MACHINE LEARNING | 5 mins read

Data engineer vs Machine learning engineer vs Data science

By Kant on 29 May 2020
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From the last article, we have discussed a difference between Data analytics and Data sciences. But, how's about Machine learning engineer and Data Engineer. What does ML engineer and Data Engineer exactly do? Let's find it out with us.

 

 

 

 

There’s some confusion surrounding the roles of machine learning engineer vs. data scientist, primarily because they are both relatively new. However, if you parse things out and examine the semantics, the distinctions become clear.

At a high level, we’re talking about scientists and engineers. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something.

 

What Does a Machine Learning Engineering do?

Machine learning engineers sit at the intersection of software engineering and data science. They leverage big data tools and programming frameworks to ensure that the raw data gathered from data pipelines are redefined as data science models that are ready to scale as needed.

Machine learning engineers feed data into models defined by data scientists. They’re also responsible for taking theoretical data science models and helping scale them out to production-level models that can handle terabytes of real-time data.

Machine learning engineers also build programs that control computers and robots. The algorithms developed by machine learning engineers enable a machine to identify patterns in its own programming data and teach itself to understand commands and even think for itself.

 

What Are the Requirements for a Machine Learning Engineer?

To work as a machine learning engineer, most companies prefer candidates who have a master’s degree in computer science. However, as this field is relatively new and there is a shortage of top tech talent, many employers will be willing to make exceptions.

According to a report by IBM, machine learning engineers should know the following programming languages
(as listed by rank) :

  • Python
  • Java
  • R
  • C++
  • C
  • JavaScript
  • Scala
  • Julia

 

What Are the Responsibilities of a Machine Learning Engineer?

Here’s what these roles typically demand:

  • Develop machine learning models
  • Collaborate with data engineers to develop data and model pipelines
  • Apply machine learning and data science techniques and design distributed systems
  • Write production-level code
  • Bring code to production
  • Engage in code reviews
  • Improve existing machine learning models
  • Be in charge of the entire lifecycle (research, design, experimentation, development. deployment, monitoring, and maintenance)
  • Produce project outcomes and isolate issues
  • Implement machine learning algorithms and libraries
  • Communicate complex processes to business leaders
  • Analyze large and complex data sets to derive valuable insights
  • Research and implement best practices to enhance existing machine learning infrastructure

 

 

 

 

With an increase in Big data analysts and machine learning the demand for data engineer are higher than ever. Data Engineer works with data architect and software developer. A data Engineer possesses a deep level of technical and software skills.

 

What Does a Data Engineering do?

  • Create and maintain optimal data pipeline architecture.
  • Building a system that is optimum in data delivery, and has greater scalability.
  • Build the infrastructure required for optimal extraction, transformation, and loading of data.
  • Create data tools to help Data Analysts and Data engineers for building and optimizing the products.

 

What Are the Requirements for a Data Engineer?

  • Data models
  • Comparative analysis of data stores
  • Logical operations
  • Data modeling techniques
  • Database clustering tools and techniques

 

What Are the Responsibilities of a Data Engineer?

  • Create and maintain optimal data pipeline architecture.
  • Building a system that is optimum in data delivery, and has greater scalability.
  • Build the infrastructure required for optimal extraction, transformation, and loading of data.
  • Create data tools to help Data Analysts and Data engineers for building and optimizing the products.

 

Let’s summarize 

 

 

  • Data scientist vs. machine learning engineer: do they need a degree? 
    • Most employers would prefer an advanced degree, but to meet demand, they will be open to hiring those who have the right skills and experience.
  • Data scientist vs. machine learning engineer: what do they actually do? 
    • While there’s some overlap, which is why some data scientists with software engineering backgrounds move into machine learning engineer roles, data scientists focus on analyzing data, providing business insights, and prototyping models, while machine learning engineers focus on coding and deploying complex, large-scale machine learning products.
  • Data scientist vs. machine learning engineer: who makes more? 
    • At present, machine learning engineers make more, but the data scientist role is a much broader one, so there is a wide variety of salaries depending on the specifics of the job.

 

Source: "Data Science vs. Machine learning engineer"  By Andrew Zola, "Data Scientist vs Data Analysis vs ML Engineer: Which job is most suited for you ?" By Swaastick Kumar Singh

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