Subject archive for "mlops," page 2

I am fortunate to work with some of the most sophisticated global companies on their AI/ML initiatives. These companies include many household names on the Fortune 500 and come from industries as diverse as insurance, pharmaceuticals, and manufacturing. Each has dozens to literally thousands of data scientists on its payroll. While they have significant investments in AI and ML, they exhibit a surprisingly wide array of maturity when it comes to MLOps.

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates how humans learn by example. While that sounds complex, the basic idea behind deep learning is simple. Deep learning models are taught to classify data from images (such as “cat vs. dog”), sound (“meow vs. bark”), or text (“tabby vs. schnauzer”). These models build a hierarchy where each layer is based on knowledge gained from the preceding layer, and iterations continue until its accuracy goal is reached. Deep learning models often achieve accuracy that rivals what humans can determine, in a fraction of the time.

Today, one of the biggest challenges facing data scientists is taking models from development to production in an efficient and reproducible way. In this way, machine learning (ML) pipelines seek to identify the steps involved in this process. Once the steps are defined, they can be automated and orchestrated, streamlining the data science lifecycle.

Machine Learning Operations (MLOps) is a term that has gained popularity in the last ten years and is often used to describe a set of practices that aims to deploy and maintain machine learning (ML) models in production, reliably and efficiently.

Businesses are increasingly betting big on data science for ambitious near-term growth, just one more indication that the rapidly rising profession is making itself a huge force for innovation in fields as diverse as healthcare & pharma, defense, insurance, and financial services. Nearly half of respondents in a recent poll said that their company’s leadership expects data science efforts to produce double-digit revenue growth. A similar survey in 2021 put that same figure at only 25%, indicating growing expectations for the young profession.

Features are input for machine learning models. The most efficient way to use them across an organization is in a feature store that automates the data transformations, stores them and makes them available for training and inference.