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Reading the Tea Leaf From the First Ever MLOps Conference
The trends of machine learning
Rev 3 took place on May 5th — 6th at the Marquis Marriott in New York, NY. It was the number one MLOps conference on the planet, and the audience was data scientists and IT leaders. Experts gathered together and shared strategic and practical insights on best practices in MLOps to drive progress within their organizations.
MLOps standards for Machine Learning Operations. It is an end-to-end MLOps lifecycle that collects data, trains models, and builds applications. We attended the conference and learned a number of trends from the current AL/ML development, including the following:
- The terms AI and ML are used interchangeably.
- Machine learning makes objective decisions.
- There is a need to streamline the end-to-end MLOps lifecycle on one platform.
- Technology advancement must follow AI regulations.
The Terms AI and ML Are Used Interchangeably
Artificial intelligence (AI) is used to describe machines that mimic and display human cognitive skills that are associated with human minds, such as learning and problem-solving. AI makes decisions based on the information/data collected.
Machine learning (ML) is a part of artificial intelligence. It is the study of computer algorithms that can automatically improve through experience and by using data.
In 1959, Arthur Samuel, a pioneer in computer gaming and artificial intelligence, defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.
Strictly speaking, AI is the superset of ML.
In reality, we heard most of the speakers using the terms AL and ML interchangeably. After all, both of them refer to machines’ decision skills.
