
Blog Article
The AI race has started, but there are several technical hurtles to get through before launching your AI application. What should you be considering as your company considers its AI future?
Today, companies of all sizes are building their own Artificial Intelligence (AI) technologies. A recent survey by the International Data Corporation (IDC) asked over 2,000 decision makers in various businesses about their plans and motives for AI development. Based on their feedback, the top three factors driving AI production are:
To this end, companies are developing AI applications for:
But the IDC survey also highlighted the problems that many companies face with AI initiatives, and revealed some reasons why AI projects fail. In this article, we explore the top technical challenges of AI production.
AI programs “learn” by reviewing massive amounts of data. But managing data for AI applications isn’t always easy. Some data management challenges for AI production include:
Establishing Data Quality Standards – The data used to train AI programs must be accurate. An AI application trained on incorrect data will inherit errors that can negatively impact its performance. For example, an AI business application may make inaccurate predictions about future trends, which can lead a company to spend money on the wrong initiatives. Also, the information used to train AI programs must be free of bias. If an AI human resource application is trained using resumes provided by male applicants, for example, it may assume that being male is a prerequisite for certain jobs.
Companies must establish data quality standards to ensure that data used to train AI applications is correct, unbiased, valid, relevant, consistent and complete. The better the quality of the training data, the better an AI application will perform and the better the outcomes for the company will be.
Integrating and Preparing Data – AI engineers must collect data from multiple sources (e.g. enterprise programs, IoT devices, etc.), consolidate it into standardized data sets and “clean” the data to remove errors, biases and duplicate information. It’s a time-consuming task, and AI developers can even spend up to a third of the AI production lifecycle on data integration and preparation. According to the IDC survey, lack of production-ready data is a major reason why AI/ML initiatives fail.
Establishing Data Governance and Security – Companies must set up governance standards for how employees use data to train AI applications, to ensure that the data remains accurate and in compliance with data laws. At the same time, companies must establish security policies to prevent unauthorized disclosure of private data. Security policies should determine which employees have access to certain types of data and govern how these employees use, store and transmit data when using it for AI training.
Many companies are also struggling to create the AI applications they require, using Machine Learning (ML) and other production tools. Application production challenges include:
Many companies have trouble finding people who understand AI coding and algorithms, data structures and ML models. The IDC survey reports that lack of staff with the necessary expertise is another common reason for AI initiative failures.
For many companies, the first major challenge will be finding available space in a data center that supports the infrastructure needed for an AI application, with high power density, liquid cooling options and a trained team to ensure continuous uptime of mission-critical systems. Contact Sabey Data Centers today to find out about pre-leasing opportunities or retail colocation options for data center services for your AI application deployment.