Final Project + Giveaways
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As we approach the conclusion of this bootcamp, it's time to transform your acquired knowledge into practical applications. The current deadline for submitting the project is 15th May 2024.
With a few weekends ahead in this schedule, you have a valuable yet concise time frame to create and showcase a meaningful project. Make the most of it!
To guide you, we have selected a range of ideas/tracks (which are optional) but you can use them for ideation if needed. However, we encourage you to go beyond and think of a few ideas by looking at the problems around you, as that's one of the better approaches to problem solving. 🌟
You should also explore the resources listed under prerequisites so the hands-on module is easier for you to finish. If you're done with that as well, you could share your learning journey with us and the world out there; learning in public comes with a dozen advantages anyway.
Now, let's quickly revisit the mandatory requirements for completing the bootcamp.
1 – Complete the Quizzes
Ensure you complete the required quizzes: one in the and another in the .
2 – Project Development
Task: Develop a real-time or static RAG-based LLM application completely using or / .
Publish: Publish your open-source project on your GitHub with a clear README that includes a video demo. We emphasize this as it makes it easy for course instructors, developers in the community, or your potential employers to evaluate what you've built
Submission: Submit the project link through the form provided.
3 – Project Guidelines
Option to Modify an Existing Project: If building an LLM application from scratch seems daunting, consider modifying the "Dropbox Retrieval App" example we discussed. Adapt it to create an application with significant business or social value. For inspiration, look at how for a better comprehension of the EU AI Act. This being said a direct replica of any published project will not be accepted.
Project Requirements:
Data Source: Your project should use real-time (preferred) or static data sources.
Open Source: Ensure your project is open source, hosted on GitHub with a clear README.md file and a License file as a best practice. Ref: / .
Documentation: The README.md must include:
A demo video link or GIF for a quick overview of your application.
A clear description explaining the purpose of your project and how it utilizes Pathway, Langchain, LlamaIndex, Ollama (sample ), etc.
Instructions for setting up and running the tool.
Originality: Your project must be original, not plagiarized, and not a direct replica of any course materials, publicly available projects, or those submitted by peers.
Bonus: If you publish your project as a tutorial on any popular developer publication (e.g. Freecodecamp, Dev.to, GFG, KDNuggets, Towards Data Science, etc.) it becomes significant proof of clear documentation and implementation for the team at Pathway and your future collaborators/employers. However, at times it may take additional efforts (simply copy-pasted Gen AI articles also need refinement) so it's not a mandatory thing. But its importance cannot be overstated.
While using the Dropbox App as a foundation is acceptable, we encourage you to innovate and create something unique. Challenge yourself to develop a project that tests your cognitive abilities and engineering skills.
If the idea of creating an LLM application from the ground up (like the one we saw in the Amazon Discounts case) feels overwhelming, you have the option to build upon the "Dropbox Retrieval App" example discussed earlier. By tailoring it to meet specific needs, you can construct an application that holds substantial business or social value.
What are additional incentives beyond learning for building a novel application? Let's see