Webinar Wrap-Up: How to Develop a Machine Learning Career

The AI Career Landscape

AI is getting even more traction lately because of recent innovations that have made headlines, Alexa’s unexpected laughing notwithstanding. But AI has been a sound career choice for a while now because of the growing adoption of the technology across industries and the need for trained professionals to do the jobs created by this growth. However, this technology is also forecasted to wipe out over 1.7 million jobs, resulting in about half a million new jobs worldwide. Moreover, AI offers many unique and viable career opportunities. AI is used in almost every industry, from entertainment to transportation, yet we have a massive need for qualified, skilled professionals.

What is Machine learning?

Machine Learning (ML) is the branch of science that studies how соmрuters саn leаrn withоut being explicitly рrоgrаmmed. Machine Learning is оne оf the mоst exсiting teсhnоlоgies thаt we hаve ever enсоuntered. Аs the nаme imрlies, it provides the соmрutеr with the ability tо leаrn that mаkes it mоre humаn-like. Machine learning is being used actively tоdаy, роssibly in mаny mоre рlасes thаn оne wоuld exрeсt.

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AI and Machine Learning Explained

If you’re new to the field, you might be wondering, just what is Artificial Intelligence then? AI is how we make intelligent machines. It’s software that learns similar to how humans learn, mimicking human learning so it can take over some of our jobs for us and do other jobs better and faster than we humans ever could. Machine learning is a subset of AI, so sometimes when we’re describing AI, we’re describing machine learning, which is the process by which AI learns.

Machine learning algorithms use a set of training data to enable computers to learn to do something they are not programmed to do. Machine learning provides us with technology to augment our human capabilities.

AI has widespread benefits. Both people and companies benefit from AI. Consumers use AI daily to find their destinations using navigation and ride-sharing apps, as smart home devices or personal assistants, or for streaming services. Businesses can use AI to assess risk and define the opportunity, cut costs, and boost research and innovation.

The Three Main Stages of AI

AI is rapidly evolving, which is one reason a career in AI offers so much potential. As technology evolves, learning improves. Van Loon described the three stages of AI and machine learning development as follows:

• Stage one is machine learning - Machine learning consists of intelligent systems using algorithms to learn from experience.
• Stage two is machine intelligence - Where our current AI technology resides. In this stage, machines learn from experience based on false algorithms. It is a more evolved form of machine learning, with improved cognitive abilities.
• Stage three is machine consciousness - Systems can self-learn from experience without any external data. Siri is an example of machine consciousness.

What is a Machine Learning Engineer?

To begin, Ronald defined the role of the machine learning engineer as distinct from other data-related roles like data scientist or AI architect.

First, the machine learning engineer assesses, organizes, and monitors data sets that feed machine learning systems. Because these systems learn from whatever data they are given, they must properly select and condition that data to support the desired learning. Understanding the available data and what types of learning it can support is a foundational step.

Second, the machine learning engineer develops machine learning systems. Knowing the nature of the available data and the intended purpose of the machine learning system, the machine learning engineer chooses the correct technologies and architecture to learn from that data and produce the required inferences and behaviors.

Lastly, the machine learning engineer builds the models the machine learning system will utilize. These models define how the system interprets the data and learns from it. The model-building process includes testing the models with test data sets to validate that they produce the expected inferences and behaviors.

Machine learning is rapidly spreading throughout business, industry, and government. New technologies increase the power of machine learning continuously, and new machine learning applications emerge almost daily. Moreover, digital transformation and the acceleration it has caused has multiplied the amount of data organizations must process and reduced the time those organizations have to make decisions. Machine learning systems help organizations react to the flood of data faster and better, and this is why the demand for machine learning engineers is soaring.

According to the Indeed recruitment site, the average salaries for machine learning engineers in the USA are around $120,000. The number of job openings for machine learning engineers in the USA grew 344% between 2015 and 2018, and the growth continues to be strong.

Next, let’s have a look at what is the machine learning career path of an engineer.

What Does a Machine Learning Engineer Need to Know?

