Is Machine Learning Saturated?

If you’re thinking of getting into the machine learning field then you might be wondering whether or not it is becoming oversaturated.

This post will show you whether or not it is, how you can enter the field and some things to consider.

So, is machine learning oversaturated? Currently, the demand for ML related positions is growing at a rate faster than the growth in the supply of qualified ML job seekers. This has resulted in a shortage of people with machine learning skills in the job market.

In some areas, there is actually an oversupply of machine learning job seekers and the type of machine learning job will have an impact on its saturation.

There are not enough experienced ML researchers or workers

According to Indeed, the demand for AI-related roles has more than doubled over the past three years but the supply has plateaued.

LinkedIn also reported a shortage of 151,717 people that have skills in data science.

There is not currently enough highly experienced people in machine learning that are able to advance the field. Currently, only a small proportion of universities and a few very large businesses are advancing the field.

If you are able to show that you have a lot of relevant experience and a relevant masters degree or Phd then you should be in a fantastic position to get a job in machine learning. If you have a Phd in a related field then you should also not have too much difficulty in finding a research-focused position if that is what you are after.

If you have a bachelors degree and you can show that you have a lot of relevant experience then you should also be able to find an entry-level position, in the field, as well.

Relevant degrees for machine learning would include those such as computer science or statistics.

Machine learning is widely applicable

Also, businesses will always be looking for ways to make use of their data and the amount of data becoming available to us is growing.

Machine learning is applicable to a number of different fields such as:

  • Financial trading (predicting the future prices of stocks)
  • Healthcare (detecting diseases, improving health records, predicting outbreaks, etc)
  • Education (optimizing lessons based on student retention, digitizing formulas from photos, etc)
  • Marketing (predicting consumer behavior)
  • Building recommendation systems (ie: recommending shows on Netflix or products on Amazon)
  • Natural language processing (making sense of human language)
  • Self-driving cars

Practically any industry that you can think of will be able to benefit from machine learning and AI in some way.

This means that the demand for people skilled in machine learning should be high since it can provide value to many different types of businesses or institutions. As the amount of data and understanding of machine learning in these industries continues to increase it should also mean that the demand for machine learning workers should also continue to rise.

How you can enter the field of machine learning

Ideally, you would get a bachelors and a masters in something such as statistics, computer science or mathematics (fill those electives with stats and CS classes).

You would work on learning machine learning whilst doing your bachelors and masters and you would do internships in machine learning or at least software engineering type roles.

You would also have a number of projects that you can use to show your ability to do machine learning. You can gather projects by coming up with them yourself, doing competitions, going to hackathons or contributing to open source projects.

If you can do all of those things above then you would be in a fantastic position to get a highly paid job in machine learning and you would be in very high demand.

It is the lack of candidates with a relevant masters degree, internships and work experience that is causing most companies to be struggling with finding people to employ.

If you’re looking to get a machine learning job and you are not able to get a relevant degree or any internships then the best option for you would be to do as many machine learning projects as possible. You can do so by contributing to open source projects, going to hackathons, doing ML competitions, or by making your own projects. I have written more about how to get a job in ML, if you haven’t got a lot of experience, here.

There are a lot of people entering the field at the entry level

As a result of MOOCs, Nano degrees and machine learning and data science bootcamps that have been introduced, in recent years, there has a been a large amount of people entering the field at the entry level.

If you are looking to get a job in machine learning at the entry level then you will be competing with a lot of other applicants.

However, many of them will not have relevant experience, a bachelors or masters in a related field or the ability to show that they have done a lot of machine learning projects. It is this lack of the required skills that are causing businesses to struggle to find qualified applicants. If you can show that you have requirements then you shouldn’t have too much trouble separating yourself from everyone else.

There are still lots of opportunities from a funding perspective

Currently there are many ML and AI related startups that are being made and there is a lot of venture capital funding going to these startups.

Ai-related funding increased by 72% in 2018 compared to 2017 and 466 startups were funded out of 533 in 2017 according to Cbinsights.

In recent years, there has been a massive surge in data available to us and computers have only recently become powerful enough to make use of deep learning algorithms. This has resulted in many opportunities arising to make predictions based on the data available to us. This means that, at the moment, there is still a lot of room to build your own AI startup. If your goal is to make an AI startup then now would be the best time to do it.

You can watch the video below to see how you might go about making your own AI startup.