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Careers in Machine Learning: Present and Future

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Brief Description:: Artificial Intelligence was, for several decades, an almost exclusive domain of academic research. In the past decade, however, Machine Learning (ML) techniques have finally achieved sufficient effectiveness and practicality for large-scale adoption in companies and institutions. This adoption, however, is still incipient: most organizations are in the early stages of understanding and proficiency in these technologies. Machine learning playing important role.

Applications and Market

Today, there are already many practical ML applications being used commercially. Some of the most common are:

  • Customer profile analysis, for example to make decisions about granting credit or prioritize marketing actions.
  • Recommendation systems for products, services and content.
  • Fraud detection systems.
  • Demand forecasting and logistics optimization.
  • Natural language analysis for routing or automatic response (chatbots).
  • Image analysis for object and face detection.
  • Text recognition and document structure extraction from images.
  • Speech to text transcription.
  • Translation between languages.
  • Screening for medical imaging exams.

Some of the markets that use ML most today are online services, marketing and financial services, but there’s potential in all industries. The biggest bottleneck for more universal adoption today is not in the capacity of algorithms or systems, but in identifying and modelling problems, gathering the data needed for training, and integrating the models with systems in production.

Roles and Careers
  • Data Analyst
  • Data Scientist
  • ML/MLOps Engineer
  • Analyst
  • ML Researcher
What should I study?
  • Computer Science and Software Engineering – Programming
  • Statistics
  • Mathematics
  • Domain-Specific Knowledge