AI is the future. One does not need to be an oracle like Nostradamus to predict that. AI has not only influenced the technology sector but the corporate world as well. Professionals willing to switch over to the AI and its sibling sectors need to have a number of specific skills.
The following are some of them that are going to rule 2020. But before going into the exact skillset, one also needs to have some mathematical background. Let’s start with it then.
An AI professional needs to have mastery in several applied mathematical streams. So it’s always a good idea to ramp up your core mathematical skills if needed. The following are some of the mathematical disciplines in which you need to have a firm grip.
As an AI professional you need to be well-versed with programming; you at least need to have a minimum amount of familiarities with some languages like Python, R, Scala, Matlab, Java, and C++. Each of the languages has its own advantages and is employed in very specific domains.
R supports many advanced features. Programmers can even manipulate R objects using other languages like C, C++, or Python. One can even link and call C, C++, or Fortran code at runtime.
Datasets are the heart of any kind of data analysis and predictive analysis. And good dataset needs a huge amount of computational resources. So much in fact that a single machine in many cases can’t handle the task. That’s where distributed computing and Big Data analysis comes into play. Many of the AI and ML tasks these days involve using the aforementioned frameworks like Hadoop, Spark, Flink, and Apache Storm. AI engineers looking to enhance their skillset are always encouraged to upskill their big data analysis skills.
Shell scripting (bash scripting, as many likes to call it) is another integral part of any AI or pattern recognition task. A shell-script is a program designed to be run by the Unix shell. Tons of other shells are out there such as KornShell (ksh), Almquist shell (ash), Powershell (msh), Z shell (zsh), Tenex C Shell (tcsh), Perl-like shell (psh), etc. But at the end of the day, bash still remains head and shoulder ahead of the rest of its competitors.
As Linux-based machines are used in any kind of professional or corporate setup, good knowledge of shell scripting is a must-have. Familiarity with commands like awk, grep, ssh, etc. goes a long in securing a good position.
If you’re into machine learning or computer vision, you would be having a hard time moving forward without the proper knowledge of signal processing.
Signal processing is a field that focuses on analyzing, modifying and synthesizing any kind of signal (e.g. audio, radio, or even image). Techniques like wavelets and contourlets are pretty common for feature extraction in machine learning. Fourier analysis and convolution are two of the most common and heavily used techniques in Computer Vision.
As you keep upskilling yourself and continue gathering new knowledge, a new window of opportunity keeps opening. The following are some of the most popular roles.
1. Data scientist: The primary aim of the data scientists is to extract useful and valuable information from large scale data using various statistical and machine learning tools. They need to be proficient at using data analysis tools like Spark, Pig, Hive, query languages like SQL and scripting and programming languages like Python, Scala, and Perl.
In a business environment, the data scientists are responsible for extracting valuable information for predicting customer behavior and identifying revenue opportunities. But that is just one aspect of the responsibility of the data scientists. A data scientist’s responsibility often includes setting best practices for data mining, data interpretation, and usage of analysis tools.
2. Machine Learning engineer: ML engineers are responsible mostly for applying predictive models to large datasets. They need to be experienced about the software development pipeline and well-versed at using the ML and statistical tools.
A typical machine learning engineer is responsible for many different tasks. Some of these responsibilities include performing various statistical analyses on the data, exploration, and visualization of data to understand the insights, analyze and understand the errors and bias in the used models, management of the data pipeline, data engineering (making sure that good data flow is always maintained between the database and backend) among others.
3. Business Intelligence Developer: Business intelligence developers’ job is much more business-centric and their primary aim is to extract business and market trend information from the data. They are thus responsible for increasing the profitability and efficiency of an organization by modeling and maintaining complex data for cloud platforms.
Simply put, a business intelligence developer is both an engineer and a developer. They are often in charge of the development, deployment, and maintenance of the business intelligence interfaces. Their responsibility set includes data visualization, ad hoc reporting, setting requirements for intelligence tools, participation in the warehouse designing, and many others. The professionals often use a host of query modeling tools to extract insights from the data.
AI is a huge subject that needs a fair amount of time to master. It is a conglomeration of several related fields including Machine Learning, Computer Vision, Natural Language Processing, Robotics, and many others. At the end of the day, you need to be patient. Patience and perseverance are the keys to making yourself successful.