Introduction
Artificial intelligence (AI) is transforming the landscape with each innovation making the headlines every year. AI has the potential to make our lives simpler by minimizing human errors, automate laborious tasks, and an overall transformation in the way we work and live today.
Other technologies have observed a steady growth in the jobs created in a specific domain. On the contrary, AI has leaped into different verticals which the majority of the population never envisioned. According to a report by Gartner, industries are expected to witness an astounding growth rate of $3.9 trillion by 2022 due to the inclusion of AI in their business
The AI industry is experiencing traction that has been more than the anticipated margins by experts. The amount of viable career opportunities that this industry is generating justifies the hype of being a sound career choice. Unlike, some of the technologies that have achieved tremendous growth which eventually became an afterthought, is not the case for the AI industry.
Moreover, the staggering growth of AI has increased the need for trained professionals for filling these specified roles. For the veterans in the software industry and aspirants alike, it is the most opportune moment to step foot into the world of AI and relish upon a long-lasting career boost.
Getting started in the AI industry
1. Important Artificial intelligence and Machine learning concepts
For those who are new to the field of AI, it is extremely important to understand both the concepts. Although the terms AI and machine learning are used synonymously, it is varying in the definition. Artificial intelligence belongs to the field of computer science that is capable of making a computer, robots or a software program operate like a human mind.
The outcome of employing artificial intelligence are intelligent systems that are capable of performing complex tasks faster and more efficiently. AI innovations have made a mark across industries worldwide.
Machine learning is a subfield of artificial intelligence. Machine learning algorithms provide the means to augment human capabilities in a computer program. Ideally, machine learning is the process by which an artificial intelligence model is about to learn and train to produce a targeted result.
The implementations of AI have been widespread across various industries ranging from navigation to smart driving assistance, personal assistants, intelligent lighting, analyzing healthcare data to aiding doctors for diagnosis, automation in manufacturing industries, freight, and cargo, finance among several others.
2. Educational requisites
For a Fresher
A Bachelor’s degree is a must in the following subjects-
- Computer Science and Engineering
- Information Technology
- Mathematics
- Statistics
Apart from a degree, an individual must have proficiency in analytical skills, problem-solving skills, coding, creative thinking, and communication as well as industry knowledge.
It is crucial to keep adding to the skillset as artificial intelligence career is competitive and only the best of the professionals get hired. However, it is advisable to continue taking up online training programs from accredited institutions. A bachelor’s degree will gain an entry into the industry with junior positions.
For Leadership roles
People looking to find leadership roles must have a Master’s degree. Higher education offers more advanced computer science expertise and know-how and improved analytical and programming skills. In recent times, artificial intelligence specializations are being offered with a Master’s degree and one must opt for it to have better hands-on experience with the latest offerings from the industry.
The individuals who are aiming for the top of the hierarchy ladder may look to acquire research experience and a Ph.D. The robust nature of research entails solving real-world problems and proposing own research models. Researcher roles are often hired for top positions in an AI-centric organization with a focus on supervision and executive related roles.
The professionals who are seeking to switch their careers to AI, it is important to fine-tune their skills and upgrade on the latest skills. Opting for skill-enhancing capstone based courses may help to improve on the theoretical and practical knowledge. Acquiring the right certification will prove handy in getting hired for specialized roles.
3. Technical Skills
Programming Languages
To have a successful career as an artificial intelligence engineer, the need for attaining proficiency in programming languages such as R, Python, Java, and C++ are the primary requisites.
One must be aware that a successful career in the technology industry requires to have expertise in a minimum of two or three programming languages. Having one skill set is not considered to be of value addition for organizations, thus diminishes the opportunity of career growth.
Neural Network
A thorough understanding of the neural network will prove beneficial in each step of the journey in the field of artificial intelligence. Learning concepts of neural networks and several types of neural networks is an added advantage.
Today, most of the complex problems related to image classification, speech recognition, machine translation are possible with the help of the neural network and deep learning techniques. Typically, a neural network is a series of algorithms that comprise artificial neurons much like a human brain to process a complex problem and produce an output.
Natural Language Processing
Natural language processing (NLP) is a subfield of artificial intelligence. This technology enables us to build intelligent systems with the ability to read, interpret, and understand human language.
The primary aim of NLP is for machines to perform high volumes of repetitive tasks related to texts such as translation software, search engines, editing and grammar checking tools, voice assistants, search engines. NLP has brought linguistics and computer science together in the same frame of work. The most popular innovation arising out of NLP research is Google Translate.
Deep Learning
Deep learning is yet another cutting-edge technology for building smarter systems. It can be considered as a subset of machine learning. The deep learning-based frameworks or models are deployed using machine learning techniques for problem-solving.
With deep learning, a neural network model improves on its performance without the need for human supervision by examining the data. Deep learning can be applied for various tasks related to detecting objects, speech recognition, decision making, and pattern recognition to conclude similar to a human decision-making process.
