Who is a Deep Learning Engineer?

Data Science, AI and ML
#1

Deep learning engineers perform data engineering, modeling, and deployment responsibilities. This includes the following:

Defining data needs, gathering, categorizing, examining, cleaning, supplementing, and relocating data are all data engineering subtasks.

Training deep learning models, setting evaluation criteria, finding hyperparameters, and reading research articles are examples of modeling subtasks.

Subtasks like converting prototyped code to production code, setting up a cloud infrastructure to deploy the model, or optimizing response times and bandwidth savings are all part of the
deployment process.

Deep learning engineers have a strong scientific and engineering background. Communication abilities are required in different ways by different teams.

In contrast to data scientists, who generally produce prototype code, and software engineers, who write a lot of production code, they write both prototyping and production code.

The most frequently utilized tools discovered in our research are shown below, organized by task.
• Python tools like NumPy, sci-kit-learn, pandas, matplotlib, TensorFlow, and PyTorch are commonly used for modeling.
• Python and/or SQL, as well as other domain-specific query languages, are used for data engineering.
• An object-positioned programming language (such as Python, Java, or C++) and cloud technologies such as AWS, GCP, and Azure are used to deploy.
• A version control system (for example, Git, Subversion, and Brilliant), a “Command Line Interface” (CLI) like “Unix”, an “Integrated Development Environment” (IDE) like “Jupyter Notebook and Sublime, and an issue tracking tool like JIRA are used to manage collaboration and workflow.