The case for embedding audit trails in AI systems before scaling

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Orchestral frames for AI services serve many functions for businesses. They should only allow applications or agents to control the workflows and agents of managers and inspect their systems.

The company starts to scalize AI services and produce, managed, monitored, monitored, inspected firm pipeline Provides their agents as likely to work. Without this control, organizations may not be aware of what is happening in AI systems, and if only one thing is wrong, they can discover the issue very late or do not follow the rules.

Kevin Kiley, head of Enterprise Orchestra PublishingIn an interview with the frameworks should include a audioability and persecution, he spoke to Venturebeat.

“It is critical to show that this observation and checking the entrance to the inspection and to show what the point in which point is.” “You need to know that this is an internal worker who does not know whether it is a bad actor or that there is a data exchange or hallucination.”

Ideally, resuscitation and audit roads should be built in the very early stage. The new AI application or providing the potential risks of the agent and to ensure that it continues to reach the standards before placing it will help facilitate the concerns around to produce AI.

However, organizations first did not design their systems Fine tracking and audit. Many AI pilot programs began life as an orchestra layer or the start of experiments without the audit track.

Now large question facility can manage all agents and applications, Ensure pipelines remain firm And if something is wrong, what’s wrong and follow the AI ​​performance.

Choosing the right method

Prior to building any AI application, experts said organizations needed Reserve their data. If a company knows the entry of AI systems and which information is well regulated, there is a key to comparing with long-term performance.

“When working some of these AI systems, more, more, which information can I confirm that my system is really properly working or not?” Yrieix Garnier, Deputy Prime Minister DatadogHe spoke to Ventureat in an interview. “It is very difficult to understand that I have a correct reference system to confirm the AI ​​solution.”

After determining the organization information and finding their data, you need to set the version or version number to determine the data or version number – to make experiments and understand what the model changes. These data and models, these special models or agents, authorized users and any application that uses basic handling numbers, or can be downloaded to the orchestra or observation platform.

When choosing to build the foundation models, orchestra must take into account the transparency and openness of the teams. Although there are numerous advantages of some indoor source orchestral orchestral, the more open source platforms can also offer the cost of some enterprises, for example, the benefits of the cost of increasing decision-making systems.

Open source platforms such as MLFLOW, Langchain and Crumb Provide agents and models by granul and flexible guides and monitoring. Enterprises can choose to develop AI pipelines from the last platform or using various coordinated tools as a datadog Aws.

Another review for the enterprise is to connect to a system with a system and application responses that apply to maps or compliance tools or responsible AI policies. Aws and Microsoft It also offers the services that follow both AI tools and the user who follow the user to protectors and other policies.

Kilay, during the construction of these reliable pipelines, said that a review of enterprises was rotated in a more transparent system. For Kilay, there is no visibility of how AI systems will not work.

“There are these situations to be comfortable and the operation of a closed system, regardless of the use or industry. There are great tools there, but I do not know how it comes to these decisions.

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