4. Non-technical skills
An AI engineer needs to be well equipped with the industry knowledge and ability to communicate the ideas well to an audience that doesn’t necessarily have a technical background such as stakeholders.
To be completely successful and have a higher role in the organization, the importance of passing complicated technical information in a simplified manner to put across a non-technical person also determines the depth of the understanding of the professional itself.
The days where technical professionals dealt with the technical aspects specifically are far left behind. It is essential to have strong communication skills to be considered for a supervisory role in the career of such professionals.
Another major aspect that adds to the skill set is to have critical thinking capabilities. It is a known fact that AI engineers deal with large amounts of data. The ability to find meaningful insights out of these datasets and come up with a conclusion is an asset for any organization.
5. Foundations for an Artificial intelligence career: Absolute Beginners
Step 1: Putting an idea into motion
There must be an idea to be put into motion. Beginners may benefit from using a real-life example that is common to practice. The individuals who are more confident with their basic foundations of programming should identify a problem to understand the process of an AI engineer’s role.
Once the problem is identified, it is important to understand the requirement of the problem to be able to use the right approach for problem-solving. For example, a problem under the category of supervised learning might not benefit from an unsupervised learning solution.
For absolute beginners, it is advisable to communicate with a mentor or an academician for the right guidance. Joining a technical group that shares crucial information to evolve as an AI professional will be advantageous.
Step 2: Understanding the process
The best approach for working through a machine learning problem is highlighted below.
- To identify the problem and the gaps that need to be addressed.
- Collection of the right data set.
- Picking the right algorithm.
- Improve the performance and the evaluation of results for putting the approach into production for real-time work.
Step 3: Programming Language
With several programming languages, an individual may not be skillful in all of them. However, it will be essential to be equipped with programming languages such as Python and basic knowledge of C++, Java to get started.
The people who are looking to get into the data science platform may add R, SAS into their knowledge repository. Individuals who are targeting computer vision as a career choice will benefit from the knowledge and ease of use of programs like MATLAB.
Step 4: Practice
The most important of all the requirements is the commitment and hours of practice to improve the skills. The basic approach for practice is to use public datasets and try to solve the problem. There are multiple references and solutions available online and most specifically GitHub for guidance.
Another form of practice would be to take up machine learning challenges by companies like Kaggle and other prominent platforms. Guided projects and MOOC courses on platforms like Coursera are some of the very popular options for practicing the skill and refining them.
Step 5: Portfolio
After gaining proficiency in the programming language, one must build a solid portfolio to demonstrate the skills to potential employers looking to hire professionals in this industry.
Possible roles in the AI sector
Data Scientist
A data scientist is responsible for gathering data and analyzing it to extract meaningful information for an organization for business value and growth. Data scientists have a background in math, statistics, data analytics, machine learning, and AI.
In recent years, data science jobs have increased by 256 percent for the last 7 years. Such an increase in the number of jobs listed demonstrates the high demand for data scientists.
Required Skills: R, Python, SAS, Spark, Tableau
Machine Learning Engineer
The key individuals for successful completion of machine learning projects are machine learning engineers. These professionals build and manage platforms for various projects. A machine learning engineer has a foundation of applied research and artificial intelligence tools. Machine learning engineers can manage colossal datasets, build predictive models and automation and supervise the overall progress of the project.
Required Skills: Python, C++, MATLAB, R, Java, Julia, Lisp
AI Architect
The role of an AI architect is entirely different from a machine learning engineer or a data scientist. Typically, they are considered to be higher in the hierarchy. AI architects are the principle individual that determines how an AI project will proceed by determining the architecture and the overall framework within which the respected teams and experts will work to achieve the end goal.
They are also responsible for setting the objective and identifying various ways of achieving it while keeping track of the organization’s vision.
Required Skills: Machine learning, NLP, CRM, AI integration, API, Programming skills, Strategic thinking.
Big Data engineer
The role of a big data engineer is responsible for building an ecosystem that empowers the business frameworks to communicate efficiently within an organization. The key responsibilities included preparing and managing the big data environment. Ideally, the preference is given to candidates with a Ph.D. in the field of computer science or mathematics.
Required Skills: SQL, Apache Spark, Python, R, Java, Data mining and Data visualization
Business Intelligence Developer
The business intelligence (BI) developer is a role that requires identifying business trends, evaluating and assessing complex data sets for formulating a plan for business growth.
This role combines business know-how with AI. The key components of focus for a BI developer are profitability and efficiency of business operations. BI developers also work on the cloud platform to deal with complicated data and assist in the workflow of an organization.
