DevOps, DataOps, MLOps

(0 reviews)

It pertains to the development of operations pipelines that employ the principles and practices of DevOps, DataOps, and MLOps for the development and deployment of models.

Key AI Functions:Python Libraries, Big Data, Machine Learning, Devops, Rust Programming

Description

Reviews

Alternative Tools

Description for DevOps, DataOps, MLOps

Develop operations pipelines by utilizing DevOps, DataOps, and MLOps.

Describe the principles and practices of MLOps, including continuous integration and delivery, model training and development, and data management.

Utilize MLOps tools and platforms to develop and deploy machine learning models in a production environment.

Level: Advanced

Certification Degree: Yes

Languages the Course is Available: 21

Offered by: On Coursera provided by Duke University

Duration: 44 hours (approximately)

Schedule: Flexible

Reviews for DevOps, DataOps, MLOps

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Teaching and Learning in the Era of AI

Higher education assessing in the age of AI

Assessment Design with AI

MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure ML

AI Prompt Engineering for Beginners

Alternative Tools for DevOps, DataOps, MLOps

ML Use Cases in Finance

Learn to apply advanced machine learning and deep learning models to real-world challenges by immersing yourself in the cutting-edge world of AI-powered finance and insurance.

#bitcoin #financial services

Rust (LLMOps)

Learn proficiency in the construction, deployment, and safeguarding of large language models at scale, utilizing Rust, Amazon Web Services (AWS), and established DevOps best practices.

#llmops #devops

MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services

Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.

#software versioning #operations

Higher education assessing in the age of AI

Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.

#artificial intelligence #education

The Ethics of AI

Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.

#artificial intelligence #machine learning

Rust for DevOps

Discover how to use Rust to apply DevOps ideas, automate system chores, and put logging and monitoring in place for effective application deployment and operation.

#devops #rust

Applications of TinyML

Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.

#artificial neural networks #smartphone operation

MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure ML

The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.

#machine learning #data engineering

Data Science with Python: Introduction

In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.

#scientific methods #data science

AI Tailored to Kids

A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.

#artificial intelligence #data science