7 AI Portfolio Tasks to Increase the Resume

I really consider that to get employed within the area of synthetic intelligence, you want to have a powerful portfolio. This implies you want to present the recruiters which you could construct AI fashions and purposes that clear up real-world issues.

On this weblog, we’ll evaluate 7 AI portfolio initiatives that may increase your resume. These initiatives include tutorials, supply code, and different supportive supplies that can assist you construct correct AI purposes.

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

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

Screenshot from the challenge

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

It’s that straightforward.

2. Construct AI Tasks utilizing DuckDB: SQL Question Engine

Challenge hyperlink: DuckDB Tutorial: Constructing AI Tasks

Screenshot from the challenge

On this challenge, you’ll study to make use of DuckDB as a vector database for an RAG software 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 simple challenge for newcomers, however earlier than you dive into constructing the AI software, you want to study 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

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

Screenshot from the challenge

Cohere API is healthier than OpenAI API  by way of performance for growing AI purposes. On this challenge, we’ll discover the assorted options of Cohere API and study to create a multi-step AI agent utilizing the LangChain ecosystem and the Command R+ mannequin. This AI software will take the consumer’s question, search the online utilizing the Tavily API, generate Python code, execute the code utilizing Python REPL, after which return the visualization requested by the consumer. That is an intermediate-level challenge for people with primary data and curious about constructing superior AI purposes utilizing the LangChain framework.

4. Wonderful-Tuning Llama 3 and Utilizing It Regionally

Challenge hyperlink: Wonderful-Tuning Llama 3 and Utilizing It Regionally: A Step-by-Step Information | DataCamp

Picture from the challenge

A preferred challenge on DataCamp that may aid you fine-tune any mannequin utilizing free sources and convert the mannequin to Llama.cpp format in order that it may be used domestically in your laptop computer with out the web. You’ll first study to fine-tune the Llama-3 mannequin on a medical dataset, then merge the adapter with the bottom mannequin and push the complete mannequin to the Hugging Face Hub. After that, convert the mannequin recordsdata into the Llama.cpp GGUF format, quantize the GGUF mannequin and push the file to Hugging Face Hub. Lastly, use the fine-tuned mannequin domestically with the Jan software.

5. Multilingual Automated Speech Recognition

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

Code Repository: kingabzpro/Urdu-ASR-SOTA

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

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

My hottest challenge ever! It will get nearly half 1,000,000 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 House by kingabzpro

On this challenge, you’ll fine-tune a speech recognition mannequin in your most popular language and combine it with a language mannequin to enhance its efficiency. After that, you’ll use Gradio to construct an AI software and deploy it to the Hugging Face server. Wonderful-tuning is a difficult job 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

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

Picture from the challenge

One other in style challenge on GitHub. It entails constructing a CI/CD pipeline or machine studying operations. On this challenge, you’ll study machine studying challenge templates and how one can automate the processes of mannequin coaching, analysis, and deployment. You’ll study 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 information is pushed or code is up to date. It’ll use new information to retrain the mannequin, generate mannequin evaluations, pull the educated mannequin, and deploy it on the server. It’s a totally automated system that generates logs at each step.

7. Wonderful-tuning Steady Diffusion XL with DreamBooth and LoRA

Challenge hyperlink: Wonderful-tuning Steady Diffusion XL with DreamBooth and LoRA | DataCamp

Picture from the challenge 

Now we have realized about fine-tuning giant language fashions, however now we’ll fine-tune a Generative AI mannequin utilizing private pictures. Wonderful-tuning Steady Diffusion XL requires only some pictures and, consequently, you will get optimum outcomes, as proven above.

On this challenge, you’ll first study Steady Diffusion XL after which fine-tune it on a brand new dataset utilizing Hugging Face AutoTrain Advance, DreamBooth, and LoRA. You may both use Kaggle totally free GPUs or Google Colab. It comes with a information that can assist you each step of the way in which.

Conclusion

The entire initiatives talked about on this weblog had been constructed by me. I made positive to incorporate a information, supply code, and different supporting supplies. 

Engaged on these initiatives will provide you with precious expertise and aid you construct a powerful portfolio, which might improve your possibilities of securing your dream job. I extremely advocate everybody to doc their initiatives on GitHub and Medium, after which share them on social media to draw extra consideration. Preserve working and hold constructing; these experiences can be added to your resume as an actual expertise.
 
 

Abid Ali Awan (@1abidaliawan) is an authorized information 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 information 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 combating psychological sickness.