Thanks! The main difference is that Argilla is built as an open-source component to be integrated into the wider MLOps/LLMOps stack. The focus being on continous data collection, monitoring, and fine-tuning with open-source and commercial LLMs, as opposed to outsourcing training data collection, and one-off labeling projects. In the blog post we mention this with other words:
Domain Expertise vs Outsourcing. In Argilla, the process of data labeling and curation is not a single event but an iterative component of the ML lifecycle, setting it apart from traditional data labeling platforms. Argilla integrates into the MLOps stack, using feedback loops for continuous data and model refinement. Given the current complexity of LLM feedback, organizations are increasingly leveraging their own internal knowledge and expertise instead of outsourcing training sets to data labeling services. Argilla supports this shift effectively.
Great question - TVM / OctoML are a great option if you have an off-the-shelf ML model and off-the-shelf hardware. Tensil is different in that you can actually customize the accelerator hardware itself, allowing you to get the best trade-off of performance / accuracy / power usage / cost given your particular ML workload. This is especially useful if you want to avoid degrading the accuracy of your models (e.g. through quantization) to achieve performance targets.
You absolutely can use it in a data centre. You can even tape out an ASIC using these designs! Currently we've done most of our prototyping with edge FPGA platforms but if you want to try other platforms we'd love to help you get started. You can email me at tom@tensil.ai or use the contact methods on the website.
Faiss actually also uses HNSW internally, HNSWLIB is just a lighter weight implementation which allowed us to iterate faster. In the future we will switch it back out for FAISS to take advantage of its full array of functionality.
