解决数据 英语是什么意思 - 英语翻译

• address data
• solving data
• resolving data
• addressing data
• they're tackling data

在 中文 中使用 解决数据 的示例及其翻译为 英语

Python在解决数据科学任务和挑战方面继续处于领先地位。
Python continues to take leading positions in solving data science tasks and challenges.

解决数据不一致问题.
Resolving data inconsistencies.

Python在解决数据科学任务和挑战方面继续处于前列地位。
Python continues to take leading positions in solving data science tasks and challenges.

描述领域知识在解决数据质量问题的作用。
Describe the role of domain knowledge in addressing data quality issues.

解决数据质量问题需要企业级的方法。
Addressing data quality requires an enterprise approach.

积极识别和解决数据问题。
Proactively identify and resolve data problems.

是时候解决数据囤积问题了.
It's time to tackle data's disposal problem.

企业可以解决数据管理问题.
Businesses should tackle data management.

为了有效地解决数据质量问题.
In order to address data quality issues effectively.

这是一种旨在解决数据丢失的项目。
It is the kind of project that aims to solve data loss.

你会依靠应用程序来解决数据问题.
You are going to be relying on application programming to solve the data problem.

在这部分中，你将会为银行解决数据分析挑战。
I will be solving a data analytics challenge for a bank.

如果客户或数据保护机构要求，威瑞信是否会为其客户提供合理的支持来解决数据隐私问题？?
In the case of requests from customers or data protection authorities, will Verisign provide its customers with reasonable support to help address data privacy matters?

问问你的团队，为什么他们是在顾问、解决数据科学问题的打包软件上花钱，而不是先考虑的解决方案。
Ask your team why they are spending money on consultants and/or packaged software for solving data science problems without considering what's available for free.

为帮助在投资者与项目间牵线搭桥，我们将解决数据缺失问题并改善项目信息渠道。
To help match investors with projects, we will address data gaps and improve information on project pipelines.”.

人口基金应负责处理并解决数据完整性问题，但至今尚未做到。
UNDP was responsible for addressing and resolving data integrity issues but so far had been unable to do so.

我们正在采取行动，以实现更大的合同标准化，解决数据缺口和改进风险缓解工具。
Achieve greater contractual standardization, address data gaps and improve risk mitigation instruments.

在解决数据科学任务和挑战方面，Python继续处于领先地位。
Python continues to take leading positions in solving data science tasks and challenges.

Q12:对你而言，在解决数据科学问题时，专业知识有多重要？?
How important is domain expertise for you when solving data science problems?

欧洲最近制定了严格的数据监管规定，这意味着企业用户将需要针对如何解决数据安全问题制定相应的战略。
Europe recently set strict regulations around data, which means that organizations will need to be strategic about how they're tackling data security issues.

我们正在采取行动，以实现更大的合同标准化，解决数据缺口和改进风险缓解工具。
The G20 Leaders will take action to achieve greater contractual standardisation, address data gaps and improve risk mitigation instruments.

数据隐私关注的增加：欧洲最近制定了严格的数据使用规范，这意味着企业将需要制定关于如何解决数据安全问题的战略。
Europe recently set strict regulations around data, which means that organizations will need to be strategic about how they're tackling data security issues.

解决数据主权的担忧，意味着欧洲的cio不得不比他们的美国同行提供更多的的云计算服务。
Addressing data sovereignty concerns means European CIOs are forced to pay more for cloud services than their US counterparts.

AI还可以帮助政府和金融机构解决数据隐私和保护问题。
AI could also help governments and financial institutions address data privacy and protection problems.

我们正在采取行动推进更大程度的合同标准化，解决数据缺口并改进风险缓释工具。
Achieve greater contractual standardization, address data gaps and improve risk mitigation instruments.

但至少，它在解决数据隐私和保护跨连接设备的数据流方面是无价的。
But at the very least, it will prove invaluable in addressing data privacy and protecting data streams across connected devices.

InCountry是第一家在云中提供全面解决方案的公司，它使公司能够在全球范围内运营并解决数据主权问题。
InCountry is the first to provide a comprehensive solution in the cloud that enables companies to operate globally and address data sovereignty.

从解决数据驻留问题到减轻知识产权风险，初创公司应该在战略上进行思考，并在选择云提供商之前考虑这些利益。
From addressing data residency issues to mitigating IP risks, startups should think strategically and take these benefits into account before selecting a cloud provider.

我们正在采取行动推进更大程度的合同标准化，解决数据缺口并改进风险缓释工具。
We are taking actions to achieve greater contractual standardization, address data gaps and improve risk mitigation instruments.

最佳实践包括记录关于数据完整性的假设，解决数据偏差，以及在实施前审查机器所确定的新规则。
Best practices include documenting assumptions around completeness of the data, addressing data biases, and reviewing new rules identified by the machine before implementing.