First, give conclusions that can be used for decision-making
An AI application can be outsourced on four levels. The diagnostic layer identifies operational tasks, samples, data, interfaces, risks and whether they are worth doing; the PoC layer validates models, knowledge, tools or critical technology items are unknown; the production layer builds product interfaces, backstages, identity clearances, interfaces, logs, monitoring, deployments and abnormal retreats; and the operating layer maintains knowledge, assessments, models, costs and malfunctions on a continuous basis. The enterprise can either purchase only one of them or commission the same team to the end, but the contract must state responsibility for data collation, third-party account numbers, model transfers, cloud resources, handmarking and client-side approvals that are not included.
What conditions need to be identified before judgement is made?
The same question may have different answers under different business, data and project phases. It is suggested that the following conditions be checked and that the common findings on the web be incorporated into their own projects.
Suggested order of advance
First, we'll be clear about the target and the border.
• Dismantling the target into diagnostics, PoC, production and operation work packages.
Validation Key Dependence
Customer input, supplier delivery and third-party dependence are identified on a case-by-case basis.
Development of assessable outcomes
(b) Proof of reversible sample and engineering acceptance for each phase.
Make sure you decide the next step with the real results.
The attribution, alteration, withdrawal and liability for transportation of assets is specified in the contract.
How do you understand it in the actual business?
The company procurement AI knowledge assistant outsources, and if the supplier is responsible for only accessing the model and setting up chat pages, document governance, synchronizing authority, system login, quality assessment and online transport may be entirely separate.
The easiest pit to step on.
Consider the "access to the big model" as the full AI project scope
The quotation did not indicate the cost of data governance, interface, deployment and operation
Source code, evaluation collection and account interface are discussed only at the end of the project
How should we end up receiving and confirming?
The scope list should cover business, AI, software engineering, systems integration, secure deployment and operation assets; each delivery counterpart, version, sample receipt and inspection, and standard adoption, allowing enterprises to independently view codes, configuration, operating costs and known limitations.
When preparing to communicate with suppliers or internal teams, it is recommended that current processes, representative samples, existing systems, planning time and budget levels be brought. First, the unknown items are clearly marked, and then the decision is made to use diagnostics, PoC, fixed-range projects or ongoing research and development, which is usually more reliable than a direct demand for a price and duration without borders.