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I actually consider that to get employed within the discipline of synthetic intelligence, it’s essential have a robust portfolio. This implies it’s essential present the recruiters that you may construct AI fashions and purposes that remedy real-world issues.
On this weblog, we’ll evaluation 7 AI portfolio initiatives that may enhance your resume. These initiatives include tutorials, supply code, and different supportive supplies that will help you construct correct AI purposes.
1. Construct and Deploy your Machine Studying Utility in 5 Minutes
Mission hyperlink: Construct AI Chatbot in 5 Minutes with Hugging Face and Gradio
Screenshot from the undertaking
On this undertaking, you may be constructing a chatbot utility and deploying it on Hugging Face areas. It’s a beginner-friendly AI undertaking that requires minimal data of language fashions and Python. First, you’ll study varied elements 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 Tasks utilizing DuckDB: SQL Question Engine
Mission hyperlink: DuckDB Tutorial: Constructing AI Tasks
Screenshot from the undertaking
On this undertaking, you’ll study to make use of DuckDB as a vector database for an RAG utility and likewise as an SQL question engine utilizing the LlamaIndex framework. The question will take pure language enter, convert it into SQL, and show the end in pure language. It’s a easy and easy undertaking for novices, however earlier than you dive into constructing the AI utility, it’s essential study just a few fundamentals of the DuckDB Python API and the LlamaIndex framework.
3. Constructing A number of-step AI Agent utilizing the LangChain and Cohere API
Mission hyperlink: Cohere Command R+: A Full Step-by-Step Tutorial
Screenshot from the undertaking
Cohere API is healthier than OpenAI API when it comes to performance for growing AI purposes. On this undertaking, we’ll discover the varied options of Cohere API and study 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 undertaking for people with primary data and concerned with constructing superior AI purposes utilizing the LangChain framework.
4. Wonderful-Tuning Llama 3 and Utilizing It Domestically
Mission hyperlink: Wonderful-Tuning Llama 3 and Utilizing It Domestically: A Step-by-Step Information | DataCamp
Picture from the undertaking
A preferred undertaking on DataCamp that may make it easier to fine-tune any mannequin utilizing free sources 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 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 regionally with the Jan utility.
5. Multilingual Computerized 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 undertaking ever! It will get nearly half 1,000,000 downloads each month. I fine-tuned the Wave2Vec2 Giant 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 undertaking, 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 utility and deploy it to the Hugging Face server. Wonderful-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
Mission hyperlink: A Newbie’s Information to CI/CD for Machine Studying | DataCamp
Picture from the undertaking
One other in style undertaking on GitHub. It includes constructing a CI/CD pipeline or machine studying operations. On this undertaking, you’ll study machine studying undertaking templates and how you 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 varied Git operations.
Finally, you’ll construct end-to-end machine studying pipelines that may run when new knowledge is pushed or code is up to date. It would use new knowledge to retrain the mannequin, generate mannequin evaluations, pull the skilled 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
Mission hyperlink: Wonderful-tuning Steady Diffusion XL with DreamBooth and LoRA | DataCamp
Picture from the undertaking
We have now realized about fine-tuning giant language fashions, however now we’ll fine-tune a Generative AI mannequin utilizing private images. Wonderful-tuning Steady Diffusion XL requires only some photographs and, because of this, you may get optimum outcomes, as proven above.
On this undertaking, 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 without cost GPUs or Google Colab. It comes with a information that will help you each step of the best way.
Conclusion
All the 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 offers you worthwhile expertise and make it easier to construct a robust portfolio, which might enhance your possibilities of securing your dream job. I extremely suggest everybody to doc their initiatives on GitHub and Medium, after which share them on social media to draw extra consideration. Maintain working and maintain constructing; these experiences will also 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 battling psychological sickness.
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