A Machine Learning Engineer's job is essentially a combination of two key professions - Data Scientists and Software Engineers.
A Data Scientist's primary emphasis is on experimenting with Big Data, while a Software Engineer's primary focus is on programming (writing code). Both responsibilities are fundamentally distinct. A Data Scientist's work is more analytical. These analytical specialists utilize a mix of mathematical, statistical, and analytical abilities and machine learning (ML) technologies to collect, process, and analyze considerable information to obtain insights.
On the other hand, software Engineers are skilled coders/programmers who create scalable programs and develop software solutions for businesses. To them, the whole notion of ML seems esoteric. Data Scientists' models are generally incomprehensible to Software Engineers since they are complicated, lack clear design patterns, and are not clean (contrary to what Software Engineers learn!).
A Machine Learning Engineer's job is quite similar to that of a Data Scientist in that both jobs entail dealing with massive amounts of data. As a result, both Machine Learning Engineers and Data Scientists must be adept at data management. However, it is the only resemblance between these two jobs.
Data Scientists are primarily concerned with producing valuable insights to drive company development via data-driven decision-making. Machine Learning Engineers, on the other hand, concentrate on creating self-running software for predictive model automation.
In such models, each time the program executes a function, the outcomes are used to conduct subsequent operations more accurately. This constitutes the software's "learning" process. Engines of Recommendation Netflix and Amazon are two of the most exemplary instances of clever software.
Typically, Machine Learning Engineers collaborate closely with Data Scientists. While Data Scientists extract valuable insights from big datasets and convey the knowledge to business stakeholders, Machine Learning Engineers guarantee that the models employed by Data Scientists can absorb massive quantities of real-time data to provide more accurate findings.
To investigate and transform data science prototypes.
Machine Learning systems and schemes must be developed.
Using test findings, conduct statistical analysis and fine-tune models.
To locate accessible datasets for training purposes on the internet.
To train and retrain ML systems and models as needed.
Extending and improving current machine learning frameworks and libraries.
To create Machine Learning applications based on the needs of the customer/client.
To investigate, test, and develop appropriate ML algorithms and tools.
To rate ML algorithms based on their success likelihood after analyzing their problem-solving skills and use-cases.
Following are the skills required for becoming a Machine Learning Engineer-
A master's degree in computer science, mathematics, statistics, or a related field is required.
Advanced mathematical and statistical abilities (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.)
Strong data modeling and data architecture abilities.
Programming expertise in Python, R, Java, C++, and other languages is preferred.
Understanding of Big Data frameworks such as Hadoop, Spark, Pig, Hive, Flume, and others.
Working knowledge of ML frameworks such as TensorFlow and Keras.
Working knowledge of different machine learning libraries and packages such as Scikit Learn, Theano, Tensorflow, Matplotlib, Caffe, and others.
Excellent written and verbal communication skills
Excellent interpersonal and teamwork abilities.
According to Glassdoor, the average yearly pay for a Machine Learning Engineer in India is Rs. 7,95,677. Although the payment of a Machine Learning Engineer is greater than the national average, it is dependent on business size and reputation, location, skillset, educational background, and, of course, professional experience, just like any other employment.
Here is a pay-scale for ML Engineers in some of the industry's top companies-
Accenture – Rs. 10,11,000 – 15,28,000 LPA Microsoft – Rs. 14,62,000 – 22,44,000 LPA
LPA - Rs. 8,50,481 Quantiphi
Tata Consultancy Services — LPA of Rs. 4,12,706
LPA – Infosys – Rs. 3,77,000 – 6,69,000
Over the past decade, the demand for Machine Learning Engineers has exceeded that of Data Scientists. According to the 2017 LinkedIn US Job Report, Machine Learning Engineer ranked first, with a 9.8-fold increase in five years (2012-17).
The worldwide Machine Learning market is expected to surpass $39,986.7 million by 2025, expanding at a CAGR of 49.7 percent between 2017 and 2025. These figures show that the ML market is growing at an unprecedented rate. Because of the increasing rivalry, businesses will need to employ skilled ML Engineers and other Data Science experts to remain firmly rooted in the market.
Machine Learning is quickly gaining momentum in contemporary business, and its applications and use-cases are becoming as diverse as Big Data itself.
Businesses and organizations are leveraging ML for spam detection and fraud detection; image and speech recognition systems; intelligent personal assistants (Siri, Alexa) and autonomous cars; intelligent homes and IoT power; generating accurate traffic predictions; personalizing social media services and online shopping/viewing services; refining search engine results, and much more.
Soon, more amazing discoveries will be pioneered by Machine Learning, and Machine Learning Engineers will remain an essential component of all such ML activities.