In Robert Half’s 2019 Future of Work report, Brandon Purell, Senior Analyst at Forrester Research stated:
“One hundred per cent of any company’s future success depends on adopting Machine Learning. For companies to be successful in the age of the customer, they need to anticipate what customers want, and Machine Learning is absolutely essential for that.”
With an increasing number of organizations exploring and leveraging the tools of Data Science – AI and ML – the demand for skilled professionals in these domains are on the rise. The role of a Machine Learning Engineer is one of the highest in-demand Data Science roles in the industry.
Today, we’ll dig deep into the role of a Machine Learning Engineer and understand its core responsibilities and requirements.
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Rise of the Machine Learning
Essentially, the role of a Machine Learning Engineer is a marriage between two pivotal roles in the industry – Data Scientists and Software Engineer.
While the core focus of a Data Scientist is to experiment with Big Data, a Software Engineer primarily focuses on programming (writing code). Both roles are inherently different. The job of a Data Scientist is more analytical – these analytical experts use a combination of mathematical, statistical, analytical skills and ML tools to gather, process, and analyze massive datasets to gain insights.
On the contrary, Software Engineers are expert coders/programmers who write scalable programs and design software systems for companies. To them, the whole concept of ML seems distant. The models created by Data Scientists are mostly incomprehensible to Software Engineers – they are complex, showing no clear design patterns, and are not clean (everything contrary to what Software Engineers learn!)
This is precisely why companies felt a need for a Machine Learning Engineer – a professional who can bring the best of both worlds to the table. Organizations wanted someone who can demystify the Data Scientists’ code and make it more useful and accessible. Machine Learning Engineers combine the laws and rules of the Data Science world with that of programming to help organizations reap the full benefits of AI/ML technologies while adhering to the standard programming practices and protocols.
What does a Machine Learning Engineer do?
The job of a Machine Learning Engineer is quite similar to that of a Data Scientist, in the sense that both roles involve working with vast volumes of data. Hence, both Machine Learning Engineers and Data Scientists must possess excellent data management skills. However, that’s all the similarity that these two roles share.
Data Scientists are mainly concerned with generating valuable insights for driving business growth through data-oriented decision making. In contrast, Machine Learning Engineers focus on designing self-running software for predictive model automation.
In such models, each time the software performs a function, it uses the results of that operation to perform future operations with greater accuracy. This makes up the “learning” process of the software. Recommendation Engines Netflix and Amazon are the best examples of this type of intelligent software.
Usually, Machine Learning Engineers work in close collaboration with Data Scientists. While Data Scientists extract meaningful insights from large datasets and communicate the information to business stakeholders, Machine Learning Engineers ensure that the models used by Data Scientists can ingest vast amounts of real-time data for generating more accurate results.
Responsibilities of a Machine Learning Engineer
- To study and convert data science prototypes.
- To design and develop Machine Learning systems and schemes.
- To perform statistical analysis and fine-tune models using test results.
- To find available datasets online for training purposes.
- To train and re-train ML systems and models as and when necessary.
- To extend and enrich existing ML frameworks and libraries.
- To develop Machine Learning apps according to customer/client requirements.
- To research, experiment with, and implement suitable ML algorithms and tools.
- To analyze the problem-solving capabilities and use-cases of ML algorithms and rank them by their success probability.
- To explore and visualize data for better understanding and identify differences in data distribution that could impact model performance when deploying it in real-world scenarios.
Skills Required to be a Machine Learning Engineer
- Advanced degree in Computer Science/Math/Statistics or a related discipline.
- Advanced Math and Statistics skills (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.)
- Robust data modelling and data architecture skills.
- Programming experience in Python, R, Java, C++, etc.
- Knowledge of Big Data frameworks like Hadoop, Spark, Pig, Hive, Flume, etc.
- Experience in working with ML frameworks like TensorFlow and Keras.
