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The reason for this error touches upon the difference between object equality and identityin Python.
This is a large and separate topic but basically, literalsin Python include strings, integers, gloats, lists, tuples etc. is and is not are not supposed to be used with literals – they are to compare whether different objects contain the same values.
That’s why, you can execute the following result to obtain True.
a = 50 b = 50 a is b
Or this one to obtain True again.
c = 10 d = 12 c is not d
Should you compare literals directly, you can use arithmetic operators (such as == for equality and != for inequality).
Have you ever been in a situation where you spend a lot of your time and energy learning something, only to realize that the skills you have gained don’t match or live up to the requirements listed by the employer? If this happens to you, you’ll have to begin learning new technologies and skills to get to the interview, which is one of the most painful and tedious tasks in the job searching process. Unfortunately, many people go through this tiresome loop.
With the recent buzz around machine learning, many courses have come into existence offering a broad curriculum. This leaves job seekers confused about what they really need to learn to become machine learning engineers. So, today we will try to find a solution and put you one step ahead of your rival ML job seekers.
After our previous analysis of Data scientist job descriptions, we have received numerous requests from people asking about a similar analysis on machine learning. That is why we conducted this analysis in an identical manner – by leveraging job boards data.
We analyzed more than 500 recent machine learning engineer job postings, and this analysis was mainly focused on the USA.
Now, let’s set our expectations straight from the start. We will try to answer the most common questions every machine learning engineer enthusiast needs to know.
What is the most sought-after educational background to become an ML engineer?
Which are the most important skills needed for a machine learning engineer?
What is the experience required by employers?
Which firms are offering more opportunities in the field?
Which are the locations that offer most opportunities?
You can find the answers to all these questions in the video below or just scroll down to keep reading.
Machine Learning Engineer Degree
What is the most sought-after educational background? Well, this is one of the most common questions among job seekers because there is a lot of confusion in the job market. Nobody has a clear idea about the ideal educational background required to become a machine learning engineer. So, let’s see what the data tells us.
According to our research:
Most of the machine learning job descriptions require a Master’s degree
There are almost as many listings asking for a Ph.D. as the ones looking for a Master’s degree.
Bachelor’s is the last on the list, but still has a very good number of openings.
In addition, what is worth noting is that most of the job ads are flexible in terms of the type of degree. For example, very often we can see Bachelor’s as required and Master’s/Ph.D. as preferred.
In terms of degree specialization, it appears that Computer Science, with Statistics and Mathematics as the not-so-close second and third place are the three specializations employers are looking for the most. Electrical engineering and physics are the other two most frequently desired degrees.
Now that we’ve covered the degrees and fields of study required to become a machine learning engineer, let’s take a look at the companies that are actively recruiting. Who are they?
Top 10 Companies Offering Machine Learning Engineer Jobs
Here are the top 10 companies in our dataset with the most machine learning engineer job openings.
As you can see, Apple undisputedly tops the list with almost 60 available offers, followed by Twitter, Amazon, Facebook, Snapchat, and TikTok. These are some of the most exciting firms in tech field, which extensively rely on machine learning to run their platforms. So, no surprise here.
Regarding company size, it is obvious that the majority of the offers are coming from big firms with more than 10,000 employees.
However, there is a considerable number of postings by both mid-range firms (1000 to 10,000 employees) and smaller firms (with less than 500 employees).
Next in our study, we analyzed the industries with the highest concentration of machine learning engineer job offers.
Machine Learning Engineer Jobs by Industry
Unsurprisingly, there are more postings in the IT and Retail/Wholesale industries at the moment. But these are far from your only options, as there’s a substantial number of offers in the Consulting, Education, and Finance industries, as well.
This gives us an idea about the companies hiring ML engineers. However, to be thorough, we need to take a look at geography, too. Here, we split the data based on the state and city where the offers came from.
Machine Learning Engineer Jobs by State and City
In terms of states, the majority of machine learning offers (almost 50% of our data) are from the state of California.
After California, there seem to be a good number of opportunities in New York, Washington, and Massachusetts.
If we consider cities where these jobs were available, we can see three important findings:
There seem to be more offers in San Francisco and Santa Clara Valey.
There are a considerably good number of offers in New York City and Mountain View.
