The hottest buzzword of 2023, ‘artificial intelligence,’ persists as an extremely broad term with near-infinite interpretations and possibilities. Despite the attention, most AI algorithm development processes remain shrouded in mystery.

At Rank One Computing (, we build smarter, faster solutions for the world’s most pressing challenges, with greater levels of transparency. As machine learning continues to dominate news cycles, we share a special look behind the scenes at how deep learning algorithms are created and integrated into practical applications.

At their core, artificial intelligence (AI) and machine learning (ML) algorithms train on vast amounts of data to inform better decisions, predict likely outcomes, and even create new data or content. 

Analytic AI algorithms – like those made here at – are trained to compare new data against statistical models trained on large volumes of existing data in order to authenticate, examine, match, predict, identify outliers, or measure traits.

Generative AI algorithms – like ChatGPT, Grammarly, and Midjourney – are trained to produce new multimedia content within the parameters of models trained on existing media, in order to execute user-provided scenarios or ‘prompts.’

In all types of AI, the source and treatment of these large training datasets are critical to the quality and performance of the resulting AI algorithms. While generative AI tools continue to raise questions, concerns, and even bans across the country,’s computer vision and biometric analytic algorithms continue to raise global standards for accuracy, efficiency, and ethical use across modalities – including face recognition, fingerprint matching, object detection, license plate recognition, and more.

So how are these game-changing algorithms developed and refined over time? In this article, gain insight and transparency on the four key steps – data development, algorithm development, algorithm integration, and customer support.

Step 1: Data Development – The Foundation of AI Algorithms

The process begins with data development, a crucial step in creating next-generation deep learning models. These models require vast amounts of labeled training data to learn model parameters that generalize across a range of operational use-cases and conditions. Though plenty of source data exists, ethical data sourcing is essential to ensure unbiased algorithms. Human data validation of collected data plays a critical role in minimizing algorithmic biases and improving overall accuracy. By carefully curating and validating the training data, developers establish a solid foundation for each algorithm.

Step 2: Algorithm Development – Unleashing the Power of Patterns

Training high-quality algorithms requires deep knowledge of statistical pattern recognition principles (as captured in the seminal textbook Pattern Classification), specialized hardware infrastructure, and powerful Graphics Processing Units (GPUs). The training of highly accurate algorithms can be a time-consuming endeavor, often spanning days, weeks, or even months. After a candidate algorithm is trained, test and evaluation datasets measure both absolute accuracy in real-world use cases, and relative accuracy through comparing the performance of different algorithms (e.g., prior releases to ensure an improvement is being delivered.)

Step 3: Algorithm Integration – Bridging the Gap Between Research and Deployment

Once the algorithms have been developed, the next step is software integration. This phase entails porting the trained computer vision models into deployable software libraries (like ROC SDK) and systems that can operate across various hardware architectures and software operating systems. These software libraries require careful creation of APIs (Application Programming Interfaces) to ensure software developers using the models embedded in these libraries can easily build fault tolerant systems.

Once the integration process is complete, we conduct extensive testing to validate the integration process and ensure that the computer vision models perform consistently in their integrated form with how they performed in the algorithm development environment. 

Step 4: Customer Support – Enabling Seamless Communication and Feedback

Integrators and customers of deployed algorithms play a pivotal role in the final step of the process: customer support. We build and maintain direct communication channels with integrators to provide feedback on the algorithm’s performance “in the field.” Such feedback can often include data exhibiting a specific set of conditions. This feedback and data can significantly impact future research cycles and aid in continuous improvement. 

Looking Beyond the Buzz

As the buzz around AI continues to build, it’s critical to understand that not all AI is created equal – meticulous development cycles and data collection methods make all the difference.  From data cultivation through testing, integration, and customer support, each step plays a vital role in bringing these cutting-edge technologies to life. As the demand for intelligent computer vision solutions continues to rise, single-source vendors like will lead the charge, driving innovation and revolutionizing industries with expertise in this rapidly evolving field.

Ready to ROC? Let’s Talk.

Rank One Computing builds faster, more accurate, reliable tools for a safer, smarter world. Our American-made biometric and computer vision algorithms are trusted by public safety and security leaders nationwide to protect critical public and private infrastructure. Reach out to learn more about how our industry-leading technology can enhance security operations for your organization.

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