Team Culture and Cohesion Matters

Between data scientists and machine learning engineers, there is a fundamental overlap of technical skills related to the use and implementation of machine learning technologies.

However, when selecting a team to support the implementation of new AI initiatives across the organization, it is important to ensure that both depth and breadth of skills are present.

The most successful AI initiatives expand internal data science efforts with the support of machine learning engineering teams.

What is Machine Learning Engineering?

Engineering is legally defined as the “application of scientific principles” and can be easily expanded to the “risk-aware application of scientific principles.”

Any major engineering field absorbs the scientific elements researched within a particular topic for the sake of employing that knowledge towards a particular goal or objective. Both data science and machine learning engineering possess a computer science advantage, whereby AI research usually starts off implemented in code. This readily usable format shortens the time required for engineering teams to use new ideas.

Although these experiments may not always be sufficiently mature, they are in a sufficiently tangible format to quickly be implemented and integrated within a larger deployment. This speed to the deployment of new models and data manipulation approaches is why modular design in AI/ML pipelines is primordial.

Distinguishing Between Machine Learning Engineers and Data Scientists

Just as in many other scientific fields, data scientists are closer to both foundational research and cutting-edge changes within the field, whereas machine learning engineers are focused more heavily on the reliability and scalability of operations.

Purpose and Intent

A simple but powerful distinction between investigative data exploration and machine learning engineering is the originating purpose.

A scientific experiment will have a hypothesis, active investigation, and an outcome, whatever the results. The hypothesis may or may not be proven correct, but the work can be deemed complete. In machine learning engineering, the focus is on the application of scientific methods to achieve a reliable and reproducible objective.

Loving the problem—or the solution

Machine learning engineers are motivated by the achievement of an objective or meeting a requirement as opposed to the refinement of a particular solution. Unsupervised, semi-supervised, and supervised methods are combined and blended together to drive the solution forward. Data science, on the other hand, prefers the refinement and elegance of a particular method.

Overall project success and achievement of business outcomes involves a combination of both breadth of applicable solutions and depth of specialization.

Accountability to delivery

Machine learning engineers build AI/ML pipelines for a specific purpose. Whether it be a feasibility assessment, a prototype for a startup, or the refactoring of an enterprise function, projects that are undertaken do not exist in a vacuum.

Why are Engineering Methods Relevant to the Field of AI?

Many core activities in machine learning engineering are heavily inspired by software engineering, which includes the following elements:

  • Requirements engineering. Traceability of goals, objectives, and constraints for project success.
  • Solution architecture. Intent-driven system design that addresses and answers the requirements and constraints defined by the project.
  • Verification and validation. Integrated quality assurance to ensure that AI models are both technically successful and achieve target business outcomes.
  • Ongoing monitoring. Post-deployment continued oversight of the deployed solutions to enter both relevance and accountability (for example, avoiding data drift issues).

Advantages

Using engineering methods for AI projects provides key advantages over open-ended teams:

  • Deployment stage-gating. By separating data science, model design, and deployment activities as distinct stages, project risk is mitigated by flagging issues of deliverables that are not accomplished.
  • Budget control. With every stage having a clear financial structure before the effort takes place, project overruns are lessened by ensuring every traceability.
  • Timeline accountability. Respecting delivery and demo dates is much easier when projects are initiated with clearly defined objectives.

Want to learn more about integrating machine learning engineering in your firm? Send us a message!

Get in touch and stay informed with our newsletter , and why not sign up for updates on our newsletter and LinkedIn.

Want to learn more about integrating machine learning engineering in your firm?

Get in touch and stay informed with our newsletter , and why not sign up for updates on our newsletter and LinkedIn.