Worldwide industries are identifying AI/ML engineers as one of the fastest-growing job titles. Since the trend is likely to last long, this means there are millions of opportunities for the present-day freshers and those planning a career. Here is all you need to know about what AI/ML Engineers are and what they do!
What Is an Artificial Intelligence and Machine Learning Engineer?
Over the past years, the role of an ML engineer has evolved. Typically, they are computer programmers, but their focus extends beyond programming to perform specific tasks that enable machines to take actions without being specifically directed to perform those tasks.
What does a Modern-day AI/ML engineer do?
Bridging Model-Building and Production
The general purpose of an ML engineer is to act as a bridge between the statistical & model-building work of data scientists & to build production-ready & robust AI/ML systems, platforms & services. The AI/ML Engineer use their knowledge of & combine it with programming and software engineering skills to enable easier use of and access to said models and analyses.
They may translate the work of data scientists from environments such as python/R notebooks analytics applications, automating model training & evaluation processes.
Improving Systems
AI/ML engineer is responsible for developing machine learning algorithms to improve systems or processes by automating tasks that otherwise would be physically executed. The job role demands skills in programming, analysis, & an understanding of tools & techniques used to apply AI/ML to real-world tasks. With the rapid increase in the use of ML, more programmers incline Machine Learning & AI courses that educate them in relevant techniques & tools.
Improving Operational Efficiency
Machine learning engineers spend their time doing several things like exploring data, organizing, cleaning, and analyzing data to find patterns & attributes to build machine learning models.
They are a part of a brainstorming team with product managers on customer needs & are expected to come up with new ideas. AI/ML Engineers monitor and fine-tuning ML models to improving team productivity.
Task-Oriented Machine Learning
A machine learning engineer monitors, optimizes, tests, trains, and deploys machine learning algorithms for specific tasks. At some places, ML engineers are expected to implement and carry on more ML-specific transformations, such as outlier detection, dimensionality reduction, feature engineering, missing value imputation, normalization, etc. Once the data is ready for the ML algorithm, the ML engineer is responsible for setting the training algorithm appropriately and executing it in a reasonable time to produce a satisfactory performance.
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