School
for
Poetic
Computation
What if datasets were small enough to care for? This class traces the data pipeline behind major AI systems and proposes an alternative: handmade datasets built with intention, consent, and scale. Students of all levels will train custom models using human-scale data and a fraction of the energy resources of large AI models .
Images courtesy of teachers.
While this class works primarily with Google Colab and Google Drive, there are alternatives to Google when it comes to working with ML that will be shared as resources to explore in the future.
How does data become AI—and who does the labor? Handmade Datasets unpacks the pipeline behind large AI systems like Stable Diffusion by tracing the journey from web scraping to training to generation.
We'll examine each step: how websites get crawled and archived, how images and their alt-text descriptions become training material, how click-workers and automated systems curate data, and how all of this shapes what a model generates. We'll then propose an alternative: "handmade datasets," or human-scale, personally assembled datasets, small enough that creators can handle each datapoint.
Drawing on examples from artists like Anna Ridler and Stephanie Dinkins as well as events like the Dataset Farmers Market, we'll explore how slowness can be a form of subversion, creating space for consent, stewardship, and intentionality.
Students will learn practical techniques for training models with limited data, including data augmentation, GANs, LoRA, RAVE, microgpt and RAG. Through hands-on projects and critical discussion, students will develop both the technical capability to work with small-scale ML and a more nuanced understanding of data collection and the labor behind AI systems. By the end of the class, participants will have created their own handmade dataset and trained a custom model.
3–4 hours a week outside class on weekly assignments building toward a final project
No prior machine learning or advanced coding experience required. Students should have basic computer literacy and a willingness to experiment.
This class may be for you if you:
This class may NOT be for you if you:
Aarati Akkapeddi is a cross-disciplinary artist, coder, and educator based in Lenapehoking (Brooklyn, NY). They often use personal and institutional archival materials, combining computational and analog techniques like machine learning & printmaking to create artwork that investigates overlooked relationships and histories. Their creative work has been supported by institutions such as The Photographers' Gallery, ETOPIA Center for Art & Technology, and LES Printshop. They work at The Experimental Humanities Collaborative Network, creating digital spaces and tools.
they/them
· website
· instagram
Isa Haid is a researcher, writer, and artist. She is fascinated by the intertwined histories of labor, technology, and infrastructure. Seeking new network topologies to reclaim our technological imagination, Isa likes to experiment with the in-between of physical and digital media. She is a co-founder of MissVideo4u, a USB-based video distribution network. Isa works at the Museum of the Moving Image in Queens, NY and is affiliated with the Border Tech Lab at UT Austin.
Applications open until Applications closed on April 27, 2026.
You can expect to hear back from us about the status of your application on May 11, 2026. Please email us at admissions@sfpc.study with any questions you have.
For 10 classes, it costs $1200 + processing fees, for a one-time payment. We also offer payment plans. Participants can schedule monthly payments of the same amount. First and last payments must be made before the start and end of class. *Processing fees apply for each payment.
SFPC processes all payments via Withfriends and Stripe. Please email admissions@sfpc.study if these payment options don't work for you.
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