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

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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.


Robust and Scalable ML Lifecycle for a High Performing AI Team

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There is no denying that we are well into the era of Artificial Intelligence, spurred by algorithmic, and computational advances, the availability of the latest algorithms in various software libraries, Cloud technologies, and the desire of companies to unleash insights from the vast amounts of untapped unstructured data lying in their enterprises. While it is clear where we are headed to there seems to be a road blocker that I will address in this blog. Sometimes Perspective is an inspiration, I recently stumbled upon a research paper by Google researchers, titled as Hidden Technical Debt in Machine Learning Systems. It highlights how small ML code is in the software(Big Picture) and how the big parts are often ignored(often due to lack of focus and competencies) leading to technical debt, ineffectiveness and often frustration for organisations. Usually in the production systems, it so happens that it is 20% Machine Learning and 80% is Software Engineering code.


The evolution of machine learning

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Catherine Dong is a summer associate at Bloomberg Beta and will be working at Facebook as a machine learning engineer. Major tech companies have actively reoriented themselves around AI and machine learning: Google is now "AI-first," Uber has ML running through its veins, and internal AI research labs keep popping up. They're pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants, and more. Despite this hype around the state of the art, the state of the practice is less futuristic.


The evolution of machine learning

#artificialintelligence

Catherine Dong is a summer associate at Bloomberg Beta and will be working at Facebook as a machine learning engineer. Major tech companies have actively reoriented themselves around AI and machine learning: Google is now "AI-first," Uber has ML running through its veins and internal AI research labs keep popping up. They're pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants and more. Despite this hype around the state of the art, the state of the practice is less futuristic.


The evolution of machine learning

#artificialintelligence

Major tech companies have actively reoriented themselves around AI and machine learning: Google is now "AI-first," Uber has ML running through its veins and internal AI research labs keep popping up. They're pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants and more. Despite this hype around the state of the art, the state of the practice is less futuristic. Software engineers and data scientists working with machine learning still use many of the same algorithms and engineering tools they did years ago.