It is no secret that Artificial Intelligence or AI is an emerging technological trend. According to a Gartner Report, Artificial Intelligence is going to create 2.3 million Jobs by 2020, replacing the 1.8 million it will eliminate. The Job Growth has already flooded the industry, as the demand for someone with AI skills has already doubled over the past few years.
In this article we will be briefly looking in to how to become an AI Engineer.
I believe, most of you guys have came across this diagram where the relationship between AI, ML & DL is clearly mentioned (well, Neural Networks too! ). Before diving into our main topic of how to become an AI Engineer, let’s quickly have a look at some info.
What is Artificial Intelligence?
AI is classified into two parts, general AI and Narrow AI. General AI refers to making machines intelligent in a wide array of activities that involve thinking and reasoning. Narrow AI, on the other hand, involves the use of artificial intelligence for a very specific task. For instance, general AI would mean an algorithm that is capable of playing all kinds of board game while narrow AI will limit the range of machine capabilities to a specific game like chess or scrabble. Currently, only narrow AI is within the reach of developers and researchers. General AI is just a dream of researchers and perception among the masses that will take a lot of time for the human race to achieve.
What is Machine Learning?
Machine learning is the ability of a computer system to learn from the environment and improve itself from experience without the need for any explicit programming. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously un-analyzed data using the information gathered. Machine learning can be performed using multiple approaches.
Now most of you guys might be wondering, what does Data Science mean.
What is Data Science?
Data science is the extraction of relevant insights from data. It uses various techniques from many fields like mathematics, machine learning, computer programming, statistical modeling, data engineering and visualization, pattern recognition and learning, uncertainty modeling, data warehousing, and cloud computing. Data Science does not necessarily involve big data, but the fact that data is scaling up makes big data an important aspect of data science.
Let’s dive in to our main topic, How to become an AI Engineer!
Now, if we start from the Basics, one needs to earn a Bachelor’s Degree first. It can be from either of the following areas or Subjects:
- Computer Science
- Information Technology
- Statistics, etc
But this is not the case always. Even though a degree in these fields are appreciated, one without these degrees can also excel in AI or closely related fields. Because what it matters is your expertise in handling stuffs and not what you’ve learned. You can also go with online courses from Udacity, Coursera, Stanford Online, etc considering the fact that, Higher studies in these areas might be costlier (if you haven’t done a degree on these topics).
You can also refer to an article by Daniel Bourke on the topic “My Self-Created Artificial Intelligence Masters Degree”. He had his higher studies on a different topic but ended up as an AI Engineer (ML actually) at one of the best startups from Australia, Max Kelsen. The interesting part was that, he was contacted for the job position directly from the company via LinkedIn by looking at his projects which he done as a part of his self study.
The next step is to fine-tune your Technical Skills. An important thing to note here is that in order to become an AI Engineer one not only needs to be good at programming but also good at Software Development techniques and practices. They need to be knowledgeable both theoretically and practically about the following topics:
- Software Development Life Cycle
- Modularity, OOPS, Classes
- Design Patterns
- Statistics and Mathematics
- Machine Learning
- Deep Learning & Neural Networks
Apart from technical Skills there are also must have Business Skills one must possess while planning on How to Become a Successful Artificial Intelligence Engineer. Some of these skills include:
- Analytic Problem-Solving
- Effective Communication
- Creative Thinking
- Industry Knowledge
Now, these skills can either be achieved through practice or by opting for a Master’s Degree. As AI is a newly emerging topic in today’s world, a lot of recent discoveries and research is going on which can be useful for your thesis. Going for a Master’s Degree in Data Science, Machine Learning or Computer Science is advised. Another Option is to go for Industry Certifications for Machine Learning, Deep Learning or Data Science. This will add a lot of value to your resume and will help you get in-depth knowledge of topics both theoretically and practically. Which will, in turn, help you get an edge over other competitors.
Technical Skills Required!
- Programming Languages (R/Java/Python/C++)
- Linear Algebra/Calculus/Statistics
- Applied Math and Algorithms
- Language, Audio and Video Processing
- Neural Network Architectures
These are few of the technical skills required to become an AI Engineer apart from the non technical, which we discussed earlier.
The Main Aspect
The main point which should be discussed apart from the requirements is EXPERIENCE.
Experience is the best teacher
Whenever you get time, try working on some projects associated with AI or ML. You can redo the projects which is available online. But it is better to work on your own project so that you can showcase it anywhere. Moreover it will show your thinking level/creativity to the recruiter which will be a bonus for you. Apart from that, you can also look into new research papers published by famous AI researchers.
Now you might be wondering, what kind of roles are available for an AI Engineer. Depending upon their Level of Expertise AI Engineers in general:
- Study and transform Data Science Prototypes
- Research and Implement Appropriate ML algorithms and AI tools
- Develop Machine Learning Applications according to Requirements
- Working with Electric Engineers and Robotics Team
- Select appropriate Datasets and Data Representation Methods
- Run Machine Learning / AI Tests and Experiments
- Train and retrain systems When Necessary
- Keep Abreast of Developments in the Field
- Get a degree
- Improve your tech skills (and non tech skills)
- Work on your own projects
- Keep up with the latest news
- Update your social profile (LinkedIn & Github)
- Learn more and look for job roles.
- continue steps 2 to 7
So these are few of the steps that need to be taken to grab a role as an AI Engineer. Hope this works out very well :)
(the article will be updated regularly)