Required skills: SQL, AI, Data Warehouse, Java Script, PowerBI, R, Tableau
Natural Language Processing (NLP)
Today, the AI world has emerged into the world of personal assistants for a simpler way of completing tasks. Chatbots navigating a website or problem solving of customers to virtual assistants at home, in a car, or on the smartphones, AI is everywhere. The AI techniques have been used to replicate human speech for assisting.
The problem of understanding a foreign language is not difficult anymore. From language translators to assisting in medical problems related to cognitive diseases, NLP has made a significant mark in the technical evolution. Such specialized tasks need experts in AI which are possible for engineers that are working in the NLP space.
Required Skills: Machine learning framework, Spark, Text classification and clustering, Machine Translation, Statistics and Algorithms, and Big data frameworks.
Artificial intelligence researcher
Much of the career opportunities have been targeting the application and other functionalities that generate new roles in the field of AI. However, a crucial role in the field of artificial intelligence is the need for researchers. An AI researcher is involved in discoveries for the advancement of AI and delves into newer opportunities and problems.
AI researchers are an integral part of academia and industry, publishing new findings and solutions out of which the most relevant ones make a mark as industry innovation by using some of those findings in building an AI product. These roles required extensive research background, hence higher degrees such as a master’s degree or a Ph.D. are preferred.
Required Skills: Mathematical and Algorithms, Statistics, Python, MATLAB, C++, Java, Machine learning and Neural Networks, GAN
Top online platforms to learn AI
Google AI
A solid platform by Google to hone the skills. The courses begin from the foundations up to an advanced level. To get started with this course, there are no prerequisites and a person who has a keen interest to learn will gain the most out of it. It is designed in a way that a beginner with minimal coding skills will be a seasoned professional in no time.
Udacity
A popular online learning platform that is taught by scientists and professionals from top-tier universities such as Stanford University. Udacity has a very unique visual representation based class where theories are taught in the simplest of manner. It has a wide range of courses starting from absolute beginners to the most advanced and specialized courses.
Coursera
Perhaps, the most popular among all the platforms, Coursera is tied up with several top universities across the world. Deeplearning.AI and machine learning by Stanford university are some of the most well-structured courses on artificial intelligence.
The courses are taught by the most renowned expert in AI, Andrew Ng who is also the co-founder of Coursera. There are basic introductions as well as specialization courses that introduce all the major concepts and practical lab work to have hands-on experience. The certification from Coursera is from top universities in the world, thus holds industrial recognition.
Udemy
Udemy has been popular among learners for being affordable without compromising on the quality of content that is being taught. For those who are looking to get started Udemy is a safe choice. Among several courses, Artificial Intelligence A-Z: How to build an AI is perhaps the most sought after course.
edX
edX is yet another platform that is renowned for being founded by Harvard University and MIT. There is a range of AI courses according to the level of the learner. Courses are well-structured and hold industrial recognition with certification by IBM and Harvard. There are specific courses that are offered at no charge.
Simplilearn
Simplilearn is one of the largest online platforms that provide specialized courses that are taught by industry professionals. The mode of teaching is a combination of theory and practice that are being taught in real-time by the trainers. It has a wide range of courses and specializations to master the skills of artificial intelligence.
Futurelearn
Futurelearn is an online platform offering hundreds of courses from leading universities worldwide. The majority of the courses can be availed for free but the test assessments and certification needs an update to a paid course.
There are numerous course options for artificial intelligence starting from the very basics to the advanced level. These courses are in partnership with Microsoft that lends industry recognition.
Microsoft Learn
Microsoft has designed a learning path with multiple modules of several courses including AI, that assures to provide a hands-on approach and an all-around development of fresher and experts with a well-designed course. These are certificates that have one of the highest industrial acceptance ensuring a successful career.
Conclusion
Artificial intelligence has played a vital role in the technical evolution that we witness today. Its role in the future may be uncertain but the way AI innovations are being rapidly introduced differs from the opinion of uncertainty.
The quality of life across industries has marginally improved with the capability of automation without human intervention.
Artificial intelligence careers are at the peak and the demand for such professionals justifies it. As more industries are opting for the use of AI in their operations, a career in AI will boost the growth of an individual professionally.
To establish a career as an AI engineer, the pre-requisites for succeeding in this industry are continuous up-gradation to stay up-to-date with the latest trends and technologies. There is a possibility of lagging in this fast-paced competitive industry, hence all aspirants must equip themselves well.
It is a must to have a thorough knowledge of theory and hands-on experience with the tools and techniques along with good analytical skills and critical thinking. Well-developed interpersonal skills and programming skills is the most desirable combination in this industry, which is likely to provide opportunities to climb the hierarchical pyramid more swiftly.
Lastly, one must be aware to opt for certification that holds industrial recognition. Opting for industrial certification by the likes of Google, IBM and Microsoft will be a permanent badge that will ensure appropriate job roles being offered.
As the range of salary continues to witness a hike each year, it is the most appropriate moment for experienced professionals and aspirants to plunge into the world of AI.
