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Unlocking the Power of AI with Implemented Machine Learning Ops Projects

Becoming Human

Machine learning operations, or MLOps, are the set of practices and tools that aim to streamline and automate the machine learning lifecycle. It covers everything from data preparation and model training to deployment, monitoring, and maintenance. What are MLOps Projects?

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

Big Data 106
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Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy

AWS Machine Learning Blog

In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving data preparation and feature engineering. Custom transforms can be written as separate steps within Data Wrangler.

AWS 94
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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

Although machine learning (ML) can provide valuable insights, ML experts were needed to build customer churn prediction models until the introduction of Amazon SageMaker Canvas. Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building.

ML 90