3 Things to Know Before Starting Your AI Journey

Implement the Right Methodology

Assuming a solid business case has been established, an effective methodology for AI projects begins with Data Acquisition and Preparation. Data acquisition entails establishing data availability, connectivity, and security, whereas data preparation involves steps such as cleaning, normalizing, labeling, and enriching. Administrative tool support for this step is something to look for when considering an Insight Engine for your AI project. The next step is Model Building, which entails model selection, hyperparameter tuning, and feature selection. Again, adequate tool support is essential to make this as simple and straightforward to execute as possible. The third step is Deployment & Validation, which involves frequent model re-training and application, systematic validation of output quality, and integration of the model’s prediction within third-party systems. The final step is Active Learning & Tuning, which involves providing labeling tools to subject matter experts, managing training sets and test sets, mitigating bias and edge cases, as well as setting up model versioning and lifecycle support.

Use a Proven and Complete ?Technology Platform

Whatever technology is chosen to support your AI projects should have been forged by experience with other organizations with similar goals and complex projects. Specifically, the technology should provide hardened capabilities such as enterprise-grade security, extensive linguistic and natural language processing capabilities, integrated machine learning, and well-designed user experience.

In order to realize value quickly without sacrificing context or quality, the various technology components must be pre-integrated and cohesively designed to form a unified, end-to-end solution. By adopting a unified solution, organizations shield themselves from the cost and complexity of IT integration. They also benefit from a development paradigm that enables implementation and administration through configuration, scalable to even the most complex environments. Adopting a complete solution also means these organizations do not have to purchase a point solution whenever new business requirements arise. Instead, they can leverage a unified, end-to-end platform to configure applications quickly that are specifically aligned with business goals.

Conclusion

As many organizations have experienced, adhering to these best practices helps move projects that use AI technology from the experimental phase into production and greatly improves the odds of achieving the predicted return on investment.