AI has become the driving force behind countless applications that empower businesses, enhance user experiences and tackle complex problems. However, developing cutting-edge AI applications is not without its challenges. In this article, we’ll share a process for developing AI apps. We’ll also cover the primary obstacles that developers face and solutions to overcoming them.
A process for developing AI apps
AI applications are complex, and developing them requires a well-structured process. This process typically includes eight essential steps.
Identify the problem/objective
Defining the business problem or objective that your AI app will address is the starting point of any app development project. Begin the planning process by creating a set of project requirements, which of the app’s functions or processes AI will be used in and the types of data required to build the model.
Prepare data
AI apps require large volumes of highly relevant training data to generate accurate predictions. The data preparation stage involves the collecting, labeling and replacement of blank or missing values. Choosing training data is important because the model will perform poorly if the data isn’t complete, accurate and relevant to the intended use case.
Choose the algorithm
AI algorithms are the set of instructions or rules that enable machines to learn independently. These algorithms determine how the data will be analyzed and used for decision-making, so selecting the right one for the use case is crucial. There are many different types of AI algorithms, all of which fall into two categories based on learning type: supervised and unsupervised. Supervised learning algorithms are trained using labeled input and output data. Unsupervised models are trained on unlabeled or raw data.
Train the algorithm
Training an AI algorithm involves trial and error. During model training, the AI algorithm uses the training data to learn and grow. Developers also assess the model for accuracy, making the necessary adjustments to ensure it is performing as intended.
Choose language(s)
Numerous programming languages can be used to develop AI apps and each has its own unique set of strengths and limitations. A few of the most popular ones include Python, Java, Lisp and R. Selecting the best one(s) for your AI app depends on the unique specifications of the project.
Select a platform
AI apps consume large amounts of data and compute resources, so selecting a platform designed for AI is important. In addition, look for a platform such as Snowflake that makes accessing development tools and libraries simple. These capabilities will streamline the development process, saving time, energy and moving the app into production sooner.
Develop and deploy
Developing the software to run an AI app follows a process similar to traditional app development. It includes creating the app’s architecture, front and back end development and designing the user interface. App development tools such as Streamlit simplify the process of developing apps that use generative AI and large language models (LLM), making it possible for teams to build and share powerful data apps with no front end development experience. Once the AI app has been developed and tested, it can be deployed to the production server.
Monitor and improve
Due to their nature, AI applications require continuous fine-tuning to ensure they’re functioning as intended. Since AI algorithms are continuously learning, they must be monitored closely to prevent data drift—unexpected changes to the statistical properties of the data used to train the AI model that can cause the performance of the AI algorithm to deteriorate over time. In addition to data updates, other routine maintenance includes security patches, version changes and the inclusion of new features.
Common barriers to AI application development and how to overcome them
Problems encountered during development stymie progress and slow the pace of innovation. Here are seven frequently encountered obstacles and the strategies and solutions for overcoming them.
Poor business use case alignment and operationalization planning
The potential use cases for AI are nearly limitless. Evaluating potential ROI against the intended business use cases helps decision-makers gain a clearer picture of where AI is likely to have the greatest impact. With target use cases identified, planning how AI-enabled solutions will be incorporated into existing infrastructure ensures these solutions can be brought online quickly. Consider the compatibility of existing business systems and processes, the need for additional compute and storage resources and the acquisition of talent to develop, deploy and maintain the AI-enabled solutions.
Lack of cross-team collaboration
Data silos stifle innovation and growth, especially in a data-intensive field such as AI. Whether you’re collaborating with global teams, various business units or third-party providers, you should be able to work collaboratively with your data. Modernizing your data environment with a platform such as Snowflake’s Data Cloud ensures your data is secure and shareable, reducing wait time. Teams should have access to live data in the moment, providing powerful insights between AI developers, analyst teams, data scientists and other stakeholders.
Inflexible development environment
Every developer has their own preference regarding programming languages and tools. Allowing people to work with their preferred tools and languages accelerates the development process while providing governance and sufficient performance capabilities.
Overestrictive data governance policies
AI apps leverage large, diverse data sets, some of which may contain sensitive information. The purpose of governance is to keep data secure and manage access to comply with relevant policies and regulations. However, locking down all data and minimizing access to it diminishes the value of the data for use in AI applications and elsewhere.
Instead, refocus your data governance regime around safely enabling the use of data. Automation can streamline governance across systems and clouds so that data is managed from one place rather than ad hoc across your entire ecosystem. Considering data governance up front, as data is ingested and uses are planned, rather than trying to securely manage it as an afterthought ensures that the data is adequately protected while allowing its full value to be realized.
Poor scalability
As mentioned before, AI apps are resource-intensive solutions. For organizations still using legacy compute and storage solutions, integrating AI tools into existing infrastructure can cause resource contention issues that interfere with operations. Upgrading to Snowflake’s Data Cloud provides instant access to rapidly scalable, near-infinite compute and storage resources.
Reliance on manual processes
AI development processes require organizations to scale their data volume and overall complexity, making it impossible to optimize your data environment using manual processes. Automating how resources are managed not only prevents costly human error, it also frees your teams to build rather than sink time into managing resources, installing upgrades and performing other maintenance. Efficiency through automation prevents wasted resources and increases value through greater productivity.
Constraints of traditional application development
Traditional, managed apps require customers to surrender their data to the care of app providers, trusting them to store it securely on their own data platform. As organizations accelerate their use of sensitive data, this practice is generating increasing concern. Connected apps resolve this problem by storing and processing customers’ data on their own platform rather than on the SaaS vendor’s own.
Advance your AI app development initiatives with Snowflake
Snowflake delivers AI-driven functionality and eliminates many traditional constraints that stifle innovation and bog down development processes. With Snowflake, developers have access to built-in functions and UIs, so they can accelerate their workflows with fast data access and elastically scalable data processing.
Additionally, Snowflake’s Native App Framework allows developers to build, market, monetize and distribute their apps to customers across the Data Cloud seamlessly and securely.
Learn more: Using Snowflake and Generative AI to Rapidly Build Features