MLOps | Machine Learning Operations

MLOps | Machine Learning Operations

MLOps | Machine Learning Operations Specialization About Outcomes Courses Testimonials MLOps | Machine Learning Operations Specialization Become a Machine Learning Engineer. Level-up your programming skills with MLOps Taught in English Some content may not be translated 8,244 already enrolled Included with Specialization – 4 course series Get in-depth knowledge of a subject 6 months at

Description

MLOps, short for Machine Learning Operations, is a critical component of the machine learning lifecycle that focuses on the deployment, monitoring, and maintenance of machine learning models in production environments. As organizations increasingly adopt machine learning to drive decision-making processes and gain a competitive edge, the need for a structured approach to managing the machine learning lifecycle has become more apparent.

Machine learning models are developed using algorithms trained on data to make predictions or decisions. However, the real value of these models is only realized when they are deployed and integrated into production systems. This is where MLOps comes into play. It ensures that machine learning models are successfully deployed, monitored, and maintained to deliver reliable and accurate results.

One of the key challenges in machine learning operations is the rapid pace at which the field evolves. New algorithms,

Duke University

MLOps | Machine Learning Operations Specialization

MLOps | Machine Learning Operations Specialization

Become a Machine Learning Engineer. Level-up your programming skills with MLOps

Taught in English

Some content may not be translated

Noah Gift

Alfredo Deza

8,244 already enrolled

Included with Coursera Plus

Specialization – 4 course series

Get in-depth knowledge of a subject


6 months at 5 hours a week

Flexible schedule

Learn at your own pace

What you’ll learn

  • Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.

  • Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.

  • Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.

  • Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

Details to know

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Specialization – 4 course series

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6 months at 5 hours a week

Flexible schedule

Learn at your own pace

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Specialization – 4 course series

What you’ll learn

  • Work with logic in Python, assigning variables and using different data structures.

  • Write, run and debug tests using Pytest to validate your work.

  • Interact with APIs and SDKs to build command-line tools and HTTP APIs to solve and automate Machine Learning problems.

Skills you’ll gain

Category: Python Programming

Category: Information Engineering

Category: Machine Learning

Category: Test Automation

Category: MLOps

What you’ll learn

  • Build operations pipelines using DevOps, DataOps, and MLOps

  • Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)

  • Build and deploy machine learning models in a production environment using MLOps tools and platforms.

Skills you’ll gain

Category: Python Libraries

Category: Big Data

Category: Machine Learning

Category: Devops

Category: Rust Programming

What you’ll learn

  • Apply exploratory data analysis (EDA) techniques to data science problems and datasets.

  • Build machine learning modeling solutions using both AWS and Azure technology.

  • Train and deploy machine learning solutions to a production environment using cloud technology.

Skills you’ll gain

Category: Microsoft Azure

Category: Python Programming

Category: Machine Learning

Category: Amazon Web Services (Amazon AWS)

Category: MLOps

What you’ll learn

  • Create new MLflow projects to create and register models.

  • Use Hugging Face models and datasets to build your own APIs.

  • Package and deploy Hugging Face to the Cloud using automation.

Skills you’ll gain

Category: Modeling

Category: Information Engineering

Category: Cloud Computing

Category: hugging face

Category: Machine Learning Software

Instructors

Noah Gift

Duke University

38 Courses87,284 learners

Offered by

New to Machine Learning? Start here.

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Frequently asked questions

The course series takes approximately 6 months to complete.

You should have basic Python programming experience, familiarity with computer science concepts, and a strong foundation in mathematics (especially linear algebra and statistics).

The course series is designed to be completed in the order outlined here on this Specialization Description Page.

More questions

About the author

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