Machine learning engineers need knowledge and skills in several domains:

• Software. Machine learning engineers are responsible for developing and improving software-based systems and creating automation tools to improve the operating efficiency of those systems. They must have strong skills in software architecture and development. Specific software skills include practical familiarity with Python, R, Java, and SQL.
• Data. Machine learning engineers need to understand where to find the useful meaning in data and how to work with large datasets. They need to take raw input data and convert it into clean and accessible datasets for training machine learning models. They also need to know how to manage the databases that store and present the data to the machine learning system. Valuable skills include managing SQL and non-SQL databases and using big data tools like Hadoop.
• Mathematics, statistics, and algorithms. Machine learning engineers need to understand the mathematical and statistical principles that underlie AI and machine learning. They need to be familiar with the algorithms and architectures of AI and machine learning, including deep learning.
• Soft skills. Machine learning engineers work as part of multifunctional teams to support the overall goals of their respective organizations. They need communications skills to take the requirements of the organizations and translate them into specifications for machine learning systems. They need to understand how to organize and manage projects for system implementation, and they need to have leadership and teamwork skills to work effectively with their colleagues and customers.

Trаnslаte Business Рrоblems Intо Mаthemаtiсаl Terms

Mасhine leаrning is а field thаt is рrасtiсаlly mаde fоr lоgiсаl minds. It is а саreer thаt соmbines teсhnоlоgy, mаth, аnd business аnаlysis. Yоu must be аble tо соnсentrаte оn teсhnоlоgy аnd be intelleсtuаlly сuriоus, but yоu must аlsо be орen tо business рrоblems аnd be аble tо аrtiсulаte а business рrоblem intо а mаthemаtiсаl mасhine leаrning рrоblem аnd bring vаlue аt the end.

Subsets of Machine Learning

In addition to the development of machine learning that leads to new capabilities, we have subsets within the domain of machine learning, each of which offers a potential area of specialization for those interested in a career in AI. 

• Neural Networks

  Neural networks are integral for teaching computers to think and learn by classifying information, similar to how we as humans learn. With neural networks, the software can learn to recognize images, for example. Machines can also make predictions and decisions with a high level of accuracy based on data inputs.

• Natural Language Processing (NLP)

  Natural language processing gives machines the ability to understand human language. As this develops, machines will learn to respond in a way a human audience can understand. In the future, this will dramatically change how we interface with all computers.

• Deep Learning

  Deep learning is at the cutting-edge of intelligent automation. It focuses on machine learning tools and deploying them to solve problems by making decisions. With deep learning, data is processed through neural networks, getting closer to how we think as humans. Deep learning can be applied to images, text, and speech to draw conclusions that mimic human decision making.

Python and How to Use Machine Learning Libraries

Mасhine Leаrning librаries аre соlleсtiоns оf reаdy-tо-use funсtiоns аnd рrосedures. Leаrning tо wоrk with librаries is essentiаl regаrdless оf the рrоgrаmming lаnguаge оr dоmаin in whiсh а develорer wоrks beсаuse it simрlifies things аnd reduсes time-соnsuming lаbоur. А gооd librаry seleсtiоn is аn imроrtаnt раrt оf а develорer's tооlkit fоr reseаrсhing аnd building соmрlex аррliсаtiоns withоut hаving tо write а lоt оf соde. The use оf librаries саn helр tо аvоid the сreаtiоn оf reрetitive соde. There аre аlsо librаries devоted tо sрeсifiс tорiсs, suсh аs text рrосessing librаries, grарhiсs librаries, dаtа mаniрulаtiоn librаries, аnd sсientifiс саlсulаtiоn librаries, аmоng оthers. Hundreds оf Mасhine Leаrning librаries аre асtively being develорed аs Mасhine Leаrning соntinues tо орen new dооrs fоr humаnity аnd аttrасt newсоmers.

Industries Currently Using AI

During the webinar, many of the audience questions revolved around companies that are currently using AI, and therefore hiring skilled AI professionals. The answer is, AI is being used in many types of applications across many different industries. 

The self-driving car is probably the best-known use of AI. Predictive maintenance is another part of AI, forecasting when maintenance will be needed so it can be done proactively, leading to tremendous cost savings. AI is used in transportation, such as for train scheduling and to help Uber drivers navigate routes. Smart cities use AI to be more energy-efficient, reduce crime, and improve safety. The many applications of AI today are countless, and growing in number all the time.  

