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Want a Career in Machine Learning? Here's What You Need to Know.


Artificial intelligence was almost exclusively the domain of academic research for decades. In the past ten years, however, machine learning (ML) techniques have finally achieved sufficient effectiveness and practicality for large-scale adoption in companies and institutions. This adoption, however, remains incipient. Most organizations are still in the early stages gaining proficiency in these technologies and growing them at enterprise scale. The potential for professionals in this area, therefore, is enormous, evinced by the steady increase in ML job openings and courses. Given the proliferation of ML jobs postings out there, what are the roles and positions in the field?

How Can You Build a Career in Data Science & Machine Learning?


Machine Learning is the crux of Artificial Intelligence. With increasing developments in AI, IoT and other smart technologies, machine learning jobs are gaining higher exposure and demand in the technology market. If you are currently an IT professional, you might be interested in a career switch because of the exciting opportunities the industry offers to its aspirants. Or, you might have an interest that you have wanted to pursue long. However, not knowing exactly how to start a career in machine learning can lead an aspirant in the wrong way. There should be a proper agenda on how to identify the right opportunity and approach it in a systematic way. In this article, let us see some of the essential steps that one can take towards their machine learning journey.

Data Scientist, Data Engineer & Other Data Careers, Explained - KDnuggets


The data-related career landscape can be confusing, not only to newcomers, but also to those who have spent time working within the field. Get in where you fit in. Focusing on newcomers, however, I find from requests that I receive from those interested in join the data field in some capacity that there is often (and rightly) a general lack of understanding of what it is one needs to know in order to decide where it is that they fit in. In this article, we will have a look at five distinct data career archetypes, and hopefully provide some advice on how to get one's feet wet in this vast, convoluted field. We will focus solely on industry roles, as opposed to those in research, as not to add an additional layer of complication.

Career Comparison: Machine Learning Engineer vs. Data Scientist--Who Does What? - Springboard Blog


There's some confusion surrounding the roles of machine learning engineer vs. data scientist, primarily because they are both relatively new. However, if you parse things out and examine the semantics, the distinctions become clear. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something. But before we go any further, let's address the difference between machine learning and data science. It starts with having a solid definition of artificial intelligence.

Rethinking AI talent strategy as automated machine learning comes of age


In recent years, as the promise of artificial intelligence (AI) crystallized across industries, organizations revamped their talent strategies to gain the skills necessary to deploy and scale AI systems. They hired legions of data scientists and other data experts to build AI applications, trained analytics translators to connect the business and technical realms, and upskilled frontline staff to use AI applications effectively. One role in particular, the data scientist, has been especially difficult for leaders to fill as competition for its illusive knowledge increased. McKinsey Global Institute research has also highlighted the talent shortage and the potential for hundreds of thousands of positions to go unfilled. Incumbent companies found it especially hard to compete with start-ups and tech giants such as Google to attract or retain the best practicing data scientists and the newest crop of graduates.