Scaling AI Models Like You Mean It

Setting up the necessary machine learning infrastructure to run these big models is another challenge. We need a dedicated model server for running model inference (using frameworks like Triton oder vLLM), powerful GPUs to run everything robustly, and configurability in our servers to make sure they're high throughput and low latency. Tuning the in... See more

Several engineers also maintained fallback models for reverting to: either older or simpler versions (Lg2, Lg3, Md6, Lg5, Lg6). Lg5 mentioned that it was important to always keep some model up and running, even if they “switched to a less economic model and had to just cut the losses.” Similarly, when doing data science work, both Passi and Jackson... See more

You’ve got a vector database that has all the right database fundamentals you require, has the right incremental indexing strategy for your use case, has a good story around your metadata filtering needs, and will keep its index up-to-date with latencies you can tolerate. Awesome.

Your ML team (or maybe OpenAI) comes out with a new version of their... See more

Top considerations when choosing foundation models

Accuracy

Cost

Latency

Privacy

Top challenges when deploying production AI

Serving cost

Evaluation

Infra reliability

Model quality

The human-centric platform for production ML & AI

Access data easily, scale compute cost-efficiently, and ship to production confidently with fully managed infrastructure, running securely in your cloud.

Deploying a Generative AI model requires more than a VM with a GPU. It normally includes:

• Container Service : Most often Kubernetes to run LLM Serving solutions like Hugging Face Text Generation Inference or vLLM.


• Compute Resources : GPUs for running models, CPUs for management services


• Networking and DNS : Routing traffic to the appropriate servic

... See more

In 2020, Jared Kaplan and his collaborators at OpenAI suggested that there was a set of “scaling laws” for neural network models of language; they found that the more data they fed into their neural networks, the better those networks performed.10 The implication was that we could do better and better AI if we gather more data and apply deep learni... See more