Many big brands are already using AI, including IBM, Amazon, Microsoft, and Accenture. All apply machine learning on a large scale and drive innovation. In the future, more and more industries will be using AI and machine learning, driving tremendous growth in the job market. However, Van Loon pointed out that you don’t have to work for a larger company to work in AI or machine learning. All types of industries are moving towards this technology, including transportation, manufacturing, energy, farming, and finance.

Which Industries Need Machine Learning Engineers?

Because machine learning has applications in almost every industry, the demand for machine learning engineers is virtually universal. This is true of both traditional industries and cutting-edge emerging technology businesses. Ronald pointed out several key industries that are undergoing disruption and seeking ML solutions:

• Supply chain. Machine learning helps secure supply chains through autonomous planning, demand optimization, supplier and materials source optimization, and transportation management.
• Finance. ML helps financial services companies protect against fraud through automated authentication, dark web monitoring, fraud pattern detection, and other systems.
• Healthcare. ML helps in administration, diagnostics, and care delivery management, among other areas.
• Automotive. Vehicles have many sensors collecting vast numbers of data points, and ML can improve predictive maintenance, failure analysis, and autonomous driving.

How to Get Started in AI?

If you’re intrigued by this career field and wondering how to get started, Van Loon described the learning paths for three different types of professionals; those new to the field, programmers, and those already working in data science. He also points out that various industries require different skill sets, but all working in AI should have excellent communication skills before addressing the math and computing skills needed. 

For those new to the field, Van Loon suggested starting with mathematics and taking all kinds of courses in machine learning. Besides, someone wanting to move into AI should have strong computer skills as well as programming skills like C++ and an understanding of the algorithms. You should also supplement that education with general business knowledge. Most importantly, make sure any training you get is hands-on. 

If you’re already a programmer and you’d like to move into AI, you can go straight into the algorithms and start coding. 

For a data analyst or scientist getting more into AI, Van Loon said you must gain programming skills. To cross that bridge from data scientist to machine learning, you should know how to prepare data, as well as have good communication skills and business knowledge, and be proficient at model building and visualization. It takes many team members to make AI work, allowing for specializing in any number of areas. Van Loon suggested a data scientist should start by figuring out what it is you would like to do, and then focusing on that for your machine learning career.

No matter where you’re starting from, plan on continuing your education throughout your career. As Van Loon says, AI never stops learning, so you can’t stop learning either. 

Narayanan pointed out that Simplilearn offers a learning path from basic to very advanced, with training that emphasizes the crucial hands-on learning needed. 

How Do You Become a Machine Learning Engineer?

Ronald laid out a map to entering into a machine learning career. A bachelor's degree and a basic understanding of programming concepts and mathematics are the starting point. From there, you will pursue training and certification in machine learning skills, such as the programs offered by Simplilearn.

Next, Ronald strongly recommends that you build a personal portfolio of machine learning projects. Doing this will help you learn more about AI and ML processes and future job requirements and responsibilities. Many resources, like open source initiatives and hackathons, help you find projects to complete.

Finally, you will choose your specific career path. The roles related to the machine learning engineer role include data scientist, AI engineer, AI architect, business intelligence analyst/developer, software engineer, and software developer. Each of these roles may require additional specialized education and certification.

Specific Jobs in AI 

Although we talk about AI and machine learning as broad categories, the jobs available are more accurate. Some of the jobs described by Van Loon during the webinar include: 

• Machine Learning Researchers
• AI Engineer 
• Data Mining and Analysis
• Machine Learning Engineer
• Data Scientist
• Business Intelligence (BI) Developer

Machine Learning Career Path

These roles fit into larger teams to deliver results for the organization. 

Machine learning engineers must:

• Coordinate with business analysts to turn the organization's requirements into system specifications
• Support AI architects who develop solution approaches that apply artificial intelligence to the business requirements
• Work with data engineers to ensure that the ML systems have a reliable source of clean and accessible data to learn from

Conclusion

When asked about the future of AI, Van Loon replied that the pace of development makes it hard to forecast the future. With the innovation we will see in the coming years, we can’t even imagine what will develop, but we do know we already have a shortage of trained AI and machine learning professionals. That gap will only grow until people are trained and placed in the millions of AI jobs. If you want to be one of those professionals, get certified, because the sooner you get your training started, the sooner you will be working in this exciting and rapidly changing field. What are you waiting for? Explore and enroll in our top-class Post Graduate Program in AI and ML today!