Ementorhub

Skills you'll gain

Data Management

Artificial Intelligence

Application Frameworks

PyTorch (Machine Learning Library)

Data Security

Scikit Learn (Machine Learning Library)

Data Cleansing

Data Pipelines

Artificial Intelligence and Machine Learning (AI/ML)

Cloud Infrastructure

Machine Learning

Application Deployment

Infrastructure Architecture

Scalability

Tensorflow

MLOps (Machine Learning Operations)

Data Processing

There are 5 modules in this course

By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and their interrelationships. 2. Analyze, describe, and critically discuss efficient data pipelines for AI & ML workflows. 3. Analyze and evaluate model development frameworks for various AI & ML applications. 4. Prepare AI & ML models for deployment in production environments. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.

Data management in AI/ML

Considering and selecting model frameworks

Considerations when deploying platforms

AI/ML concepts in practice