AI development is reshaping traditional work processes, revolutionizing conventional methods. Traditionally, the software development cycle starts with a meeting to discuss the software’s goals and vision. This is followed by gathering requirements, developing a business strategy, designing the user interface, planning architecture, developing, testing (both functional and security), and finally, delivering the software.
While traditional software development methods, especially agile ones, have shown reasonable success, they may not be ideal for creating AI-driven applications.
Recent years have seen a significant shift. In 2020, the pandemic led many businesses to realize the benefits of AI, leading to increased AI usage and investment.
Recent reports show that AI model development has become more efficient.
What Sets AI Apart from Traditional Development?
The traditional development life cycle is increasingly being overshadowed by AI advancements. AI aims to replicate human intelligence and reduce the manual processes found in traditional development methods. While AI offers error-free development, it can be more expensive compared to conventional methods.
7 Essential Steps for AI Development
Here are seven key steps that every AI development team should follow to ensure successful AI software development and technological progress.
- Define the Use Case
Start by clearly identifying the problem the organization wants to solve. Ensure the development team has a thorough understanding by answering these questions:
- What specific problem is being addressed?
- What outcomes are expected from this solution?
- How will this problem be tackled?
- How do stakeholders believe AI will improve the existing process?
- What metrics will indicate success?
- What standards are required?
- How can the problem be broken down into manageable tasks for development sprints?
- Verify Data Availability
Identifying and securing the right data is crucial for AI model development. Since machine learning models depend heavily on the data they receive, finding accurate and relevant data is essential for ensuring model quality and effectiveness.
Consider these questions:
- What specific data (e.g., customer information, inventory levels) is needed to address the problem?
- How much data is required?
- Is the current data sufficient for building the model?
- Is there a need for additional data?
- Where and how will this data be stored?
- Perform Basic Data Exploration
The type of data you need will influence how you prepare it. There are no strict rules for the data’s format, quality, or quantity, but it’s important to prepare it properly. Teams specializing in machine learning spend considerable time cleaning and reformatting data. At this stage, it’s also crucial to divide the data into training, testing, and validation sets.
Consider these points:
- Modify data to fit a specific format or template.
- Remove irrelevant or incorrect data.
- Address any data gaps by adding or improving the data.
- Choose the AI Development Model
After selecting the appropriate algorithm, developers need to test the model against training data. It’s common not to get the desired results initially, so adjustments to hyperparameters, random arguments, or the neural network’s depth may be needed. Developers might also use pre-trained models to build their own.
Keep track of the model’s output by versioning each iteration.
- Ensure the Implementation Platform
The choice of platform for training and deploying the AI model is crucial. Platforms like Microsoft Azure, Google Cloud AI, and TensorFlow offer significant benefits for AI development.
Choosing the right platform aligned with business needs can enhance performance, speed, and security. Cloud-based development can lead to better results.
- Test AI Software
The goal at this stage is to minimize unexpected changes in behavior when the model is deployed. The model undergoes extensive testing using training, validation, and testing datasets.
If the model doesn’t perform well, it may need adjustments. Improving performance might involve switching algorithms, enhancing data quality, or expanding the dataset. Address potential issues like overfitting by including more data.
- Deploy and Maintain
After thorough testing, evaluate the model’s effectiveness by comparing it against the business criteria set in the first step. Check Key Performance Indicators (KPIs) to ensure the model meets its goals. If the criteria are not met, you might need to revise the model or enhance the data quality and quantity.
Once the model meets all requirements, deploy it in the desired environment, such as cloud, edge, or on-premises. Before deployment:
- Schedule regular reviews of the model’s performance.
- Use this as a benchmark for future model iterations.
- Maintain and update the model to adapt to new data.
How Neuralberry Can Help
Expanding the use of AI presents numerous benefits. It is becoming increasingly important in software engineering. Companies can stay competitive and innovative by developing and implementing effective AI solutions.
Neuralberry offers the expertise and tools to help businesses create and deploy AI-enabled software and applications, giving them a competitive edge.
Contact us today to learn more about how AI can benefit your business.