How to Host Your AI App for Free: Deploying Gradio to Hugging Face Spaces
We built a Gradio app to demo our AI models. But it only ran on your local computer (localhost) so How do you show it…
Big Data in Polars: How to Scan and Process Multiple Files at Once
In the real world, data is rarely in one big CSV. It’s usually split, and this is where you might want to use Polars to…
AI Project: Chat with Images (Visual QA with LLaVA)
We’ve done Image Captioning (getting a simple description). But what if you want to have a conversation about an image? That’s where Hugging Face LLaVA…
Big Data in Polars: Reading and Writing Partitioned Parquet Files
When you have 1TB of data, you don’t save it in one giant file. You split it up. Polars Partitioned Parquet is handling large datasets…
AI Project: Text-to-Music Generation (Meta MusicGen)
We have generated images, video, and speech. The final frontier of generative media is Music. let’s explore Text to Music Python and see how text…
Processing Data Larger Than RAM: The Polars Streaming Engine (sink_parquet)
In Pandas, if your dataset is bigger than your RAM (e.g., a 30GB file on a 16GB laptop), you get a MemoryError. The Polars Streaming…
AI Project: Text-to-Video Generation (Hugging Face diffusers)
We’ve generated text, audio, and images. The final frontier is Video. Now, Hugging Face Text to Video technology is opening up exciting new possibilities for…
The Ultimate Combo: Using SQL on Polars DataFrames with DuckDB
You love Polars for its speed, but sometimes you just miss writing SQL. Maybe a colleague gave you a complex query and you don’t want…
Polars vs. Pandas: Zero-Copy Conversion (The Arrow Revolution)
One of the biggest fears about switching to a new tool like Polars is: “What if I need a library that only works with Pandas?”…
AI Project: Efficient Fine-Tuning with LoRA and PEFT (Train LLMs on Consumer Hardware)
In Fine-Tuning (Part 3: Evaluation & Sharing), we fine-tuned a small BERT model. But if you try to fine-tune a modern LLM (like Llama 3…
