Hire a Machine Learning Engineer, not a Data Scientist

#mlengineer #datascientist

One of the most important technological developments in recent decades, across all industries, is artificial intelligence. It includes anything from straightforward predictive modelling to trickier projects like chatbots and self-driving cars. It has demonstrated its value not only in the field of research but also in helping businesses achieve their objectives. This includes anything from boosting customer retention rates to generating more insightful big data analysis and even supporting automated customer service agents.

According to a recent analysis by Research and Markets, the global machine learning market is expected to increase from $1.4 billion in 2017 to $8.8 billion by 2022. As a result, the need in the market for a reliable artificial intelligence team has skyrocketed. Even as artificial intelligence itself continues to advance quickly, so do the jobs inside an AI team. The rising position of machine learning engineer is required within an AI team, according to Gartner.

What does a machine learning engineer actually do? How does that compare to a data scientist? How is it arranged within an AI team? Here, I'd want to talk about some of my recent work as a machine learning engineer.

Data scientists had to learn machine learning on their own because managers did not have a common concept of what it is. This was a grave error, as effective ML programmes necessitate a close feedback loop between domain and data expertise.

Two distinct machine learning skill sets exist: those of AI researchers who create new algorithms. And ML engineers that create AI commercial applications using algorithms.

In this example, you need cooks (ML Engineers) to prepare food, not electrical engineers to create equipment (AI Researchers).

A skilled cook is required when making bread, not an electrical engineer with expertise in oven building.

It is increasingly becoming evident that you don't require specialists (statisticians) to develop machine learning solutions, which many businesses first refused to believe. You need generalists, which are skilled engineers who are also outstanding communicators and are familiar with AI.

An expert with a PhD in statistics, mathematics, or computer science is not required to complete it. Like front-end development, databases, or scalable cloud deployments, it's merely a tool that any motivated developer can learn to use. It's a skill, not a subject of study.

Does AI + engineering + business knowledge equal the mythical unicorn??Not exactly. The subject of machine learning is current. Many engineers are proficient in it already, and much more desire to enter the field. The more difficult task is finding excellent communicators.

What qualifications must a machine learning engineer have?

1. Strong development background: A lack of good engineering practices was the issue that ruined the majority of AI efforts. Your machine learning engineer should be able to develop, improve, and maintain commercial applications.

2. Fundamentals of machine learning: An experienced, motivated engineer can learn to develop AI applications in a few months. A data scientist would need considerably more time to develop into a competent engineer (2–4 years on average).

This implies that you can expand your own machine learning team if you have the necessary time and engineers. No one must be hired.

3. Effective communication: Because AI is new and overhyped, many managers are still getting to know it. Additionally, it is necessary to successfully and clearly communicate the results to the management and clients.