The emergence of generative AI prompted several prominent companies to restrict its use because of the mishandling of sensitive internal data. According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT.
Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models. Currently, no standardized process exists for overcoming data ingestion’s challenges, but the model’s accuracy depends on it.
Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues. The groundwork of training data in an AI model is comparable to piloting an airplane. If the takeoff angle is a single degree off, you might land on an entirely new continent than expected.
The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions.
IBM DataStage streamlines data integration by combining various tools, allowing you to effortlessly pull, organize, transform and store data that is needed for AI training models in a hybrid cloud environment. Data practitioners of all skill levels can engage with the tool by using no-code GUIs or access APIs with guided custom code.
The new DataStage as a Service Anywhere remote runtime option provides flexibility to run your data transformations. It empowers you to use the parallel engine from anywhere, giving you unprecedented control over its location. DataStage as a Service Anywhere manifests as a lightweight container, allowing you to run all data transformation capabilities in any environment. This allows you to avoid many of the pitfalls of poor data ingestion as you run data integration, cleaning and preprocessing within your virtual private cloud. With DataStage, you maintain complete control over security, data quality and efficacy, addressing all your data needs for generative AI initiatives.
While there are virtually no limits to what can be achieved with generative AI, there are limits on the data a model uses—and that data may as well make all the difference.
Try DataStage with the data integration trial
The post The importance of data ingestion and integration for enterprise AI appeared first on IBM Blog.
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