Introduction
Generative AI has revolutionised the entire digital landscape through its numerous capabilities of making the digital space easy to operate as well as use.
With generative AI, organisations are limited as far as their imagination, as they can make GenAI models perform every function that they deem possible with the correct algorithms and training data.
It is one thing conceptualising an application and a whole different thing productionizing that application. Therefore, it is very challenging to convert a large language model (LLM) or generative AI application from proof of concept (POC) to production.
In this blog, we will discuss about productionizing generative AI solutions and its various challenges along with the various solutions that help to overcome the challenges of productionizing generative AI solutions.
Productionizing Generative AI Solutions
Let us begin by understanding the various aspects of productionizing generative AI solutions given below:
Understanding the Transition from POC to Production
As generative AI solutions move from the POC to the production phase, the focus shifts from feasibility to reliability, scalability, and maintainability.
In the POC phase, the organisation focuses on whether the model can achieve the desired outcome which is decided on the basis of experimenting with various different algorithms, data sets, and configurations that help to reach the optimal solution.
As a successful POC is not a guarantee for the successful production of generative AI solutions, the production phase tests the model on many different criteria. These include making sure that the model can operate at scale (scalability), provide a performance that is consistent (reliability), and be maintained over time (maintainability).
Additionally, rigorous testing is carried on to ensure that the model is free from challenges of data privacy, data security and regulatory compliance.
Further, in the production phase, the generative AI model requires a strong architecture that is capable of handling real-world constraints as they need to be integrated with the existing systems and workflows of the organisation.
Model Optimisation and Fine-Tuning
For smoothly productionizing generative AI solutions, it is important to optimise and fine-tune the model for improved efficiency, accuracy, and robustness. Further optimisation and fine-tuning are important, as even though a model may perform well in a controlled POC environment, it might need some adjustments for real-world applications.
Various techniques are used to improve the performance of the model and reduce its complexity, such as hyperparameter tuning, pruning, and quantisation.
Fine-tuning is done on the models on domain-specific data to make sure that the model performs well in the intended application context.
Model optimisation and fine-tuning are extremely important steps in productionising generative AI solutions as they help to improve the relevance and utility of the model in production scenarios and help the model adapt to the required nuances of the target use case.
Data Management and Preprocessing
Without effective data management, the productinizing of generative AI solutions is not possible. Good quality data is required to ensure that the models are trained of accurate data resulting in accurate outcomes.
During the POC stage, the data sets are cleaned and curated manually but it becomes impossible to manually clean data in the production stage. As a result automation and scalability is required during the cleaning of data in the production stage.
This is achieved by developing robust data pipelines that are able to ensure the quality of the data and handle large volumes of data effectively.
Also, various data preprocessing steps such as normalisation, tokenisation and augmentation should be standardised and integrated with the existing workflows.
Further, continuous monitoring of the data pipeline along with its updation is required to ensure that the model adapts to changing data patterns and optimises its performance.
Challenges In Productionizing Generative AI Solutions
Let us now look at some of the challenges in productionizing generative AI solutions and what effects it has on an organisation:
Handling Large-Scale Data
One of the most significant challenges faced in productionizing generative AI solutions is handling large-scale data by the GenAI model.
At the POC stage, the model uses small and well-curated datasets to test the feasibility of the model but at the production stage, the model is required to handle large volumes of data in real-time.
As a result, the organisations need to ensure that the data has high quality, is consistent as well as available which is often a challenging task for the organisation.
Further, organisations must also ensure that the data pipeline is strong enough to handle large amounts of data ingestion, preprocessing and storage in an efficient and effective manner.
Ensuring Model Generalisation
Another challenge faced by productionizing generative AI solutions is to ensure that the models that perform well in the POC environment generalise in the production environment.
It is often seen that GenAI models perform well in a controlled POC environment but fail to generalise to the real-world scenarios.
This is because of the reason that the POC environment often uses only specific datasets to test the model which leads to the lack of diversity and complexity in the dataset which would be present in the production environment.
