文章指出推理成本高昂会导致团队限制使用、减少上下文或避免某些应用。这个数据点虽然没有具体数字，但反映了当前AI部署的经济瓶颈，是SubQ试图解决的核心问题之一。

【令人震惊】「负责任地在核心业务流程部署 AI」——这句话意味着 Anthropic 正在承接以前由麦肯锡、埃森哲做的企业变革咨询工作。纯模型 API 商业模式的顶峰可能已过：Claude 的护城河从「技术优势」升级为「有金融资本背书的企业实施能力」，中间层 AI 集成商和咨询公司的生存空间被直接压缩。

最佳实践建议是简化部署过程，避免手动步骤，使自动化部署更加高效。

This highlights a common pitfall for beginners: understanding the infrastructure requirements for deploying software, especially the need for accounts and payment methods.

初学者常见陷阱：错误地认为部署到生产环境只需要代码，而忽略了账户、支付和API令牌等必要条件。

可能带有偏见的表述：Anthropic 声称五角大楼有机会在部署前测试模型，这种表述可能暗示了 Anthropic 对五角大楼决策过程的看法。

这种动态迫使企业以全新的方式部署资本和架构内部工作流程，表明了人工智能对企业管理方式的深远影响。

使用超过100万颗Trainium2芯片的数据，展示了Anthropic在AI硬件部署上的巨大规模。这一数字不仅反映了计算能力的投入，也显示了与AWS在芯片定制上的深度合作。对于AI模型训练而言，百万级芯片的部署规模是行业顶尖水平，表明Claude可能需要大量计算资源进行训练和推理。

使用超过100万个Trainium2芯片，这是一个惊人的硬件部署规模。这一数字不仅显示了Anthropic与Amazon的深度合作，也反映了训练和运行大型语言模型所需的庞大计算资源。相比其他AI公司，这种规模的芯片部署表明Anthropic正在全力投入AI基础设施。

大多数人认为企业会谨慎地小规模试点AI工具，但作者认为NEC正在全球范围内大规模部署Claude，这表明企业对AI技术的信任度远高于预期，挑战了传统的技术采用曲线和变革管理理论。

大多数人认为模型部署应该是全有或全无的，要么完全替代原模型要么完全不使用。但作者提出了一种'部分激活'的激进方法，只在代理模型与原模型达到特定一致性阈值时才使用代理，这种细粒度的控制方式打破了传统的二元部署思维。

使用Terraform和Helm进行云基础设施部署体现了现代DevOps实践在AI安全平台中的应用。这种基础设施即代码(IaC)方法确保了部署的可重复性和一致性，同时支持阿里云等特定云平台，显示了平台对生产环境的适应性。

简化的启动流程展示了容器化部署的优势，使用Docker Compose一键启动所有服务，大大降低了部署复杂度。这种设计反映了现代AI平台开发的一个重要趋势：简化环境配置，使研究人员能够快速开始工作，而不是陷入环境设置的困境。

这一观点提出了AI安全的经济新模式，通过广泛部署小型廉价模型来弥补单一大模型的不足。这种'广撒网'策略可能比依赖少数昂贵模型更有效，尤其在大规模代码库扫描场景中，为AI安全的经济可行性提供了新思路。

这个观点揭示了现有方法在实际部署中的一个重要局限性：它们通常依赖于特定的模型实现假设，而实际部署的模型往往更加灵活和复杂。这强调了Attention Editing框架的优势——它不依赖于精细的结构要求，可以适应各种实际部署场景，为模型转换提供了更大的灵活性。

令人惊讶的是：一张消费级显卡竟然能直接运行320亿参数的大模型，这打破了人们对大模型硬件需求的固有认知。过去需要多张高端显卡或专业服务器才能运行的模型，现在单张RTX 4090就能实现，这标志着大模型普及的门槛大幅降低。

令人惊讶的是：SELFDOUBT方法通过两级部署策略，在没有任务特定标签的情况下实现了90%的准确率和71%的覆盖率。这一成果表明，通过简单分析模型输出中的犹豫和验证行为，可以构建出高效的置信度过滤器，大幅提升模型在实际应用中的可靠性，无需额外标注数据。

