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AI Development Lifecycle: Complete Breakdown in 2023

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Artificial intelligence (AI) has emerged as a game-changing technology in recent years, offering businesses the potential to unlock new insights, streamline operations, and deliver superior customer experiences. 91.5% of leading businesses have invested in AI on an ongoing basis. Since AI continues to grow as a powerful solution to modern business problems, the AI development lifecycle is becoming increasingly complex. Today, AI developers are facing several challenges, including data quality, quantity, selecting the right architecture, etc., that must be addressed throughout the AI lifecycle.

Hence, realizing AI benefits requires a structured and rigorous approach to AI development that spans the entire lifecycle, from problem definition to model deployment and beyond. Let's explore the different stages of a successful AI development lifecycle and discuss the various challenges faced by AI developers.

9 Stages of Building A Successful AI Development Lifecycle

Developing and deploying an AI project is an iterative process that requires the revisitation of steps for optimal results. Here are the nine stages of building a successful AI development lifecycle.

1. Business Objective Use Case

The first step of the AI development lifecycle is identifying the business objective or problem that AI can solve and developing an AI strategy. Having a clear understanding of the problem and how AI can help is crucial. Equally important is having access to the right talent and skills is crucial for developing an effective AI model.

2. Data Collection and Exploration

After having established a business objective, the next step in the AI lifecycle is collecting relevant data. Access to the right data is critical in building successful AI models. Various techniques are available today for data collection, including crowdsourcing, scraping, and the use of synthetic data.

Synthetic data is artificially generated information helpful in different scenarios, such as training models when real-world data is scarce, filling gaps in training data, and speeding up model development.

Once the data is collected, the next step is to perform exploratory data analysis and visualizations. These techniques help to understand what information is available in the data and which processes are needed to prepare the data for model training.

3. Data Preprocessing

Once data collection and exploration are done, the data goes through the next stage, data preprocessing, which helps prepare the raw data and make it suitable for model building. This stage involves different steps, including data cleaning, normalization, and augmentation.

  • Data Cleaning – involves identifying and correcting any errors or inconsistencies in the data.
  • Data Normalization – involves transforming the data to a common scale.
  • Data Augmentation – involves creating new data samples by applying various transformations to the existing data.

4. Feature Engineering

Feature engineering involves creating new variables from available data to enhance the model's performance. The process aims to simplify data transformations and improve accuracy, generating features for both supervised and unsupervised learning.

It involves various techniques, such as handling missing values, outliers, and data transformation through encoding, normalization, and standardization.

Feature engineering is critical in the AI development lifecycle, as it helps create optimal features for the model and makes the data easily understandable by the machine.

5. Model Training

After preparing the training data, the AI model is iteratively trained. Different machine learning algorithms and datasets can be tested during this process, and the optimal model is selected and fine-tuned for accurate predictive performance.

You can evaluate the performance of the trained model based on a variety of parameters and hyperparameters, such as learning rate, batch size, number of hidden layers, activation function, and regularization, which are adjusted to achieve the best possible results.

Also, businesses can benefit from transfer learning which involves using a pre-trained model to solve a different problem. This can save significant time and resources, eliminating the need to train a model from scratch.

6. Model Evaluation

Once the AI model has been developed and trained, model evaluation is the next step in the AI development lifecycle. This involves assessing the model performance using appropriate evaluation metrics, such as accuracy, F1 score, logarithmic loss, precision, and recall, to determine its effectiveness.

7. Model Deployment

Deploying an ML model involves integrating it into a production environment to produce useful outputs for business decision-making. Different deployment types include batch inference, on-premises, cloud-based, and edge deployment.

  • Batch Inference – the process of generating predictions recurrently on a batch of datasets.
  • On-Premises Deployment  – involves deploying models on local hardware infrastructure owned and maintained by an organization.
  • Cloud Deployment – involves deploying models on remote servers and computing infrastructure provided by third-party cloud service providers.
  • Edge Deployment – involves deploying and running machine learning models on local or “edge” devices such as smartphones, sensors, or IoT devices.

8. Model Monitoring

AI model performance can degrade over time due to data inconsistencies, skews, and drifts. Model monitoring is crucial for identifying when this happens. Proactive measures like MLOps (Machine Learning Operations) optimize and streamline the deployment of machine learning models to production and maintain them.

9. Model Maintenance

Model maintenance of the deployed models is critical to ensure their continued reliability and precision. One approach to model maintenance is to build a model retraining pipeline. Such a pipeline can automatically re-train the model using updated data to ensure it remains relevant and efficient.

Another approach to model maintenance is reinforcement learning, which involves training the model to improve its performance by providing feedback on its decisions.

By implementing model maintenance techniques, organizations can ensure that their deployed models remain effective. As a result, models provide accurate predictions that align with changing data trends and conditions.

What Challenges Can Developers Face During The AI Development Lifecycle?

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Image by L_Nuge from Adobe Stock

With the increasing complexity of AI models, AI developers, and data scientists can struggle with different challenges at various stages of the AI development lifecycle. Some of them are given below.

  • Learning curve: The continuous demand for learning new AI techniques and integrating them effectively can distract developers from focusing on their core strength of creating innovative applications.
  • Lack of future-proof hardware: This can hinder developers from creating innovative applications aligned with their current and future business requirements.
  • Use of complicated software tools: Developers face challenges when dealing with complicated and unfamiliar tools, resulting in slowed development processes and increased time-to-market.
  • Managing large volumes of data: It is difficult for AI developers to get the computing power needed to process this vast amount of data and manage storage and security.

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