To enable predictions, AI-powered products need to instruct their underlying machine learning model to identify patterns and correlations in data. This data, known as training data, can include collections of images, videos, text, audio, and more.

You can leverage existing data sources or gather new data specifically for training your system.

For e.g., you can utilize Overture maps, recently open sourced to develop AI based  predictive navigation system

The quality and labelling of the training data you obtain or collect directly shape the output of your system, influencing the overall user experience.

Consider following as guiding principles for collecting and evaluating data for AI systems

• Acquire High Quality Data :Begin by strategizing the acquisition of high-quality data as a foundational step. While model development is often prioritized, allocating adequate time and resources to ensure data quality is essential. Proactive planning during data gathering and preparation is crucial to prevent adverse consequences stemming from suboptimal data choices later in the AI development process.
• Map Data Needs to User  Needs : Identify the type of data necessary for training your model, taking into account factors such as predictive capability, relevance, fairness, privacy, and security.  Read my previous article for more details
• Source your data ethically and diligently: Whether utilizing pre-labelled datasets(there are a lot of sources to pre-labelled data,( Google’s DataSet Search and FACET Dataset Explorer are  an excellent resource) or collecting your own, it’s crucial to rigorously evaluate both the data itself and the methods employed in its collection to ensure they align with the ethical standards and requirements of your project.
• Thoroughly prepare and document your data :Ensure your dataset is suitably primed for AI applications, and document both its contents and the decisions made during the data gathering and processing stages. Partition the data into training and test sets. Test sets consist of data unfamiliar to your model, serving as a means to determine the effectiveness of your model. The training set must be sufficiently extensive to effectively train your model, while the test set should be sizable enough to thoroughly evaluate your model’s performance.
• Adapt your design for labelers and labeling processes.Data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.Labels can be applied through automated procedures or by individuals referred to as labelers. The term “labelers” is inclusive, encompassing diverse contexts, skill sets, and levels of specialization. In the context of supervised learning, the accuracy of data labels is paramount for obtaining valuable insights from your model.Deliberate design of labeller instructions and user interface flows can enhance the quality of labels, thereby improving overall model output.
• Fine-tune your model. Once your model is operational, scrutinize the AI output to verify its alignment with product goals and user requirements.What if Tool by Google is an excellent resource to fine tune your model If discrepancies arise, troubleshoot by investigating potential issues with the underlying data.

In conclusion, it is evident that data serves as the cornerstone of any AI system. The guidelines presented in the article offer valuable insights for obtaining accurate, meaningful, and reliable data for your upcoming experiments or new product development. Recognizing the pivotal role of data in shaping the performance and outcomes of AI models, the provided strategies underscore the importance of meticulous planning, ethical sourcing, and thorough documentation.

In essence, the article provides a roadmap for practitioners to not only gather data effectively but also to enhance the overall integrity and reliability of their AI systems. Implementing these guidelines can lead to more accurate predictions, improved user experiences, and ultimately, the successful deployment of AI-driven solutions.