Introduction
In the recent past, generative AI has been the most revolutionising technological advancement that the world has witnessed. With almost every organisation adopting GenAI applications across every industry, it is important to understand the journey that an organisation takes from PoC to production.
PoC, or Proof of Concept, is a process in which organisations experiment with GenAI and evaluate its feasibility for specific use cases in an organisation.
Where PoC is conducted in a controlled environment with limited variables, the major issue arises when the PoC is to be converted into production in a dynamic environment. It is during the production stage that many GenAI application experiments fail to provide the expected results.
Therefore, it is important for organisations to master GenAI transition from PoC to production to ensure that the application is feasible throughout the industry.
In this blog, we will understand some major pointers to be considered when moving from PoC to production, along with important rollout techniques and implementation strategies that help in mastering the GenAI transition from PoC to production.
Moving GenAI from PoC to Production
Some of the important steps to consider when moving GenAI from PoC to production include:
Understanding the Gap Between PoC and Production
The first and most important step when moving from PoC to production is to understand the gap between these two stages.
Where PoC is considered to be a small-scale, controlled experiment, production includes deploying the GenAI solution at scale and in a dynamic landscape that can be across departments or geographies.
While transitioning from PoC to production, it is important for organisations to be aware of various challenges that may arise, including ensuring data consistency, managing increased computational requirements, and maintaining model accuracy over time.
Data Management and Scalability
As data is considered to be the backbone of every AI system, it is essential to effectively manage data when transitioning from PoC to production. This involves many crucial steps, including scaling the data infrastructure and ensuring data quality, consistency, and security. This is because, during production, AI models often require real-time data processing, which has the power to strain existing organisational systems.
Therefore, it is critical that organisations invest in strong data pipelines that have the ability to handle large volumes of data without compromising their performance. Additionally, it is also important to ensure data privacy and compliance with required regulations, such as GDPR.
Further, a well-planned data management strategy is essential for an organisation to mitigate risks and ensure the smooth operation of GenAI in production.
Model Optimisation and Maintenance
When AI models are in the PoC stage, they are typically optimised for specific tasks within a controlled environment. However, once the models reach the production stage, they are required to perform reliably across a range of scenarios.
To ensure that the model remains accurate and relevant, organisations must ensure continuous monitoring and maintenance of the model.
One of the most common challenges during the production stage is model drift, wherein the performance of the model degrades over time due to changes in the input data. To address this challenge, organisations must implement mechanisms for regular model retraining and validation in order to maintain high performance. This may involve setting up automated systems for model monitoring and updates.
GenAI Production Rollout Techniques
Some of the rollout techniques that can be implemented to ensure a successful GenAI transition from PoC to production include the following:
Phased Rollout Approach
Phased rollout approach is a technique wherein the GenAI solution is gradually introduced to different parts of the organisation. As a result, this approach allows the organisation to test and refine the AI solution in a controlled manner, leading to reduced risk of widespread disruptions.
In a phased rollout approach, the GenAI solution is first deployed in a single department or use case. Once all the lessons are learnt from the single department or use case, the appropriate solutions are found and applied, and then the AI model’s deployment is expanded to other areas.
As a result of this incremental approach, organisations are able to gain valuable insights into the performance of the AI model in a real-world environment and are able to perform required adjustments before full-scale deployment of the AI model.
Pilot Programs
Another rollout technique is the pilot programs. Pilot programs are considered to be a small-scale implementation of the GenAI solution within a specific environment or group.Â
Pilot programs are considered to be an intermediate step between the transition of an AI model from PoC to production that allows organisations to test the solution’s functionality, usability, and integration with existing systems.
The various insights that the organisation gains from pilot programs can inform broader deployment strategies and help in the identification of potential challenges. Further, pilot programs provide organisations an opportunity to gather valuable user feedback and make the necessary adjustments required to improve the GenAI model before it is rolled out and deployed on a larger scale.
Parallel Run
In the rollout technique known as parallel run, the GenAI solution operates alongside the existing system for a period of time. As a result, the organisations are able to compare the performance of the new AI systems with the organisation’s legacy systems to ensure that the AI system is functioning as expected and as required.
