Machine learning is one of the most discussed topics in the scientific world. It inherits Artificial intelligence and train systems to work automatically without human intervention. Likewise, data scientists are analytical specialists who implement mathematical algorithms to solve complex problems and do their jobs as a scientist or trend-spotter. The duties performed by machine learning engineers and data scientists are not clear up to this point. To discriminate between two, we need to examine them deeply.
A scientist is someone who is assumed to have full knowledge of the science behind his work while an engineer is assumed to build or recreate something. To differentiate between them, it is important to know the difference between data science and machine learning.
Machine learning Vs Data Science
Machine learning is a subset of AI. The artificial intelligence concept was discovered by McCarthy in 1956 which was based on “thinking machines”. This concept can be summarized into three points such as
- Automata theory
- Complex information processing
Artificial intelligence, today, is involved in Decision-making, Speech recognition, Translation between languages, Visual perception, etc and is known as sub-filed of computer science with the core objective to automate different tasks.
Machine learning is a branch of Artificial intelligence that implements data-driven algorithms to improve the decision making of computer systems without the need for extensive programming to train them. The basic principle of machine learning is to design an algorithm that receives input and uses a statistical model to generate appropriate output and update outputs in case of any change in the data set.
The process of machine learning is quite similar to that of data mining and predictive modeling as all these processes are focused on analyzing data, identifying different patterns and train algorithms on the basis of these recognitions. Machine learning outputs can be observed in our daily life routine such as online shopping through Amazon, Netflix, location-based services, etc.
Data Science can be described as a multi-disciplinary branch of science which implements scientific processes, methods, and algorithms on structured and unstructured data which is helpful for individual and enterprises in decision making. It is a branch of science which deals with the conversion of data into valuable information. To achieve accuracy and perfection in data science, the huge amount of data has to be mined to detect patterns which in return help businesses to increase their efficiency, accessing new market opportunities and rule in costs.
Machine learning engineer Vs data scientists
Assume that data scientists and machine learning engineers are working on the same team. The job of the data scientist is to do statistical analysis of data to identify which machine learning algorithm would be best and modeling of a prototype for testing whereas, the job of a machine learning engineer is to transform that prototype model into a real-life product. It is not mandatory for machine learning engineers to understand predictive modeling and underlying mathematics deeply, he is expected to have deep knowledge and understanding of software tools to transform the prototype into the usable products.
The Job of machine learning engineer
The job of machine learning engineers lies between software engineering and data sciences. Machine learning engineers are experts having tools and programming support to take theoretical data and convert them into a production-level model that is capable of handling TB of data in real-time scenarios.
It is a job of ML engineers to develop programs and processes for computers and robots. The logic behind the development of robots is to design algorithms that enable machines to identify different patterns within their own programming data and enable them to think and make decisions like a human.
The job of data scientist
While businesses encounter complex problems and they need answers to a certain question, they rush towards data scientist who gathers, process and derives valuable information from raw data. Data scientist’s job is to traverse through all business processes when hired by any organization and perform robust analysis by developing programs such as in Java. For businesses to achieve significant success, data scientist uses online experiments and develop customized data products which help organizations in better decision making and customer satisfaction.
While interviewing about the definition of data scientist and machine learning engineer; two technology experts reviews are as below:
According to Mansha Mahtani, a famous data scientist
“Given both professions are relatively new, there tends to be a little bit of fluidity on how you define what a machine learning engineer is and what a data scientist is. My experience has been that machine learning engineers tend to write production-level code. For example, if you were a machine learning engineer creating a product to give recommendations to the user, you’d be actually writing live code that would eventually reach your user. The data scientist would be probably part of that process-maybe helping the machine learning engineer determine what are the features that go into that model but usually, data scientists tend to be a little bit more ad hoc to drive a business decision as opposed to writing production-level code.”
A machine learning engineer Shubhankar Jain at Survey Monkey, said in an interview:
“A data scientist today would primarily be responsible for translating this business problem of, for example, we want to figure out what product we should sell next to our customers if they’ve already bought a product from us and translating that business problem into more of a technical model and being able to then output a model that can take in a certain set of attributes about a customer and then spit out some sort of result. An ML engineer would probably then take that model that this data scientist developed and integrate it in with the rest of the company’s platform and that could involve building, say, an API around this model so that it can be served and consumed, and then being able to maintain the integrity and quality of this model so that it continues to serve really accurate predictions.”