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:

1. Improving customer experiences
2. Improving employee efficiency
3. Speeding up innovation

To this end, companies are developing AI applications for:

• IT automation
• Intelligent task/process automation
• Supply and logistics
• Automated customer service agents
• Automated human resources
• Automated threat analysis/investigation

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.

Data Management Challenges

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.

Application Production Challenges

Many companies are also struggling to create the AI applications they require, using Machine Learning (ML) and other production tools. Application production challenges include:

• Creating an IDE – An Integrated Development Environment (IDE) is a centralized platform with a source code editor, a compiler and a debugger. Using an IDE allows AI developers to write, debug, compile and publish software code under one interface. The lack of an IDE has been cited by some companies as a reason for the failure of their AI initiatives.
• Enabling Cross-Team Collaboration – To overcome conflicts between AI production teams, many companies are adopting a standard known as Machine Learning Operations (MLOps). This provides a set of best practices, operational strategies, processes and tools to create a framework for machine learning models. MLOps encourages collaboration between teams and gives each team visibility and control over the AI development lifecycle.
• Scalability of AI Development – Companies have trouble scaling ML model development operations to allow their developers to work with larger datasets and more complex model types. MLOps provides a solution for this problem by establishing documentation and version control standards that allow AI developers to track their progress and apply lessons learned from previous ML models to new models.
• Security of AI Development – To keep AI applications secure from outside attacks in the production phase, companies must establish and enforce security procedures for how and where AI applications are created. Some companies are choosing to keep AI application development in-house or in a leased and collocated data center environment, instead of using public or hybrid cloud platforms, to protect it from outside security breaches.
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Human Resource Challenges

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. 

Infrastructure Challenges

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.