During the parallel run rollout technique, any discrepancies that are found between the two systems can be identified and addressed, minimising the risk of errors when the GenAI system is fully deployed and operational.
Parallel runs are considered to be an important rollout technique that is particularly useful in critical operations where uninterrupted service is essential.Â
GenAI Implementation Strategies
The various GenAI implementation strategies that can be used for the successful transition of GenAI from PoC to production include the following:
Agile Implementation
One of the methodologies that are well-suited for implementing GenAI solutions is agile methodologies as agile methodologies promote flexibility, collaboration, and iterative improvement.
Agile implementation includes breaking down the entire project into smaller and manageable tasks, with frequent reviews and adjustments that are based on user feedback.
As a result, this approach allows organisations to quickly adapt to changes in the project scope, user requirements, or technological advancements.
With the help of agile strategies, organisations are able to accelerate the development and deployment of GenAI solutions while maintaining a high level of quality and user satisfaction.
Cloud-Native Development
Cloud-native development is an implementation strategy that uses cloud computing resources for the development and deployment of GenAI solutions. As a result, this approach has many advantages, such as scalability, flexibility, and cost-effectiveness.
With the help of cloud-native development, organisations are able to save up large upfront investments in infrastructure by allowing organisations to quickly scale AI models up or down as required (according to the demand of the organisation).
Further, as cloud AI platforms provide organisations with access to advanced AI tools and services, the organisation is able to develop and deploy GenAI solutions more quickly and efficiently.
When an organisation adopts a cloud-native strategy, it ensures that the GenAI solution becomes scalable, resilient, and capable of meeting future demands of the organisation.
Continuous Integration and Continuous Deployment (CI/CD)
Another implementation strategy that must be used by organisations for a seamless transition from PoC to production is a continuous integration and continuous deployment (CI/CD). CI/CD pipelines help organisations to automate the process of integrating code changes, testing, and deploying GenAI models into production.
As a result of this strategy, organisations ensure that the updates are delivered quickly and reliably, leading to reduced time between development and production.Â
Additionally, with the implementation of CI/CD pipelines, organisations are also able to streamline the entire deployment process, reduce the risk of errors, and ensure that the GenAI model and solution remains updated with all the latest advancements.
Further, by improving the collaboration between development and operations teams, CI/CD pipelines also help to improve the overall quality of the AI solution.
Conclusion
It is extremely important for organisations to have a seamless GenAI transition from PoC to production to ensure that they fully utilise the potential of GenAI solutions. The journey from PoC to production is often extremely challenging for organisations, but once completed, it is also extremely rewarding.
The process of the transition from PoC to production requires an organisation to undertake careful planning, collaboration, and continuous improvement.
We at CrossML help organisations implement and integrate GenAI solutions into their existing systems and workflows as a result of successful production deployment of our GenAI solutions. Such successful deployment helps our clients to achieve success in their industries by automating and streamlining their workflows.
To gain further knowledge on the importance of the seamless transition of GenAI models from PoC to production, you can also watch our webinar: Taking GenAI and LLMs from POCs to Production.
FAQs
The key stages of the GenAI transition include proof of concept (PoC), data management, model training, testing, infrastructure planning, governance, rollout, and post-rollout evaluation. With the help of each of these stages, organisations ensure their readiness for production deployment.Â
In order to achieve effective transition from PoC to production with GenAI, organisations must focus on scalable infrastructure, strong data management, rigorous testing, and continuous monitoring. Further, organisations must also adopt agile methodologies, involve cross-disciplinary teams, and ensure compliance with required regulatory governance and ethical standards.
The best practices for mastering GenAI transition include implementing agile development, using cloud-native solutions, adopting CI/CD pipelines, integrating Human-in-the-Loop, enforcing an AI framework that is ethical, and effectively managing change to ensure smooth transition.
The various challenges that organisations can face in GenAI transition include data quality issues, scalability concerns, model drift, regulatory compliance, user adoption, and resistance to change.
To ensure a successful transition to production with GenAI, organisations must ensure strong governance, continuous monitoring, iterative improvement, and post-rollout evaluations. It is also important for organisations to engage stakeholders, provide adequate training, and maintain flexibility to adapt to evolving requirements.
