MLOps 101: Scalable Processes for ML Development & Use
MLOps is a term used to encompass the tools, technologies, and practices that allow end-to-end operationalization of Machine Learning (ML) algorithms. To gain consistent benefit at scale from ML models and Artificial Intelligence (AI) applications, ML models have to be effectively managed using repeatable processes. These processes need to cover the entire ML model lifecycle, from creation and training all the way to validation, deployment, and monitoring. Developing sustainable processes around development and use allows you to maximize your ML potential, taking your sophisticated algorithms and ensuring they’re always ready, up to date, and performant.
Don’t just take our word for it! In a recent study, Forrester found that 73% of respondents said MLOps adoption would keep them competitive, while 24% said it would make them an industry leader.
With MLOps, your organization can democratize machine learning, empower a community of data practitioners, and maximize business impact. By making ML efforts more efficient and effective, MLOps allows them to scale so they can meet the largest challenges. It is essential to providing deeper, richer, and more consistent insights with your ML applications. It can be the bridge between the exploratory work of data scientists creating effective algorithms and the real solutions built on those algorithms for end users. MLOps covers many different aspects of AI/ML work, and making progress in each of the six areas will allow you to see benefits even if you don’t fully move to automation right away.
In this eBook, learn how you can turn your creativity and vision for AI/ML into sustainable products and solutions using MLOps and understand how the approach helps you get the most out of your ML/AI applications by addressing eight core areas:
- Why MLOps
- Essential MLOps
- Understanding Business Needs
- Data Governance and Ingestion
- Model Development
- Model Operationalization
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