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Machine Learning: Using MLOps to Achieve AI Best Practices
Businesses recognize the potential of Machine Learning, but many aren’t yet prepared to use the technology. The solution is to train and hire engineers who skilled in Machine Learning. This new category of AI-trained engineers are being called MLOps (Machine Learning + Information Technology Operations) engineers. The MLOps engineer is a cross between a data scientist and IT professional.
MLOps engineers develop, configure, deploy, and monitor the machine learning models that businesses use to better forecast and predict trends to help businesses run more efficiently and smoothly. MLOps solutions are expected to grow from $350 million in size in 2019 to $4 billion by 2025.
Brad Shimmin, Omdia chief analyst, said that “you need to have a process, a lifestyle. This is a collaborative sport. You’re not just your data scientists, but you have data engineers, you have business analysts, you have executive sponsors. There are many, many different roles that play a very important part. One of the least understood and the least cared for is IT operations and development, and MLOps is a way to really help with that.”
Kyle Wiggers wrote for VentureBeat that “the advantage of MLOps is that it puts operations teams at the forefront of best practices within an organization. The bottleneck that results from machine learning algorithms eases with a smarter division of expertise and collaboration from operations and data teams, and MLOps tightens that loop.”