令人惊讶的是：SELFDOUBT方法仅需单个推理轨迹就能进行不确定性量化，而传统方法通常需要多次采样或访问模型内部参数。这一突破使得该方法可以在延迟和成本受限的部署环境中使用，特别适用于无法获取模型内部信息的专有API，大大降低了实际应用门槛。

将AI原型到生产的时间从几个月缩短到几天是一个惊人的加速，这将彻底改变企业采用AI的方式。这种快速部署能力可能加速AI在各行业的普及，但也带来了关于AI系统安全性和治理的紧迫问题，企业需要在快速采用和确保安全之间找到平衡。

大多数人认为强大的AI模型应该广泛普及以造福更多人。但作者明确表示不会公开发布这个最强大的模型，暗示了AI能力扩散可能带来的风险大于收益，这与技术民主化的主流观点相悖。

大多数人认为谷歌作为 AI 领域的领导者会持续专注于云端大模型，但其突然转向端侧开源模型的做法令人意外。这种战略转变表明谷歌可能重新评估了 AI 部署的未来方向，从集中式向分布式转变，挑战了'更大模型更好'的行业共识，暗示了端侧 AI 可能成为下一个技术热点。

大多数人认为运行最大的Gemma 4模型需要专门的硬件和复杂的部署流程。但作者声称vLLM可以在DGX Spark上高效运行这些大型模型，暗示推理优化技术可能已经达到了一个临界点，使得复杂模型部署变得更加简单和高效。

大多数人认为大型语言模型(31B参数)只能在数据中心环境中运行，但作者声称这些模型可以在本地环境中高效运行。这一观点与行业共识相悖，暗示边缘计算能力可能比我们想象的更强大，可能会改变AI部署的格局。

Absolute size thresholds for degenerative aneurysms: [1][3][5]

Ascending aorta/aortic root: ≥5.5 cm

Descending thoracic aorta: ≥6.0 cm (or 5.5 cm if favorable anatomy for TEVAR)

Thoracoabdominal aorta: ≥6.0 cm

Lower thresholds apply for: [3]

Marfan syndrome or genetic conditions: 4.0-5.0 cm depending on condition

Bicuspid aortic valve: 5.0-5.5 cm

Rapid growth: >0.5 cm/year

Concomitant cardiac surgery: >4.5 cm if undergoing aortic valve surgery

Immediate surgical evaluation: [5]

Any symptomatic aneurysm regardless of size (chest/back pain, dysphagia, hoarseness, hemoptysis)

Acute complications (dissection, rupture, malperfusion)

Post-Repair Surveillance

After TEVAR: CT at 1 month, 12 months, then annually if stable. [1]

After open repair: CT or MRI within 1 year, then every 5 years if no residual aortopathy. Annual imaging if residual disease or abnormal findings.

for - stats - 27% of the gap can be reduced by wind and solar deployment and 20% by action on forests, while efficiency, electrification, fuel switching in buildings, transport and industry sectors can also contribute - UN Emissions Gap Report 2024 - Key Messages

TRSP Desirable Characteristics

TRSP Desirable Characteristics

TRSP Desirable Characteristics Data governance should take into account the potential future use and future harm based on ethical frameworks grounded in the values and principles of the relevant Indigenous community. Metadata should acknowledge the provenance and purpose and any limitations or obligations in secondary use inclusive of issues of consent.

TRSP Desirable Characteristics Ethical processes address imbalances in power, resources, and how these affect the expression of Indigenous rights and human rights. Ethical processes must include representation from relevant Indigenous communities

TRSP Desirable Characteristics Ethical data are data that do not stigmatise or portray Indigenous Peoples, cultures, or knowledges in terms of deficit. Ethical data are collected and used in ways that align with Indigenous ethical frameworks and with rights affirmed in UNDRIP. Assessing ethical benefits and harms should be done from the perspective of the Indigenous Peoples, nations, or communities to whom the data relate

TRSP Desirable Characteristics Resources must be provided to generate data grounded in the languages, worldviews, and lived experiences (including values and principles) of Indigenous Peoples.

