Data Engineer vs ML Scientist: AI Roles Explained

In this video, I break down the complete AI Data Pipeline and explain the critical roles that make Artificial Intelligence possible. From the initial data source to the final deployed model, you'll learn exactly how Data Engineers, Data Scientists, and Machine Learning Engineers collaborate to build AI systems.
We cover the entire lifecycle: ingesting raw data from databases (like Snowflake or Kafka), processing it through ETL pipelines, performing Exploratory Data Analysis (EDA), and finally training and deploying models. Whether you are looking to start a career in AI or just want to understand the workflow, this guide clarifies who does what in the industry.
Timestamps:
0:00 - Introduction to AI Data Flow
0:51 - Role 1: Data Engineer (Building Pipelines & ETL)
2:03 - Role 2: Data Scientist (Analysis & Cleaning)
3:31 - Role 3: Machine Learning Engineer (Model Training)
4:22 - Role 4: Prompt Engineer (GenAI & LLMs)
4:53 - Role 5: LLM Engineer & MLOps (Fine-tuning & Deployment)
5:36 - Cloud AI Providers (AWS, Azure, GCP)
Key Topics:

The difference between Data Engineering vs Data Science
Real-world AI workflow from source data to model inference
Emerging roles: Prompt Engineering and LLM Operations
Cloud platforms for AI: AWS, Azure, and Google Cloud

#DataEngineering #MachineLearning #DataScience #AICareer #MLOps #ArtificialIntelligence

Видео Data Engineer vs ML Scientist: AI Roles Explained канала Neuneworks