LLMOps course
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.
Description for LLMOps course
Features of Course
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
Pricing for LLMOps course
Use Cases for LLMOps course
FAQs for LLMOps course
Reviews for LLMOps course
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for LLMOps course
Learn the principles, advantages, components, and deployment strategies of multi-cloud computing for enhanced resilience, scalability, and adaptability.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Become a machine learning engineer. Enhance your programming abilities with MLOps
Learn to explain Azure Machine Learning Studio's no-code capabilities, fundamental machine learning principles, key development tasks, and common ML categories.
Commence Your Career in Data Science. Apply data science and machine learning to the development and execution of machine learning operations on Azure.
The course outlines techniques for establishing a data science environment on Azure and conducting predictive model training and data experimentation.
Effectively employ Azure ML Studio for predictive model development, experiment establishment, and operationalizing machine learning workflows through drag-and-drop modules.
Learn to use Databricks and MLlib for creating and advancing machine learning models with Spark.
Featured Tools
The primary objective of the course is to emphasize responsible AI and best practices in the development of machine learning models using Vertex AI.
Gain the skills and industry experience needed to lead successful machine learning projects and advance your career in AI.
Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.
Learn to use TensorFlow for computer vision and natural language processing, manage image data, prevent overfitting, and train RNNs, GRUs, and LSTMs on text repositories.
Enhance your master's application, prepare for a software development career, and develop robust programming skills by enrolling in a four-course specialization that includes increasingly intricate applied learning projects.