TRSP Desirable Characteristics Use of Indigenous data invokes a reciprocal responsibility to enhance data literacy within Indigenous communities and to support the development of an Indigenous data workforce and digital infrastructure to enable the creation, collection, management, security, governance, and application of data

TRSP Desirable Characteristics Indigenous Peoples have the right to develop cultural governance protocols for Indigenous data and be active leaders in the stewardship of, and access to, Indigenous data especially in the context of Indigenous Knowledge

TRSP Desirable Characteristics Indigenous Peoples have the right to data that are relevant to their world views and empower self-determination and effective self-governance. Indigenous data must be made available and accessible to Indigenous nations and communities in order to support Indigenous governance.

TRSP Desirable Characteristics Indigenous Peoples have rights and interests in both Indigenous Knowledge and Indigenous data. Indigenous Peoples have collective and individual rights to free, prior, and informed consent in the collection and use of such data, including the development of data policies and protocols for collection.

TRSP Desirable Characteristics Indigenous data are grounded in community values, which extend to society at large. Any value created from Indigenous data should benefit Indigenous communities in an equitable manner and contribute to Indigenous aspirations for wellbeing.

TRSP Desirable Characteristics Data enrich the planning, implementation, and evaluation processes that support the service and policy needs of Indigenous communities. Data also enable better engagement between citizens, institutions, and governments to improve decision-making. Ethical use of open data has the capacity to improve transparency and decision-making by providing Indigenous nations and communities with a better understanding of their peoples, territories, and resources. It similarly can provide greater insight into third-party policies and programs affecting Indigenous Peoples.

TRSP Desirable Characteristics Governments and institutions must actively support the use and reuse of data by Indigenous nations and communities by facilitating the establishment of the foundations for Indigenous innovation, value generation, and the promotion of local self-determined development processes

TRSP Desirable Characteristics Indigenous Peoples’ rights and wellbeing should be the primary concern.

TRSP Desirable Characteristics Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.

TRSP Desirable Characteristics Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.

TRSP Desirable Characteristics Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.

No mention of how to authenticate the deployment, which is disappointing. See the repository for more.

• https://github.com/salto-io/salesforce-ci-cd-org-dev/blob/ca2177829b49b0fce1167ad4c93ea0190293f9ad/.github/workflows/push-master-branch.yml#L40C22-L40C97
• https://developer.salesforce.com/docs/atlas.en-us.sfdx_cli_reference.meta/sfdx_cli_reference/cli_reference_org_commands_unified.htm#cli_reference_org_login_sfdx-url_unified

Deployment: Significance of Branching for Continuous Delivery

The deployment of software in DevOps is based on Continuous Delivery. Continuous Delivery enables all kinds of changes, including new features, configuration changes, bug fixes and experiments, to be put into production safely and quickly in a sustainable manner. A Branching strategy and Base Truncs play an important role in this.

Model deployment in Azure ML

On the technical front, the number of components affected is much smaller; on the business front, it's usually a much easier conversation to persuade the team to roll back one small feature than twenty big features

Optimising covid-19 vaccine uptake—Time for a more nuanced approach. (2021, April 30). The BMJ. https://blogs.bmj.com/bmj/2021/04/30/optimising-covid-19-vaccine-uptake-time-for-a-more-nuanced-approach/

Guidance on developing a national deployment and vaccination plan for COVID-19 vaccines. (n.d.). Retrieved January 21, 2021, from https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-Vaccine_deployment-2020.1

Europe, W. H. O. R. O. for. (2020). Strategic considerations in preparing for deployment of COVID-19 vaccine and vaccination in the WHO European Region, 9 October 2020. https://apps.who.int/iris/handle/10665/335940

Newrelic deployment markers can also be generated via a post request although it's generally simpler to use the agent specific method.

Since Hypothesis uses a server side config I'm not sure what to do here as there is no config file to provide. 🤔

If it turns out this isn't feasible (although I'm sure there's a solution for this config_file issue) the alternative approach would be to use the post method.

To enable recording of deploys on the python agent via New Relic, you can simply call the newrelic-admin record-deploy command and pass it the necessary revision information. This will place a deployment marker on any graph you view in newrelic as a vertical line-indicating that a new revision of the code was released at that point in time.

Excelente para você entender, na prática, sobre Cloud Deployment (um de nossos importantes subtópicos!). Além disso, vai sair da palestra com mais ferramentas para seu cinto de utilidades!

Push a local non-master branch to heroku master