#453 overall
Full Stack Deep Learning
Full Stack Deep Learning
1 recommender · 1 thread · recommended in 1 year, 2023 · score 1.0
Full Stack Deep Learning is a course on taking deep learning models from research to production, covering the practical engineering side of building, training, and deploying ML systems. It was taught at UC Berkeley and is run by the Full Stack Deep Learning team.
One commenter says they took the course several years ago at Berkeley and highly recommends it.
Recommendations by year
20062026
I love the full stack deep learning crew, and took their course several years ago in Berkeley. I highly recommend it.
One thing that always blows my mind is how much it is just not worth it to train LLMs in the cloud if you're a startup (and probably even less so for really large companies). Compared to 36 month reserved pricing the break even point was 8 months if you bought the hardware and rented out some racks at a colo, and that includes the on hands support. Having the dedicated hardware also meant that researchers were willing to experiment more when we weren't doing a planned training job, as it wouldn't pull from our budget. We spent a sizable chuck of our raise on that cluster but it was worth every penny.
I will say that I would not put customer facing inference on prem at this point- the resiliency of the cloud normally offsets the pricing, and most inference can be done with cheaper hardware than training. For training though you can get away with a weaker SLA, and the cloud is always there if you really need to burst beyond what you've purchased.