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United States 
What is AI Model Deployment?
Artificial Intelligence (AI) models are computational algorithms capable of identifying and generalizing patterns, trends and representations from input data and form the building blocks of AI/ML systems. They serve as necessary tools for business growth; thus, their seamless deployment in a real-world scenario becomes crucial for extracting their true value and generating impactful events. Model deployment is the process of transitioning an AI model from the initial development phase to a functional state in a working environment. The model is trained using huge datasets to develop predictive capabilities to be operational for the end users and systems in real-world applications.
AI development services can integrate models into existing software systems to receive batch-wise data from the input stream, source or requests. It further processes this data via algorithms to build outputs such as classifications or predictions. This helps in evaluating the model’s functioning and application while enabling businesses to innovate, increase efficiency and automate decision-making. This process is crucial for realizing the value of machine learning models, as it enables organizations to automate decision-making, enhance efficiency, and drive innovation across various domains, from healthcare and finance to e-commerce and autonomous vehicles.
The global AI as a service market was approximately valued at US $12.7 billion in 2024 and is expected to increase at a CAGR of 30.6% till 2034. Furthermore, the global AI consulting market was valued at US $16.4 billion as of 2024 and is predicted to grow to US $257.6 by 2033, surging at a CAGR of 35.8%. By understanding the strategies to deploy AI model, businesses can be equipped to maximize AI initiatives and manage related complexities of the same. Let us go through this blog that deals with the deployment of AI models and how the transition from model prototyping to production environment occurs.
Growing market size of AI and NLP model deployment during the period 2022 to 2034
Strategies of AI Model Deployment
Inference refers to the use of a trained model to make predictions using new data (vs. historical/training data), as compared to model deployment where the trained model makes predictions in a new production environment. Businesses can choose between batch, real-time, or streaming inferences as per performance metrics including latency needs, cost, model speed, user value, and infrastructure demands. Let us now delve into the different types of deployment –
Shadow Deployment
This AI model deployment technique includes developing a model replica in an existing production environment while not disturbing live traffic. It does not serve any predictions but records the observations to evaluate performance of the new model version in a controlled environment involving no risks prior to complete deployment commit. This helps in pattern recognition and machine learning, identifying discrepancies, assessing issues, impact of changes, effectiveness and transition method of new models, regressions introduced and potential performance improvement.
Blue/Green Deployment
A deployment method to reduce downtime and risks of software development services or updates in systems such as stock monitoring systems. Simultaneously two production environments are maintained, one is the active one called ‘blue’ and the other is inactive and ready to be rolled back or updated, called ‘green’ or vice versa. The initial system version is deployed, verified and validated in the ‘green’ environment, after which traffic is routed to the ‘blue’ environment for ease of transition. Risks are minimized during deployment, model versioning is made possible, and updates in production services are easy to roll out, as traffic can be redirected to ‘blue’ to address issues or degradation.
Multi-Armed Bandits
It features a dynamic testing approach to manage and optimize model selection for delivering predictions and classifications by storing, accessing and comparing results in real-time, in lieu of tuning model serving configurations manually or static allocation strategies. This type of NLP model deployment uses different inferences and dynamic traffic allocation through performance (data distribution, data drift, client behavior) or inference request-based model versioning to maximize accuracy, client satisfaction and latency. Every input feature request needs to be synced to a query tool like Snowflake or data lake with pre-inference, output prediction and other variables automatically, as it affects the traffic strategy.
Continuous Integration/Continuous Delivery
CI/CD pipelines showcase automatic building, evaluation, testing, validation and deployment of changes in models such as in architectures and hyperparameters, during fine tuning in a controlled environment. Platforms like GitHub Actions reduce errors, manual intervention and time-to-market, while allowing seamless integration with version control systems such as in AI solutions in retail for continuous monitoring of the model’s performance.
