0:59
Bringing #llms in-house with Adam Probst
neptune_ai
ZenML and #llm security (#mlops world 2023)
0:50
ZenML in the #llm space with Adam Probst at #mlops World 2023
0:58
Challenges of adopting #llms in production on your own infra
LIVE
[Private video]
0:41
#llms used at Digits with Hennes Hapke at #mlops world 2023
Classical #ml models vs. #llms at Digits
#generativeai use cases at Digits
Challenges with #llms
Methods for evaluating #llms with Hennes Hapke
Hosting and #finetuning #llms (#mlops #genai world)
0:55
Reviving and improving Kubeflow: #mlops world 2023
0:48
#kubernetes distributions for #ml
The future of #kubernetes
1:00
The future of #opensource, #mlops and #llms
0:42
Future plans for Canonical’s #ai #ml projects with Maciej Mazur (Principal ML Engineer)
0:32
Modular philosophy of #ubuntu with Maciej Mazur: MLOps World 2023
The future of #opensource with Rajiv Shah (ML Engineer at #huggingface)
0:47
#llm cost considerations: self-hosting vs. proprietary API
0:39
Future applications of #llms
0:57
#llm latency with Rajiv Shah (ML Engineer at #huggingface)
0:56
#llm deployment considerations: #gpu vs. #cpu
#llm projects: data availability and maturity in enterprises
Evaluation in #finetuning and #promptengineering
0:44
#llm evaluation methods: models vs. humans
0:36
#llm based evaluation
0:49
Challenges of classical #llms with Rajiv Shah (ML Engineer at #huggingface)
0:54
Evaluating #llm models with Rajiv Shah (#mlops world 2023)
Safeguarding #llms with Shreya Rajpal from Guardrails AI
0:51
Challenges when building #guardrails #ai: Shreya Rajpal at #mlops world 2023
User experience of plugging-in #guardrails #ai when using #chatgpt
Predictions for #llms with Charles Frye (#mlops #generativeai world 2023)
Experiment tracking and LLMs
The intersection of #neuroscience and #llms
Importance of long-term memory in #agentsystems
Best practices for evaluating #llms with #ai Makerspace
Tips for working with #llms
Using #llms as evaluators
0:34
What’s next for #llms with Greg Loughnane
How #mlops started at Pinterest
Multi-task learning in #ml
Moving from #tensorflow to #pytorch
Main challenges of the #ml training pipeline at Pinterest
