MACHINE LEARNING

What machine learning engineer?

Machine learning engineers act as critical members of the?data science?team. Their tasks involve researching, building, and designing the artificial intelligence responsible for machine learning and maintaining and improving existing?artificial intelligence?systems.

Often, a machine learning engineer will also serve as a critical communicator between other data science team members, working directly with the data scientists who develop the models for building AI systems and the people who construct and run them.

While job responsibilities for machine learning engineers will differ, they often include:

• Implementing machine learning algorithms
• Running AI systems experiments and tests
• Designing and developing machine learning systems
• Performing statistical analyses

A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate?predictive models. Machine learning engineers design and create the AI algorithms capable of learning and making predictions that define machine learning (ML).

An ML engineer typically works as part of a larger data science team and will communicate with data scientists, administrators, data analysts,?data engineers and data architects. They may also communicate with people outside of their teams, such as with IT, software development, and sales or web development teams, depending on the organization's size.

ML engineers act as a bridge between data scientists who focus on statistical and model-building work and the construction of machine learning and AI systems.

• The machine learning engineer role needs to assess, analyze and organize large amounts of data, while also executing tests and optimizing machine learning models and algorithms.

Roles and responsibilities of a machine learning engineer

An ML engineer's primary goals are the creation of machine learning models and retraining systems when needed. Responsibilities vary, depending on the organization, but some common responsibilities for this role include:

• Designing ML systems.
• Researching and implementing?ML algorithms?and tools.
• Selecting appropriate data sets.
• Picking appropriate data representation methods.
• Identifying differences in data distribution that affects model performance.
• Verifying?data quality.
• Transforming and converting data science prototypes.
• Performing?statistical analysis.
• Running machine learning tests.
• Using results to improve models.
• Training and retraining systems when needed.
• Extending machine learning libraries.
• Developing machine learning apps according to client requirements.