LLMOps course

(0 reviews)

Master the operations of large language models. Acquire proficiency in the deployment, management, and optimization of extensive language models on a variety of platforms, such as Azure, AWS, Databricks, local infrastructure, and open source solutions, through practical projects.

Key AI Functions:Azure,Databricks

Description

Reviews

Alternative Tools

Description for LLMOps course

Gain mastery in Large Language Models (LLMs) through a comprehensive Coursera and Duke University specialization, covering open-source LLM management and generative AI techniques on platforms like Azure, AWS, Databricks, and local infrastructure.

Acquire practical experience in designing, deploying, and scaling robust language models for diverse applications via immersive projects and industry best practices.

Build a portfolio demonstrating LLM management skills by tackling real-world challenges, preparing for roles such as Machine Learning Engineer, DevOps Engineer, Cloud Architect, AI Infrastructure Specialist, or LLMOps Consultant.

Engage in over 20 hands-on coding projects, including deploying LLMs on Azure and AWS, creating prompts with LLM frameworks, running local models using external APIs, and building chatbots with vector databases, to gain authentic, portfolio-ready experience.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 21

Offered by: On Coursera provided by Duke University

Duration: 5 months at 10 hours a week

Schedule: Flexible

Reviews for LLMOps course

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

ML Use Cases in Finance

Self-Driving Cars Specialization

NLP: Twitter Sentiment Analysis

Deep Learning for Computer Vision Specialization

An Intuitive Intro to Probability

Alternative Tools for LLMOps course

Foundation to Multi-Cloud

Learn the principles, advantages, components, and deployment strategies of multi-cloud computing for enhanced resilience, scalability, and adaptability.

#Multi Cloud networking #K8s architecture

Automated Reasoning with GPT Assistant API: ReAct Agents

Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.

#Artificial Intelligence (AI) #Python Programming

Operationalizing LLMs on Azure

Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.

#Artificial Intelligence (AI) #Python Programming

MLOps Specialization

Become a machine learning engineer. Enhance your programming abilities with MLOps

#Microsoft Azure #Big Data

Microsoft Azure ML

Learn to explain Azure Machine Learning Studio's no-code capabilities, fundamental machine learning principles, key development tasks, and common ML categories.

#Artificial Intelligence (AI) #Microsoft Azure

Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate

Commence Your Career in Data Science. Apply data science and machine learning to the development and execution of machine learning operations on Azure.

#Manage Azure resources for machine learning #learning solutions

Creating ML Models in Microsoft Azure

The course outlines techniques for establishing a data science environment on Azure and conducting predictive model training and data experimentation.

#Modeling #Microsoft Azure

Predictive Modelling with Azure Machine Learning Studio

Effectively employ Azure ML Studio for predictive model development, experiment establishment, and operationalizing machine learning workflows through drag-and-drop modules.

#Artificial Intelligence (AI) #Microsoft Azure