Conclusion 22 More resources 24 2 AI in the EnterpriseA new way 
 to work As an AI research and deployment company, OpenAI prioritizes partnering with global companies because our models will increasingly ChatGPT Enterprise and our API. And our Deployment Team takes these products into companies to address their most pressing use cases. We use iterative deployment to learn quickly from customer use cases vary by company and industry but the lessons apply across all markets. The common theme: AI deployment benefits from an open, experimental mindset, backed by rigorous evaluations, and safety guardrails

ones as you uncover new vulnerabilities. Guardrails are a critical component of any LLM-based deployment, but should be coupled with robust authentication and authorization protocols, strict access controls real-world performance without compromising user experience. It’s especially important early 
 in deployment, helping identify failures, uncover edge cases, and establish a robust evaluation cycle. Implementing intervention, helping ensure agents operate safely and predictably in production. The path to successful deployment isn’t all-or-nothing. Start small, validate with real users, and grow capabilities over time.

2025… …To reflect the urgency of this effort, Dr. Makary has directed all FDA centers to begin deployment immediately, with the goal of full integration by the end of June. - USA FDA Press Release, and how it reshapes institutions, labor, and decision-making will depend on the safeguards and deployment frameworks that accompany it. The productivity upside may be significant, but unevenly distributed Developer Use Cases = Broad & Varied Note: CI / CD pipelines are continuous integration / continuous deployment pipelines. Source: IBM, ‘AI in Software Development’ (2024); Anthropic; Katalon; AccelQ; Monday;

file from the autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment 3. For application on 32bits, no support of 32bit tensorflow , a workround from FrozenGene a

our members at the coming TVM Conference 2019, stay tuned...Thank you Join Linaro to accelerate deployment of your Arm-based solutions through collaboration contact@linaro.org

Open Source at O〇ctoML TVM Meetup 11/8/2019 Jared Roesch OctoML is a new company building DL deployment solutions using the Apache (incubating) TVM project. A goal is to nurture the TVM community and

runtime pipeline available in github (https://github.com/Xilinx/ml-suite/blob/master/examples/deployment_modes/mp_classify.py) Streamlined multi-process pipeline using shared memory Usually need >4 Pre-Process

cached to accelerate inference, so MHA needs to cache 2?ℎ?ℎ? elements for each token. In model deployment, this heavy KV cache is a large bottleneck that limits the maximum batch size and sequence length