Role of AI Engineer in 2021

Role of AI Engineer in 2021 Artificial Intelligence is the latest buzzword that has taken every industry by storm.

It is not an

exaggeration to mention that AI has reformed the way an industry

operates, be it IT, Banking, Automobile, Manufacturing, Healthcare, etc.

In the IT sector, AI helps in building safe systems, improving

automating

processes,

productivity, and many more, whereas in Banking, AI assists in Fraud

detection, Customer Service, etc. The application of AI is beyond industries, thus making it one of the most sought after careers. Do you know how AI assists you in day to day activities?

Remember the last time you used Google maps to visit a place and the App keeps on updating you regarding the traffic information. Well it is AI that is analyzing the traffic speed to keep you informed and suggest how long it will take to reach the destination. Isn't it fascinating how every aspect of traffic is analyzed to give you real-time updates? From Movie recommendations to

Online Banking to Smart

Assistants and Autonomous Vehicle, everything is powered by Artificial intelligence.

Are Data Scientist, Data Engineer, Software Developer or AI Engineer same?

It is very common to confuse the term Data Engineer with AI Engineer, however, in practice

Data Engineer is a subset of AI Engineer. The problem solver who

understands business goals, builds neural networks, implements machine learning algorithms, and develops software is classified as an AI Engineer. In other words, an AI Engineer has the expertise of a data engineer, software developer, and data scientist in addition to the mastery of AI skills.

As an AI Engineer your goal would be to develop new systems or applications that make use of

artificial intelligence to improve the performance of the business.

Following are some of the skills you need to master: •

Analyzing AI principles to associate it with business problems.

•

Applying the techniques of machine learning and AI for reconstruction.

•

Implementing machine learning and AI solutions to address specific business problems.

•

Developing AI-powered solutions that mimics behavior of humans and completes complex tasks or processes.

•

Combining the best practices of machine learning with artificial intelligence to create solutions.

•

Evaluating the application’s performance in AI-ML domains.

•

Improving the algorithms to improve performance and make it more powerful.

AI Project Lifecycle There is a standard procedure a company follows to complete an AI project. These are: •

Data Engineering: Here, the engineers prepare the basic data and transform it into the formats that can be easily used by other team members. This is referred to as the process of data cleaning. It is one of the most important steps as the cleanliness of data determines the accuracy of the model.

•

Modeling: This step involves looking for patterns in the dataset. These patterns

are

useful

in

making

opportunities, and risks of the

predictions

for

various

outcomes,

company. The modeling also helps in

predicting the cause and effect relationship in the data.

•

Deployment: The deployment step is all about combining the dataset with the model and testing its integration. This is done before deploying the model for production. In case of any mismatch, the modeling is redone.

•

Business Analysis: Once the model is finalized and deployed, the business analysis step is all about evaluating the performance of the model. The goal is to see how the model is performing and make amendments to maximize benefit.

•

AI infrastructure: As the last step of the AI project’s lifecycle, engineers build fast, reliable, and secure systems that are easily scalable.

Do Industries Need AI Engineers?

As per a 2019 Gartner report, the growth in AI applications has been 270% between the year 2015 and 2019. This simply indicates the insane growth in the usability of AI. Based on the on- going research, it is valid to say that opportunities in AI will continue to grow further at an exponential rate. It is also referred to as the “Jobs of the Future.” According to the World Economic Forum (WEF), there will be a net 58 million new jobs because of AI. Companies

such as Google, Apple, Amazon are on continuous lookout for Certified AI Engineers who have

a deep understanding of the systems. On top of it, there are many

startups specializing in artificial intelligence who are nurturing individuals with AI skills.

However, there is a serious lack of highly experienced and trained professionals in the field of AI thus restricting the market growth. If you want to be an AI engineer, start by learning from courses and become a Certified AI Engineer. AI Engineer skills from a renowned platform will help you kickstart your career at high-speed.

Conclusion The world needs professionals with AI engineer skills to develop, program, and train complex algorithms and create AI applications. The expertise in Computer Science, Software

Engineering,

Data

minimum. However, if you do

Science,

Mathematics,

and

Statistics

is

bare

not have a background in the before-mentioned

education, you can still pursue the dream of

becoming an AI Engineer. To be a

certified AI Engineer, you need to learn the following

mandatory skills- Machine

Learning, Deep Learning, Statistics, Model Deployment, Basic Programming (Python, Java, etc.). If data is your passion and you want to add fuel to the fire, get yourself enrolled in a recognized AI Engineering Certification course. Happy Learning.