Role of Machine Learning Engineer? What Do They Do?


Opting Machine Learning as a career is trending nowadays. The growth in the Industry is remarkable. If you wish to get in-depth understanding you can refer to Machine Learning classes in Pune. One can Go either for online and offline options. The job of a Machine Learning Engineer is essentially a marriage between two crucial positions in the field: software engineer and data scientist. 

A Software Engineer’s primary concentration is on programming, as opposed to a Data Scientist’s primary focus on experimenting with Big Data (writing code). They are fundamentally different jobs. A Data Scientist’s job is more analytical; these analytical professionals gather, analyse, and analyse huge datasets to uncover insights using a combination of mathematical, statistical, analytical, and ML methods. 

Software engineers, on the other hand, are skilled coders and programmers who create software systems and scalable programmes for businesses. They find the entire idea of ML to be abstract. Most data scientist models are incomprehensible to software engineers because they are complex, lack obvious design patterns, and lack cleanness (contrary to everything software engineers are taught!). 

Because they can combine the best of both environments, machine learning engineers are exactly what businesses thought they needed. Organizations sought a someone who could simplify and improve the usability of the Data Scientists’ code. To assist businesses fully benefit from AI/ML technologies while adhering to accepted programming conventions, machine learning engineers mix the laws and regulations of the data science world with those of programming. For more information you can refer to machine learning course in pune. 

What does a Machine Learning Engineer do?

A Data Scientist and a Machine Learning Engineer both work with enormous amounts of data, which is a common aspect of both jobs. Consequently, good data management abilities are required of both Machine Learning Engineers and Data Scientists. But that is the only similarity between these two roles. 

Data scientists are primarily focused on producing insightful data that may be used to make data-driven decisions that will increase corporate growth. Machine learning engineers, on the other hand, concentrate on creating self-running software for the automation of prediction models.

In such models, each time the programme executes a function, it makes use of the outcomes of that operation to execute subsequent operations more precisely. The software’s “learning” process is made up of this. engines that recommend things The most effective examples of this clever software are Netflix and Amazon. 

Machine learning engineers and data scientists frequently collaborate closely. Machine Learning Engineers make sure the models used by Data Scientists can absorb massive volumes of real-time data for producing more accurate results, while Data Scientists extract relevant insights from enormous datasets and disseminate the knowledge to business stakeholders. 

A machine learning engineer’s duties

  • To research, modify, and apply data science prototypes.
  • To create and construct methods and plans for machine learning. ● Employing test findings to do statistical analysis and improve models. ● To search internet for training datasets that are readily available. ● ML systems and models should be trained and retrained as appropriate. ● To improve and broaden current ML frameworks and packages ● To create machine learning applications in accordance with client or customer needs.
  • To investigate, test, and put into practise appropriate ML tools and algorithms. ● To evaluate the application cases and problem-solving potential of ML algorithms and rank them according to success likelihood.
  • To better comprehend data through exploration and visualisation, as well as to spot discrepancies in data distribution that might affect a model’s effectiveness when used in practical situations.

What You Need to Know to Become a Machine Learning Engineer?

  • Advanced degree in mathematics, statistics, computer science, or a similar field. ● Advanced knowledge in math and statistics (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.)
  • Talents in data modelling and architecture that are strong
  • Coding expertise in languages like Python, R, Java, C++, etc.
  • Having familiarity with big data technologies like Hadoop, Spark, Pig, Hive, and Flume, among others
  • Working knowledge of machine learning frameworks like TensorFlow and Keras.
  • Working knowledge of a variety of machine learning tools and libraries, including Scikit-Learn, Theano, Tensorflow, Matplotlib, Caffe, etc.
  • Strong verbal and written communication
  • Outstanding interpersonal and teamwork abilities.

Why is there a growing need for machine learning engineers?

The requirement for Machine Learning Engineers has even outpaced that of Data Scientists in the previous ten years. Machine Learning Engineer was ranked first in the 2017 LinkedIn US Job Report, with a growth of 9.8 times over the previous five years (2012-17). 

The market for machine learning is expected to reach more than $39,986.7 million by 2025, expanding at a CAGR of 49.7% from 2017 to 2025. These figures demonstrate the ML market’s extraordinary rate of growth. Companies will need to appoint excellent ML Engineers and other Data Science specialists in order to stay rooted firmly in the market in light of the escalating competition. 

Machine Learning is quickly gaining popularity in the modern economy, and as a result, its applications and use-cases are diversifying just like Big Data. 

Businesses and organisations are utilising ML for a variety of purposes, including spam detection and fraud detection, image and speech recognition systems, smart personal assistants (Alexa, Siri), autonomous vehicles, smart homes, and the Internet of Things (IoT). ML is also used to personalise social media services, online shopping/viewing services, search engine results, and much more. 


Machine Learning Engineers will continue to be a crucial component of all such ML operations, and there will soon be more such amazing achievements led by ML. You can also enroll in machine learning training in pune for better understanding.

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