Machine learning, AI, and data science are buzzwords we’ve been hearing for quite some time now. For good reasons, they’re the hottest career in the industry right now. Even since the pandemic struck the world, there have been furloughs and job losses.
The good news is, there’s still strong demand for AI, machine learning, and data science skills even during the crisis. Thus, if you’re inclined to become an AI engineer or a data science professional this may be the right time to upgrade skills and take your career to a new level.
Over the past decade, terms such as machine learning, data science, or big data have risen in the forefront making people confused. Regardless of their usage, there is a difference between all these terms. But our focus will be on two major job titles or job roles “AI engineer and data scientist.”
Both these terms denote technically skilled professionals whose focus is to majorly build, test, and deploy models while a data scientist’s job is to make complete sense of the data and derive actionable insights out of it.
Let us further define the differences.
Ideally, a data scientist is someone who fetches information from different sources. They come up with certain calculations and predictions about how the data can be of use to the organization in terms of business.
While an AI engineer majorly focuses on how they can help monitor the data in order to help data scientists fix their problems. However, a data scientist needs to first define the value of the project and set expectation goals from the engineers. Once the goals of setting up modeling options the AI professionals will start the prototyping immediately. Further on, the engineers create a minimum viable product and then establish a baseline for the model’s accuracy. While the process is still running, there is certain uncertainty that can be removed provided it slows down the process. Beyond this, organizations can easily use sprints to narrow down their options in modeling.
AI and data science: a powerful combination
Businesses have been shaped around technology making progress at lightning speed. From machine learning to AI and data science, new technologies are creating curiosity waves around the industry. Most commonly discussed topic is that of the AI engineer and data scientist, and how both can serve the industry in a better way.
The core function of an AI professional such as an AI expert is to ensure the data science team is serving its customers and stakeholders by productizing their tasks. And since they’re a part of the organization, they need to work and coordinate with other teams as well – business analysts and data architects, etc.
Precisely, data scientists and AI engineers work hand in hand. However, it is the sole responsibility of the engineer to keep a check on the latest breakthrough in the industry and check how they can bring higher evolution for the company to keep the business intact. In short, these AI ninjas work toward amalgamating software engineering along with data science to help businesses.
Building infrastructure as code requires a keen eye of the AI engineers. They need to create an environment that is transportable and self-contained ensuring the models have been conveniently deployed.
The job role of the AI ninjas and data scientists vary, while the data scientist may pack up and head home later once the job is done and the engineer stays a while longer to make sure the entire system is properly functioning. Once everything seems to look fine, the engineer hands out the system to the operators and gets back home.
Despite sharing different job roles, both AI engineers and data scientists are crucial to the industry.
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