16 postings, didn’t mention a particular city.
Now that we’ve outlined the landscape for ML engineer job postings, it’s time to pay attention to one of the crucial factors to land this lucrative job – working experience.
Machine Learning Engineer Work Experience
According to the data, there are generally more offers for people with at least 2 years of relevant experience. For comparison, there seem to be more offers in the range of 1–5 years of experience and fewer opportunities for 5+ years-of-experience candidates and freshers at the moment. And that’s certainly good news for those of you who considered many years on the job as a hard prerequisite for this position. But let’s elaborate on the experience factor a bit more – this time in relation to degrees.
On average, the experience required with a Bachelor’s degree is 4 years, while for Master’s degree, it’s roughly one year less – 3 years.
On the other hand, if you hold a Ph.D., then you’ll need 2 years of experience. However, there is a little catch here, as most of the recruiters haven’t mentioned the required experience for Ph.D. holders specifically. They mentioned it in a generalized way like: Required 2+ years of experience with education in MS or Ph.D. So, overall, if you have a Bachelor’s degree, you stand a pretty good chance with ML employers, provided that you have worked for a few years and you have acquired some valuable experience.
Moving forward, it’s time to dissect the most practical aspect of landing a machine learning engineer job – the required skillset.
Machine Learning Engineer Skills
In terms of general skills for the machine learning engineer position, we discovered the following:
To be a machine learning engineer, obviously, machine learning is the primary skill required. In addition, most of the jobs have mentioned deep learning and its fields like Natural Language Processing (NLP) and computer vision as a requirement. But that’s not all! There have been plenty of mentions of data analytics, statistical modeling and data visualization, as well. Big Data, version control tools like Git and deployment tools like Docker have been requested in quite a few descriptions, too.
How about we dive deep into each type of skills required?
Starting with programming languages.
Programming Languages
No surprise here – Python is leading the chart with a significant number. What’s worth noting is that C++ and Java are mentioned more frequently than R, and SQL is mentioned in quite a few jobs, as well.
Deep Learning Frameworks
Continuing with the most sought-after skills, we can’t skip deep learning frameworks:
Tensorflow is leading our chart with Pytorch as a close second. Then the top two are followed by Caffe and Keras. Tensorflow and Pytorch definitely look like the two most popular frameworks at the moment.
Machine Learning Libraries
Being able to work with different packages that are suitable for the task at hand is an essential skill for a machine learning engineer. So, let’s examine the most frequently requested Python machine learning packages.
Scikit-learn, where most of the machine learning algorithms and all other important functions are available, is listed as the top package, followed by pandas – one of the important libraries for all data manipulation activities. In third place, we have NumPy and SciPy where, basically, all the important math functions reside.
Big Data Technologies
Spark tops the list with a significant lead over Hadoop, while Hive and Kafka have been mentioned in fewer job postings.
Cloud Technologies
In terms of cloud technologies, AWS is the most in-demand cloud technology at the moment with Google’s GCP and Microsoft’s Azure following in its footprints.
Data Visualization
Are data visualization skills important for an ML Engineer?
According to the data – not really. In fact, there are very few mentions of Data visualization tools for machine learning jobs. Tableau was mentioned just 15 times, whereas Power BI only 2 times, which makes it clear that the default packages in Python should suffice for aspiring ML engineers when it comes to data visualization.
Communication Skills
Last on the list of ML Engineer job requirements come communication skills.
This one is slightly different than all the other skills we have seen until now. Apart from regular technical skills, communication skills appear to be equally important. Let’s see how many jobs have mentioned strong communication skills explicitly.
220 jobs have a mention of communication skills as a definite requirement for the desired candidate.
How to Become a Machine Learning Engineer: Next Steps
Now, you’ve got a good idea about the skills and education required to land a machine learning engineer job. One last piece of advice from our side: knowing technology is one thing and applying it is a whole different thing. So, to be successful in the ML field, learn the most mentioned important skills first. Then try to solve a real-world problem by combining all your skills to get a more real-life-like experience.
Remember machine learning is a very dynamic field, so be ready to upgrade yourself every day.
That said, if you want to sharpen your predictive modeling skills, check out our Machine Learning in Python course.
