Current State of AI
How MLOps alleviates challenges faced in AI implementation
AI Model Lifecycle
Introduction to ML Platforms
Requirements
- No programming experience needed. You will learn everything you need to know.
Description
AI is no longer exclusively for digitally native companies like Amazon, Netflix, or Uber. Unsurprisingly, Gartner predicts that more than 75% of organizations will shift from piloting AI technologies to operationalizing them by the end of 2024 — which is where the real challenges begin. Unfortunately, scaling AI in this sense isn’t easy. There is a chasm between ML and MLOps that can be tricky to scale. Getting one or two AI models into production is different from running an entire enterprise or product on AI. And as AI is scaled, problems can (and often do) scale, too.
Organizations that are serious about AI have to adopt a new discipline, “MLOps” or Machine Learning Operations. MLOps is the bridge. It is an engineering culture and practice that aims to unify ML system development and operations to facilitate data processing, machine learning pipeline, model training, experimentation, evaluation, registry, deployment, monitoring, serving, and scaling. Essentially, MLOps refers to a set of practices that helps in deploying and maintaining machine learning models in production efficiently and reliably. It is a collaborative team function often comprising of data scientists and DevOps engineers.
In this course, you will learn:
- The building blocks of MLOps
- The best practices and tools that facilitate rapid, safe, and efficient development and operationalization of AI
Who this course is for:
- Aspiring MLOps Professionals and Enthusiasts
- Individuals interested in data and AI industry
Course content