- Experience in working with various ML libraries and packages like Scikit learn, Theano, Tensorflow, Matplotlib, Caffe, etc.
- Strong written and verbal communications
- Excellent interpersonal and collaboration skills.
Salary of a Machine Learning Engineer
According to the 2019 Indeed report – The Best jobs in the U.S. & India- Machine Learning Engineer takes the crown position in the list with an average salary of $146,085. What’s more interesting is that the role of an ML Engineer recorded a whopping 344% increase since 2015!
Glassdoor maintains that the average annual salary of a Machine Learning Engineer in India is Rs. 7,95,677. Although the salary of a Machine Learning Engineer is higher than the national average, just like any other job, it depends on company size and reputation, location, skillset, educational background, and of course professional experience.
Here’s a salary chart of ML Engineers in some of the leading companies in the industry:
- Microsoft – Rs. 14,62,000 – 22,44,000 LPA
- Accenture – Rs. 10,11,000 – 15,28,000 LPA
- Quantiphi – Rs. 8,50,481 LPA
- Tata Consultancy Services – Rs. 4,12,706 LPA
- Infosys – Rs. 3,77,000 – 6,69,000 LPA
Read more about the Machine Learning Salary in India.
Why is the demand for Machine Learning Engineers increasing?
In the last decade, the demand for Machine Learning Engineers has even surpassed the need for Data Scientists. In the 2017 LinkedIn US Job report, Machine Learning Engineer took the top rank with a recorded growth of 9.8 times in five years (2012-17).
As for the global Machine Learning market, it is predicted to exceed $39,986.7 million by 2025, growing at a CAGR of 49.7% between 2017 and 2025. These stats make it clear that the ML market is expanding at an unprecedented pace. In light of the growing competition, companies will have to hire talented ML Engineers along with other Data Science professionals to stay grounded firmly in the market.
With Machine Learning fast gaining traction in the modern industry, its applications and use-cases are becoming as varied as Big Data itself.
Businesses and organizations are leveraging ML for spam detection and fraud detection; for image and speech recognition systems; to create smart personal assistants (Siri, Alexa) and autonomous cars; to enable smart homes and power IoT; to generate accurate traffic predictions; to personalize social media services and online shopping/viewing services; to refine search engine results, and so much more.
Soon, there’ll be more such astounding breakthroughs pioneered by Machine Learning, and Machine Learning Engineers will continue to be an integral part of all such ML operations.
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What is the future of Machine learning?
Machine learning is slowly making its way into each and every sector of society. From voice recognizing objects to intelligent devices, all of these new inventions make use of machine learning. These days machine learning is used in the banking sector, entertainment and media sector, investment sector, and many other sectors. There are a few more sectors that have been untouched by machine learning, but machine learning professionals are slowly doing research to reach these sectors. Machine Learning professionals are in high demand, as almost every technological startup and significant corporate wants to hire them to help them contemporize their companies.
What is the relationship between Artificial Intelligence and Machine Learning?
Machine Learning is the study of systems that can learn from past experiences, such as data. When we talk about machine learning, we usually refer to predictive modeling, which is a subfield of machine learning. It has to do with constructing models from data in order to make predictions based on new data. Artificial intelligence is a unit of computer science that emphasizes on developing smart computers with human-like intelligence, including a wide range of capabilities like learning, remembering, and goal-setting. Artificial Intelligence has the subfield called Machine Learning.
What are the real-life use cases of Machine learning?
Machine learning has a wide range of applications, from business to science and medicine. It's used in medicine to search through big chemical databases and determine which drug-like compounds are most likely to bind to a specific receptor protein. It's used in web search and recommendation to recognize and find input, find relevant searches, anticipate which results are most relevant to us, and return a ranked output. It is used in banking and finance to determine whether an applicant is eligible, identify credit card fraud, and discover potential stock market trends. Machine learning is also used in areas such as text and speech recognition, as well as space, astronomy, robotics, social networks, and advertising.