7 AI Portfolio Initiatives to Increase the Resume

I really consider that to get employed within the subject of synthetic intelligence, it’s essential have a powerful portfolio. This implies it’s essential present the recruiters that you could construct AI fashions and functions that remedy real-world issues.

On this weblog, we are going to evaluate 7 AI portfolio tasks that may enhance your resume. These tasks include tutorials, supply code, and different supportive supplies that can assist you construct correct AI functions.

1. Construct and Deploy your Machine Studying Software in 5 Minutes

Venture hyperlink: Construct AI Chatbot in 5 Minutes with Hugging Face and Gradio

Screenshot from the venture

On this venture, you’ll be constructing a chatbot utility and deploying it on Hugging Face areas. It’s a beginner-friendly AI venture that requires minimal data of language fashions and Python. First, you’ll be taught numerous parts of the Gradio Python library to construct a chatbot utility, after which you’ll use the Hugging Face ecosystem to load the mannequin and deploy it. 

It’s that easy.

2. Construct AI Initiatives utilizing DuckDB: SQL Question Engine

Venture hyperlink: DuckDB Tutorial: Constructing AI Initiatives

Screenshot from the venture

On this venture, you’ll be taught to make use of DuckDB as a vector database for an RAG utility and in addition as an SQL question engine utilizing the LlamaIndex framework. The question will take pure language enter, convert it into SQL, and show the lead to pure language. It’s a easy and easy venture for learners, however earlier than you dive into constructing the AI utility, it’s essential be taught a number of fundamentals of the DuckDB Python API and the LlamaIndex framework.

3. Constructing A number of-step AI Agent utilizing the LangChain and Cohere API

Venture hyperlink: Cohere Command R+: A Full Step-by-Step Tutorial

Screenshot from the venture

Cohere API is best than OpenAI API  when it comes to performance for growing AI functions. On this venture, we are going to discover the assorted options of Cohere API and be taught to create a multi-step AI agent utilizing the LangChain ecosystem and the Command R+ mannequin. This AI utility will take the person’s question, search the net utilizing the Tavily API, generate Python code, execute the code utilizing Python REPL, after which return the visualization requested by the person. That is an intermediate-level venture for people with primary data and taken with constructing superior AI functions utilizing the LangChain framework.

4. Advantageous-Tuning Llama 3 and Utilizing It Domestically

Venture hyperlink: Advantageous-Tuning Llama 3 and Utilizing It Domestically: A Step-by-Step Information | DataCamp

Picture from the venture

A preferred venture on DataCamp that may provide help to fine-tune any mannequin utilizing free assets and convert the mannequin to Llama.cpp format in order that it may be used regionally in your laptop computer with out the web. You’ll first be taught to fine-tune the Llama-3 mannequin on a medical dataset, then merge the adapter with the bottom mannequin and push the total mannequin to the Hugging Face Hub. After that, convert the mannequin information into the Llama.cpp GGUF format, quantize the GGUF mannequin and push the file to Hugging Face Hub. Lastly, use the fine-tuned mannequin regionally with the Jan utility.

5. Multilingual Automated Speech Recognition

Mannequin Repository: kingabzpro/wav2vec2-large-xls-r-300m-Urdu

Code Repository: kingabzpro/Urdu-ASR-SOTA

Tutorial Hyperlink: Advantageous-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers

Screenshot from kingabzpro/wav2vec2-large-xls-r-300m-Urdu

My hottest venture ever! It will get nearly half one million downloads each month. I fine-tuned the Wave2Vec2 Massive mannequin on an Urdu dataset utilizing the Transformer library. After that, I improved the outcomes of the generated output by integrating the language mannequin.

Screenshot from Urdu ASR SOTA – a Hugging Face Area by kingabzpro

On this venture, you’ll fine-tune a speech recognition mannequin in your most well-liked language and combine it with a language mannequin to enhance its efficiency. After that, you’ll use Gradio to construct an AI utility and deploy it to the Hugging Face server. Advantageous-tuning is a difficult process that requires studying the fundamentals, cleansing the audio and textual content dataset, and optimizing the mannequin coaching.

6. Constructing CI/CD Workflows for Machine Studying Operations

Venture hyperlink: A Newbie’s Information to CI/CD for Machine Studying | DataCamp

Picture from the venture

One other standard venture on GitHub. It includes constructing a CI/CD pipeline or machine studying operations. On this venture, you’ll find out about machine studying venture templates and the way to automate the processes of mannequin coaching, analysis, and deployment. You’ll find out about MakeFile, GitHub Actions, Gradio, Hugging Face, GitHub secrets and techniques, CML actions, and numerous Git operations. 

In the end, you’ll construct end-to-end machine studying pipelines that may run when new knowledge is pushed or code is up to date. It should use new knowledge to retrain the mannequin, generate mannequin evaluations, pull the educated mannequin, and deploy it on the server. It’s a absolutely automated system that generates logs at each step.

7. Advantageous-tuning Secure Diffusion XL with DreamBooth and LoRA

Venture hyperlink: Advantageous-tuning Secure Diffusion XL with DreamBooth and LoRA | DataCamp

Picture from the venture 

We’ve realized about fine-tuning massive language fashions, however now we are going to fine-tune a Generative AI mannequin utilizing private images. Advantageous-tuning Secure Diffusion XL requires only some pictures and, in consequence, you will get optimum outcomes, as proven above.

On this venture, you’ll first find out about Secure Diffusion XL after which fine-tune it on a brand new dataset utilizing Hugging Face AutoTrain Advance, DreamBooth, and LoRA. You possibly can both use Kaggle at no cost GPUs or Google Colab. It comes with a information that can assist you each step of the way in which.

Conclusion

All the tasks talked about on this weblog had been constructed by me. I made certain to incorporate a information, supply code, and different supporting supplies. 

Engaged on these tasks provides you with worthwhile expertise and provide help to construct a powerful portfolio, which may improve your possibilities of securing your dream job. I extremely advocate everybody to doc their tasks on GitHub and Medium, after which share them on social media to draw extra consideration. Maintain working and preserve constructing; these experiences can be added to your resume as an actual expertise.
 
 

Abid Ali Awan (@1abidaliawan) is a licensed knowledge scientist skilled who loves constructing machine studying fashions. At the moment, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in expertise administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids scuffling with psychological sickness.