The market for Artificial Intelligence and Machine Learning is extremely hot right now.
It’s no surprise that the Artificial intelligence talent market is white-hot at present. In fact, Gartner maintains that the business value of AI will stand at $3.9 trillion in 2022., while IDC estimates that the worldwide spending on cognitive and artificial intelligence systems will reach $77.6 billion by 2022. Read: Future scope of Machine Learning
Reinstating the tremendous impact that AI is set to create in the near future, a 2019 report by the McKinsey Global Institute states that AI and ML hold the potential to create an additional business value of around $1.4-2.6 trillion in global marketing and sales, and another $1.2-2 trillion in supply-chain management and manufacturing by 2020.
What do these stats depict?
These statistics only show how AI and ML are penetrating the industry at an unprecedented pace. Today, almost every sector of the industry – healthcare, education, retail, manufacturing, supply chain and logistics, BFSI, and even governance – is leveraging the applications of AI and ML in some way or the other. Naturally, as the adoption of these intelligent technologies is increasing, so is the demand for skilled professionals in AI and ML increasing. This is the time to build your Machine Learning career path!
According to a 2019 Indeed report, Machine Learning Engineer is the #1 job in the list of The Best Jobs in the US, recording a whopping 344% growth with a median salary of $146,085 per year. In India, the national average salary for Machine Learning jobs is ₹11,05,748.
The demand for talented and skilled professionals in Machine Learning is at its peak now, and in the future, it will only escalate higher. The great thing about a Machine Learning career is that apart from job satisfaction and security, it also promises hefty annual compensation and fast career growth. All the more reason to consider building a Machine Learning career path.
Machine Learning Career Paths
Once you have acquired the right ML skills, here are the top five promising Machine Learning career paths that you can aspire for:
1. Machine Learning Engineer
As we said earlier, a Machine Learning Engineer career path is one of the most coveted and promising ones in the field of Data Science. Machine Learning Engineers are primarily involved with the design and development of ML systems and applications by using ML algorithms and tools.
It is their job to shape and develop efficient self-learning ML applications by performing statistical analysis and fine-tuning them using test results. They also conduct and run various ML experiments using programming languages such as Python, Java, Scala, R, and C++, to name a few.
Skills required: A Machine Learning Engineer must have a strong foundational knowledge of Mathematics, Statistics, and programming. He/she should be well-versed with software architecture, system design, data structures, data modeling, and ML algorithms. Read more about the machine learning skills.
2. Data Scientist
The job profile of a Data Scientist has been hailed as the “Sexiest Job of the 21st Century” by a Harvard Business review article. If this does not establish the popularity of this Machine Learning career path, what will!
Data Scientists are high-profile experts who leverage advanced technologies (like Big Data, AI, ML, Deep Learning, etc.) and analytical tools on a daily basis to collect, store, process, analyze, and interpret massive amounts of data. Their primary duty is to extract valuable insights from large datasets that can be converted into business value.
Skills required: Just like an ML Engineer, a Data Scientist must have good knowledge of Mathematics, Statistics, and programming (mainly in Python). Data Scientists must also have thorough experience of data mining and how to apply various statistical research techniques and use Big Data platforms (Hadoop, Pig, Hive, Spark, Flume, etc.). Read more about data science skills.
3. NLP Scientist
Natural language processing (NLP) aims to impart machines with the ability to understand natural human languages. NLP Scientists are primarily responsible for designing and developing machines and applications that can learn the patterns of speech of a human language and also translate spoken words into other languages. The goal here is to help machines comprehend human languages as naturally as humans do. Grammarly and Duolingo are two excellent examples of NLP applications.
Skills required: Since the primary job of NLP Scientists is to teach machines how to understand the nuances of human languages, they must be fluent in the syntax, spelling, and grammar of at least one language (the more, the better). Also, they should have the basic-level ML skills.
4. Software Developer/Engineer (AI/ML)
Software Developers/Engineers with specialization in AI/ML are the creative minds behind intelligent computer programs. Their main job is to develop efficient ML algorithms and applications. Software Developers/Engineers design, develop, and install AI/ML software solutions; create specific computer functions; prepare product documentation, flowcharts, layouts, diagrams, charts, etc. for visualization; write and test code; create technical specifications, upgrade and maintain systems, and much more.
Skills required: Software Developers/Engineers (AI/ML) must be proficient in coding in multiple programming languages, including Python, Java, R, C, C++, Scala, etc. They must possess a good understanding of operating systems, data structures, data architecture, computer architecture, data analytics, distributed processing, software testing and debugging, among other things. Also, they must have extensive knowledge of ML concepts, algorithms, systems, and tools.
5. Human-Centered Machine Learning Designer
Machine Learning has an exclusive branch that is dedicated to designing ML algorithms centered around humans. Hence, the name Human-Centered Machine Learning. Human-Centered Machine Learning Designers are responsible for creating intelligent systems that can “learn” the preferences and behavior patterns of individual humans through information processing and pattern recognition.
These systems require minimal or no human intervention or even cumbersome programs to account for every conceivable user scenarios. Netflix and Amazon’s Recommendation Engine is an excellent example of Human-Centered Machine Learning.
Skills required: As is true of any Machine Learning career path, Human-Centered Machine Learning Designers must also possess an in-depth understanding of various ML concepts, algorithms, and how they function. They should also have a good base in Mathematics and Statistics along with coding proficiency.
While these are the top five Machine Learning career paths, there are many other career pathways in this domain, such as Data Analyst, Data Architect, Cloud Architect, and Business Intelligence Developer, to name a few. Ultimately the choice of a Machine Learning career path depends on you – what do you wish to specialize in and what skills you have and wish to acquire.
Many industries employ machine learning for different purposes so the demand increases day by day. If you would like to know more about careers in Machine Learning and Artificial Intelligence, check out IIT Madras and upGrad’s Advanced Certification in Machine Learning and Cloud.
What are the skills required to opt for a career in ML?
Different Machine Learning professions require distinct skills to carry out the demands of their tasks. The first and most crucial requirement is coding skills in a programming language (any programming language, such as Python, C++, C, Java, etc.). Secondly, knowledge of fundamental statistics and calculus is another important skill, which comes in handy when creating Machine Learning algorithms. Machine learners will benefit from having knowledge of data engineering and software engineering skills. Students interested in a career in Machine Learning do not need to possess all of these skills because they can learn them over time.
How are Machine Learning and Data Science related?
Machine Learning is one of the most interesting tools in the current Data Science world. It is one of the numerous tools available in the field of Data Science. Data Science is an interdisciplinary domain that makes use of the extensive amounts of data and processing power at its disposal to generate insights. In other words, it is a field in the technological world that focuses on extracting meaning and insights from a vast amount of data. Whereas, Machine Learning is a set of Data Science approaches that allow computers to comprehend data. These methods produce good results without the need for explicit rules to be programmed.
Which is the most preferred programming language for Machine Learning professionals?
Python is the most preferred programming language for Machine learners. Most of the IT giants prefer Python because of many reasons. Continuous data processing is required for Machine Learning, and Python has built-in libraries and packages for almost every task. When working with complex Machine Learning applications, these features aid Machine Learning professionals in reducing development time and increasing productivity. Python's simple syntax and emphasis on code readability allow Machine Learning developers to concentrate on their writing rather than how they're writing it. Python's multi paradigm and flexible structure allows Machine Learning engineers to tackle a problem in the most straightforward way feasible.