Dealing with Concept Drift
Concept drift is one of the most challenging issues while productionizing generative AI solutions.
Concept drift leads to the declining performance of a GenAI solution model due to the changes in the statistical properties of the target variable over a period of time.
As the datasets in the production environment keep on evolving on a regular basis due to the dynamic environment, there are often instances of concept drift which needs to be resolved proactively for the optimised working of the generative AI solutions.
Overcoming Challenges In Productionizing GenAI Solutions
Now that you have understood the main aspects of productionizing generative AI solutions alon with the three most impactful challenges that productionizing generative AI solutions face, let us now look at how we can overcome these challenges.
Using Scalable Infrastructure
One of the most common solutions for the challenge of scalability in productionizing generative AI solutions is using a scalable infrastructure that would help to handle large-scale data while ensuring effortless organisational operations.
Organisations can use various Cloud-based solutions, like Google Cloud, AWS or Azure as they provide several flexible and scalable resources. These resources can be customised as per the specific requirements of the generative AI solution.
By using various managed services for various aspects of generative AI solutions like data storage, processing and machine learning, organisations can simplify their infrastructure management while being optimised for scalability.
Implementing Continuous Integration and Deployment (CI/CD)
Another solution to solve the challenges in productionizing generative AI solutions is implementing continuous integration and deployment (CI/CD) practices. As a result, organisations can easily automate their deployment process and ensure that there is effective and efficient delivery of all updates and improvements.
This is achieved by ensuring that the organisation is able to quickly identify as well as address various issues by setting up automated testing, integration and deployment pipelines.
CI/CD practices also helps to improve the performance of GenAI solutions with respect to responding to changing user requirements and feedback as it helps in continuous monitoring and feedback.
Using Transfer Learning and Domain Adaptation
A solution that ensures the success of productionizing generative AI solutions is the effective utilisation of transfer learning and domain adaptation as it ensures model generalisation in the production environment and reduces the need for extensive and repetitive retraining.
With the help of transfer learning, organisations can pre-train their GenAI model on a large and diverse set of data while fine-tuning it on specific domain data.
Through domain adaptation, organisations are able to align the GenAI model to the target domain. This is done by adjusting the parameters of the GenAI model and incorporating it with domain specific knowledge.
As a result, the performance and robustness of the generative AI solutions model is improved significantly for the real-world scenarios.
Conclusion
Productionizing generative AI solutions is a very complex and difficult task, specifically if the POC environment is a controlled environment leading to many challenges.
To ensure the seamless conversion of POCs into Production of generative AI solutions, it is important that organisations overcome these challenges in an effective and efficient manner.
CrossML helps its customers in a seamless transition from the POC to the production stage while helping them to overcome all the probable challenges. This is done as CrossMl provides the clients with appropriate solutions to improve the performance of their generative AI solutions in the real-world production environment.
FAQs
The various challenges in productionizing generative AI solutions are handling large-scale data, ensuring model generalisation, dealing with concept drift, balancing performance and resource constraints, ensuring data privacy and security, achieving regulatory compliance, managing deployment complexity, addressing ethical concerns and ensuring user adoption and trust.
Generative AI solutions can be successfully deployed in production by using scalable infrastructure, implementing CI/CD practices, using fine-tuning and domain adaptation to optimise the GenAI solution models, establishing robust data pipelines, setting up continuous monitoring systems and ensuring regulatory compliance.
Steps involved in overcoming challenges of productionizing generative AI solutions include fine-tuning and optimising the generative AI solution model, automating data management and preprocessing, using performance and scalability optimisation, integrating the model with the existing systems and workflows of the organisation and implementing continuous monitoring and improvement.
Best practices for scaling generative AI solutions in production include using cloud-based and scalable infrastructure, using parallel processing and distributed computing techniques, implementing CI/CD pipelines, optimising generative AI solutions models and monitoring performance and adjusting resources and the model accordingly.
