What is “AI Engineering“
"AI Engineering" refers to the discipline of designing, building, and managing AI applications by leveraging foundation models like GPT-4O and Llama 3.1. The key categories within AI Engineering include:
AI Model Adaptation: Selecting the right AI model and tuning it for your specific use case. Common adaptation techniques include in-context learning, retrieval-augmented generation (RAG), parameter-efficient fine-tuning, and instruction fine-tuning.
AI System Design: Focusing on the typical system design while considering factors such as foundation model costs, rate limits, inference latency, and data frameworks.
AI Application Development: Applying software engineering practices while addressing the uncertainties introduced by AI models.
AI DevOps: Ensuring the security of AI model access, data privacy, and prompt management.
What to expect from this Newsletter
"The AI Engineering" newsletter is a comprehensive resource focused on the field of AI Engineering. It covers:
AI Model Selection and Adaptation
AI System Design and Development
Managing AI Applications
The content is categorized into three main areas:
Knowledge: Fundamental concepts required for designing, developing, and managing AI applications.
Skill: Hands-on examples and how-to guides for AI applications.
Insight: Practical insights derived from real-world implementations.
What NOT to expect from this Newsletter
This newsletter does not cover topics best handled by experts in those fields, such as core ML Engineering, AI model pre-training, or developing models from scratch.
Who can benefit from this Newsletter
"The AI Engineering" is designed for a diverse audience united by a common interest in AI's potential:
AI Engineers: Software engineers involved in building AI applications, including front-end and back-end developers, QA specialists, and DevOps professionals.
AI Engineering Leaders: Senior executives driving AI engineering projects.
About Me
My name is Selva, and I bring to you over 15 years of rich and diverse experience in the software industry, ready to guide you through the fascinating world of AI Large Language Models in "The AI Engineering". I hold a Master's degree in Computer Engineering from North Carolina State University, a testament to my strong academic foundation in the field.
My career journey has seen me wear many hats - a software developer specializing in cloud infrastructure, an entrepreneur who founded and ran a SaaS company for five years, and currently, a Senior Technical Architect at an AI-based SaaS company. These varied roles have provided me with a holistic understanding of the tech industry and the impact of AI on businesses.
As an entrepreneur, I navigated the challenges of building a company from the ground up, gaining invaluable insights into the market needs, customer behavior, and business strategies. Although I had to shut down the company, the experience was a treasure trove of learning that has shaped my understanding of the practical implications of AI.
In my current role, I spearhead AI initiatives, applying my knowledge and skills to drive innovation. My work involves both technical and strategic decision-making, positioning me at the intersection of technology and business.
I have also expanded my knowledge by completing various AI certifications from esteemed platforms like Coursera, edX and Google. This continuous learning process keeps me abreast of the latest advancements in AI, a crucial aspect in this rapidly evolving field.
With "The AI Engineering", I am excited to share my insights, experiences, and knowledge with you. I am confident that this blend of practical industry experience, academic rigor, and continuous learning will make the content I deliver valuable and actionable for you. Join me as we explore the fascinating world of AI Large Language Models together!
