Strategic AI Engineering And Ethics

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Free Download Strategic AI Engineering And Ethics
Published 1/2026
Created by Eric Yeboah
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 30 Lectures ( 2h 38m ) | Size: 4.11 GB

Steps to developing artificial intelligence system, AI driven system design, How to develop AI software etc.
What you'll learn
Steps to developing artificial intelligence system
Artificial iintelligence deployment guide
How to build and deploy AI/ML systems
Artificial intelligence Driven System Design
How to make your own artificial intelligence
How to develop artificial intelligence software
Artificial intelligence principles
Requirements
Desire to learn more about artificial intelligence
No special requirement
Description
Artificial intellligence engineering is a technical discipline that focuses on the design, development, and deployment of artificial intelligence systems. Arificial intelligence engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions. Data serves as the cornerstone of AI systems, necessitating careful engineering to ensure premium quality wide spread availability, and usability. AI engineers gather large diverse dataset from multiple sources such as databases, APIs and real-time streams. This data undergoes cleaning, normalization, and preprocessing, often facilitated by automated data pipelines that manage extraction, transformation and loading processes.. An AI engineers workload revolves around the AI systems lifecycle, which is a complex, multi-stage process. This process may involves building models from scratch or using pre-existing models through transfers learning, depending on the projects requirement. Once the model is trained, it must be integrated into the broader system, a phase that largely remains the same regardless of how the model was develop. System interaction involves connecting the AI model to various software components and ensuring that it can interact with external system, databases, and user interfaces. The deployment stage typically involves the same overarching strategies- whether the model is built from scratch or based on an existing model. However models built from scratch may require more extensive fine-tuning during deployment to ensure they meet performance requirements in a production environment.
Who this course is for
Companies, AI professionals, managers, consultants, directors, students, government, CEO, institutions, universities, general public etc.
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