different pieces work together, communicate with them and build appropriate interfaces for your component that others will depend on.
Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data. Software engineering best practices (including requirements analysis, system design, testing, documentation are important for productivity, collaboration, quality and maintainability.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the companies that hire Machine Learning Engineers.
Companies Hiring Machine Learning Engineers
The number of opportunities is exponentially growing and this is amazing because you’ll be trending when you’re a machine learning engineer and you’ll be really well paid as well.
Everyone from Apple to Uber, Facebook to Salesforce — All these big players are on a constant hire spree and they pay big dollar as well.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the companies that hire Machine Learning Engineers.
Future Of Machine Learning
What is perhaps most compelling about Machine Learning is its seemingly limitless applicability.
There are already so many fields being impacted by Machine Learning, including education, finance, computer science and so much more that again, I couldn’t fit all these.
There are also virtually NO fields to which Machine Learning doesn’t apply. In some cases, Machine Learning techniques are in fact desperately needed. Healthcare is an obvious example, right?
The world is unquestionably changing in rapid and dramatic ways, do you agree?
And the demand for Machine Learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems right now.
If this is your future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed.
Machine Learning is one amazing thing in a bubble, period.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the salaries and trends of Machine Learning Engineers.
Salaries And Trends Of Machine Learning Engineers
As a fresher, there is a median salary of almost 13 Lakhs and a rising for a Machine Learning Engineer.
A Machine learning engineer in the USA gets an annual pay of about 140 thousand dollars. It’s about 50,000 pounds in the UK and about 13 Lakhs in India.
This definitely is a lot of money in my opinion and the opportunities are endless.
Look at this trend chart, it keeps going up and up. Your value as a machine learning engineer will keep on increasing and you can make a lot of money being a machine learning engineer as I’ve kept on mentioning.
Conclusion
I hope this ‘How to become a Machine Learning Engineer’ blog helps you in learning all the fundamentals needed to get started with taking up Machine Learning as a career path.
It would be nice to have the trendiest job of the job along with a high salary as well. If you wish to check out more articles on the market’s most trending technologies like Python, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series that will explain the various other aspects of Data Science.
2.Math And Statistics For Data Science
9.Introduction To Machine Learning
12.How To Create A Perfect Decision Tree?
13.Top 10 Myths Regarding Data Scientists Roles
15.Data Analyst vs Data Engineer vs Data Scientist
16.Types Of Artificial Intelligence
17.R vs Python
18.Artificial Intelligence vs Machine Learning vs Deep Learning
20.Data Analyst Interview Questions And Answers
21.Data Science And Machine Learning Tools For Non-Programmers
22.Top 10 Machine Learning Frameworks
23.Statistics for Machine Learning
25.Breadth-First Search Algorithm
26.Linear Discriminant Analysis in R
27.Prerequisites for Machine Learning
28.Interactive WebApps using R Shiny
29.Top 10 Books for Machine Learning
Originally published at https://www.edureka.co on December 26, 2018.
