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README.md

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   name: AC215, CSCIE-115
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-**Course Topics Overview** 
+
+# AC215, CSCIE-115
+{:.no_toc}
+
+## Table of contents
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+
+1. TOC
+{:toc}
+
+---
+
+## Course Topics Overview
+
 <span style="color:red">IMPORTANT❗ - Draft more details soon </span>
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 As we journey through these topics, students will gain a holistic perspective, bridging the gap between model development and real-world deployment. With a blend of theory and practical exercises, this course ensures that by the end, you're not just familiar with these concepts, but proficient in applying them.
-​
+​
+
+
+## Course Introduction
+
+In today's AI-driven world, building a robust deep learning model is only half the journey. The real challenge often lies in bringing this model to life in the form of an application that's scalable, maintainable, and ready for real-world deployment. Welcome to AC215: Productionizing AI (MLOps), where we will traverse the complex landscape of Machine Learning Operations. This course has been meticulously curated to provide a holistic understanding of the complete deep learning workflow, from refining your models to deploying them in production environments. By blending the theoretical aspects with hands-on application, we will dive deep into topics like containerization, cloud functions, data pipelines, and advanced training workflows, among others. Our objective is not only to help you grasp these concepts but also to empower you to build and deploy scalable AI applications. Whether you are an AI enthusiast wanting to understand the intricacies of MLOps or a seasoned professional aiming to fortify your knowledge, this course promises a comprehensive exploration of the production side of AI. 
+​
+
+## Prerequisites 
+​
+To ensure a seamless learning experience and to make the most of this course, participants are expected to come with a foundational knowledge in the following areas:
+​
+1. **Programming Proficiency in Python:** 
+   - A strong command over Python's basic constructs, including functions, classes, and modules. Familiarity with libraries like NumPy,  Pandas, Matplotlib is essential, as they form the backbone of many data manipulation tasks in AI.
+​
+2. **Deep Learning Framework - Tensorflow:** 
+   - A working knowledge of the TensorFlow (or PyTorch) framework is crucial, as many topics will delve into its functionalities and methods. Understanding TensorFlow's basic operations, data handling, and model building mechanisms will be invaluable.
+​
+3. **Basic Shell Commands:** 
+   - Comfortability in navigating the command-line interface (CLI), executing shell commands, and performing basic file operations are foundational for many MLOps tasks.
+​
+4. **Basic Data Structures:** 
+   - A good grasp of Python's primary data structures, especially dictionaries and lists, will be instrumental in understanding and manipulating data.
+​
+5. **File I/O:** 
+   - Knowledge of basic file input/output operations in Python, including reading from and writing to files, is vital for tasks involving data storage and manipulation.
+​
+6. **General AI and ML Concepts:** 
+   - While this course is centered around MLOps, a basic understanding of AI and machine learning concepts, including what models are and how they are trained, will set the context for many advanced topics.
+​
+It's important to note that while prior knowledge in these areas will provide a solid foundation, the course has been structured to ensure gradual progression. Even if you're not an expert in all of the prerequisites, a willingness to learn and engage actively in the course's hands-on components will be crucial for success. If you find yourself struggling with some concepts, we encourage leveraging the course resources, attending office hours, and participating in peer discussions to reinforce your understanding.
+​
+
+## Lecture
+
+Date Time 
+
+## Resources
+
+
+
+## Course Components
+​
+- **Weekly Sessions:** Structured lectures focusing on the core topics.
+
+- **Office Hours:** Dedicated time with the instructors and TAs for queries and clarifications.
+
+- **Reading Assignments:** Curated readings to supplement lecture material and deepen understanding.
+
+- **Quizzes:** Short assessments to gauge understanding and reinforce key concepts.
+
+- **Exercises:** Hands-on tasks and challenges designed to provide practical experience.
+
+- **Team Projects:** Collaborative assignments that culminate in the creation of a fully functional AI app.
+
+- **Discussion Forums:** Platforms for peer-to-peer learning, discussions, and knowledge sharing.
+​
+Remember, these components might undergo changes to ensure the best learning experience, so always stay updated with the latest course schedule and announcements.
+
+
+## Course Policies  
+
+
+1. **Getting Help:**
+   - **ED Forum:** For questions related to exercises, course content, or package installations, post on the ED forum first. This promotes peer learning, as your peers might have faced similar issues. All posts are visible to everyone, and the teaching staff monitors them regularly.
+   - **Office Hours:** Should you need more personalized assistance or deeper clarifications, attending office hours is recommended.
+   - **Email:** For private matters or specific concerns, feel free to directly email the instructor.
+​
+2. **Academic Honesty:**
+   - This course places a strong emphasis on ethical behavior. Whether it's ethically handling data or attributing the work of others, students are expected to maintain high standards of integrity.
+   - **Acceptable Behaviors:** Discussing course materials, engaging in office hours, debugging with peers, using and citing small portions of code found online, seeking online knowledge, and seeking guidance from tutors.
+   - **Unacceptable Behaviors:** Accessing or sharing solutions before submission, plagiarizing, not citing sources of external code or techniques, paying or offering payment for coursework, and sharing course material with future potential students.
+   - Engaging in unacceptable behaviors will lead to disciplinary action. When in doubt, always consult the course instructors.
+​
+3. **Deadlines:**
+   - Adhering to deadlines is crucial for a consistent learning experience. All quizzes and exercises are due before the day of the next session. No extensions will be provided, so plan your time effectively.
+​
+4. **Collaboration & Teamwork:**
+   - Collaboration is encouraged, especially for projects. However, ensure you contribute equally and do not divide tasks in a way that prevents you from understanding all parts of the assignment.
+
+5. **Feedback & Evaluation:**
+   - Continuous feedback is vital for the learning process. While the course has several grading components, always focus on understanding rather than just marks. Do provide feedback on the course structure, content, and delivery, so we can continually improve.
+​
+6. **Accessibility & Inclusion:**
+   - We strive to make this course accessible to all. If you have any special needs or require specific accommodations, please reach out to the course administrators at the earliest.
+​
+Students are encouraged to thoroughly understand and abide by these policies. The guiding principle should always be fairness, respect for one's own learning journey, and respect for the learning journey of peers.