As one of the most exciting topics in the tech world today, artificial intelligence (AI) has received a great deal of attention. These sophisticated systems can perform some incredible feats, and we’re seeing them increasingly integrated throughout our everyday lives.
Given the complexity of the technology, it’s no surprise that designing AI models and systems is difficult and highly technical. In this guide, we look at some common issues that can arise in AI model pipelines. Read on to learn more.
Why Use a Pipeline?
An AI pipeline is a process through which developers can automate design workflows when creating AI tools. The process encompasses a number of distinct steps, each of which handles a specific task, from data collection and analysis to implementation.
Using a pipeline allows for faster and more accurate development. Each stage of the process can be focused on to ensure everything is done correctly before progressing to the next step. Pipelines are also scalable, making them perfect for tools looking to expand and grow as the technology develops.
However, issues can arise when using pipelines for AI models. Let’s take a look at some of the most common problems.
3 Most Common Pipeline Issues for AI Models
Creating an AI model involves dealing with vast volumes of data. This is also the case with pipelines, developers must stay on top of data management to prevent it from spiralling out of control.
Transferring and ingesting data can lead to problems, and when dealing with such large amounts, locating the source of errors can be tricky. Using an ML Observability Platform can help with data segmentation and management to prevent this from happening.
Training the Model
As we mentioned above, AI tools can be used for a huge range of different things. They can generate text and images or even power self-driving cars. However, AI systems don’t work straight out of the box. They must first be trained by developers, a critical stage in the design pipeline.
The biggest error designers can make is using unverified, unreliable data to train AI models. This can lead to biases and mistakes in the results delivered by the system. To prevent this, developers must ensure all data used to train models is vetted and comes from a reputable source.
After the development stage is complete and the AI model has been implemented, the pipeline process must continue.
AI systems are dynamic and responsive; they evolve over time as they learn and ingest more information. This means the pipeline must continue and the model must be continually monitored to ensure it is always working correctly.
This monitoring stage can be one of the biggest pipeline issues, especially if it is not conducted thoroughly. Monitoring software should be used to identify and address any problems that might arise.
The latest research has revealed that 35% of companies worldwide are now using AI. This is an incredible statistic and is a testament to how rapidly the technology has grown. For developers, understanding common pipeline issues for AI models is critical if they want their products to be ready for the AI age.