Machine Learning Engineer vs Data Scientist

Machine Learning Engineer vs Data Scientist

Data Scientists and Machine Learning Engineers are two important professionals in the field of computing. They play an important role in model development. And their role in AI development isn’t that much different but from a technical skills perspective, there’s a difference. Data scientists are sort of a mathematician who can program using his data analysis skills.

While a Machine Learning engineer is more skilled within the programming part and with mastery of related software tools. Their roles are dependant each other and supportive. Machine learning engineers are further down the road than data scientists within the identical project or company. a Data scientist, quite simply, will analyze data and glean insights from the information. While a machine learning engineer will specialize in writing code and deploying machine learning products. Here are some understandings of ” Machine Learning Engineer vs Data Scientist “.

Machine Learning Engineer

Machine learning engineers are responsible for using production-level coding to build the models/projects that data scientists use to quickly analyze raw data. At the academic end, Machine Learning Engineers, are graduated with highly qualified degrees. And require decisive skills with extensive knowledge to perform their task in a professional manner. Skills needed for becoming a Machine Learning Engineer:

  • Computer science fundamentals,
  • Strong Machine Learning Programming skills,
  • Proficient in Python/C++/R/Java,
  • Probability and Statistics Modeling,
  • Natural Language Processing,
  • Understanding of Machine Learning algorithms,
  • Data Modeling and Evaluation skills

Roles and Responsibilities Of a Machine Learning Engineer:

  • Understand and transform the prototypes of Data science,
  • Research, design and Frame Machine Learning systems,
  • Choose and implement the right and best Machine Learning algorithm,
  • Select the right training datasets for Machine Learning model development,
  • Understand business objectives and developing suitable models,
  • Perform Machine Learning model tests and experiments,
  • Perform statistical analysis and also fine-tune the testing results,
  • Verifying data quality, and ensuring it via data cleaning methods,
  • Develop the Machine Learning model as per the requirement.

Data Scientist

Data Scientists are responsible for extracting information and insights from a huge amount of structured and unstructured data. Understanding and translating the meaning behind the data is what makes these individuals valuable for a project. To become a data scientist, there is a need to gain more education as a master or doctorate degree to make your academic skills more strong and gain the capability to analyze and make use of data for deep learning. Data scientists define which models Machine Learning engineers will use to funnel the data. Skills needed for becoming a Data Scientists:

  • Strong statistical and fundamentals,
  • Big Data analysis and interpretation,
  • Data-Driven problem solving,
  • Machine Learning and Deep Learning,
  • Data visualization & communication,
  • Programming languages like R and Python,
  • Unstructured Data management techniques,
  • Use big data tools like Hadoop, Hive, and Pig

Roles and Responsibilities of a Data Scientist:

  • Data source identification and automated collection,
  • Data Mining using State-Of-The-Art methods,
  • Enhance data collection procedure and techniques,
  • Analyze huge Big Data to discover trends and patterns even in complex situations,
  • Create analytical methods and Machine Learning models,
  • Assess the importance of old or new data sources,
  • Evaluate the accuracy of data gathering techniques,
  • Apply and implement the popular Deep Learning frameworks,
  • Data visualization, presentation, and storytelling techniques

All you need to know about Machine Learning

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Future of Machine LearningRole of Machine Learning in Business Growth
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