Steps of AI Model Deployment
The model deployment process includes various aspects such as selection of the model, preparation of data, selection of deployment platforms, containerization, API development, security, application development and maintenance etc. Once deployed, these models assist machines to predict and adapt to changing environments. Given below are the key steps for deploying AI models –
Model & Environment Preparation
Prior to model deployment, the initial step is to prepare the model and the environment it will be deployed in. This step includes marshalling the trained data into a format which is simple to load, transport and store in multiple deployment environments. Through this process, architecture, metadata and parameters of the model get serialized to ensure device or platform agnostic portability and compatibility. The environment should feature scalability and favorable performance in terms of input data volume, computer power optimal for the model, compliance standards. This may include cloud platforms, edge devices or on premises servers as per the AI/ML model’s operational requirements.
API Designing & Model Deployment
Post initial foundational steps to deploy AI model, the next steps involve model integration and gaining inference in real-time by making the model accessible. An application programming interface acts as a bridge between the software application and the model for to and fro relay of input data and response predictions. Therefore, once the model and environment are selected, API is designed using application-compatible styles like RESTful and GraphQL.
This involves defining the response formats, requests, end points, authentication strategies and error-handling mechanisms. Thereafter, the API and the model are deployed on the selected environment by configuring networking, deciding on the infrastructure and ensuring computability between both. By using containerization techniques like Docker, LXC etc. for packaging both into portable containers and scaling the deployment with orchestration platforms like Kubernetes, Apache Airflow etc., developers can assure continuous integration into software applications.
Various types of inferences when businesses deploy AI model
Security & Performance Monitoring
The model’s performance must be monitored through key metrics like accuracy, prediction latency and throughput in real-time to identify any anomalies or issues. This is necessary to assure the model meets the standards mentioned in the service level agreements, mark performance bottlenecks and optimize resource allocation. API endpoints are secured to protect the model and the data processed through authentication-based access for users or applications. Hypertext Transfer Protocol Secure ensures communication channel encryption for safeguarding the data transferred between model and the client side. Moreover, data privacy and compliance with GDPR and HIPAA regulations are a must to mitigate legal issues and risks related to data breaches to maintain confidentiality and integrity.
Maintenance & Documentation
Businesses need to document the NLP model deployment processes to utilize the same effectively, as it provides precise instructions for troubleshooting, loading, tackling issues etc. These may include practices, code snippets, real-life examples and more for the ease of integration into process workflows. Once deployed, the model’s performance needs to be monitored and maintained through version and dependency control, also it should be updated for reliability, effectiveness to provide accurate predictions and to avoid model drift. Apart from tracking changes, collecting user feedback is necessary to address arising issues, requests and gain maximum value from the model to obtain desired outcomes.
Best Practices in AI Model Deployment
Development and deployment of these models is complex and requires many resources, while their environmental integration is quite risky. Listed below are a few challenges associated with deployment of AI models.
1. Privacy: It is of very much importance to maintain security and privacy whenever we deploy AI model. They must adhere to compliances such as HIPAA, GDPR etc., encrypting data during transit and implementing authorization mechanisms.
2. Management: Maintaining and managing various versions of the dependencies or model versioning is necessary to track changes, allow reproducibility and facilitate rollback.
3. Scalability: The model should be able to handle workloads as per demands without trading off in terms of reliance and performance.
4. Optimization: The performance needs to be monitored and optimized over time to meet service level agreement standards and facilitate responsiveness.
5. Data Drift: Models are trained with the underlying historical data which may become outdated and less accurate when deployed in the scenario. Thus, it needs to be updated with new data periodically to maintain performance.
6. Compatibility: Compatibility between the infrastructure of the environment selected such as networking configurations, hardware, software dependencies etc. and the deployed model, needs to be ensured at all times.
Ease AI Model Deployment with KritiKal
In this blog, we saw various approaches to deploying AI models as well as the steps involved such as environment preparation, API designing etc. to ensure user satisfaction, performance, scalability, security and reliability. KritiKal can assist you in NLP model deployment, automating retraining pipelines and effective infrastructure planning by fostering cross-functional communications and best practices. We help you unlock data-driven insights, boost innovation and drive informed business outcomes to future AI initiatives. Please get in touch with us at sales@kritikalsolutions.com to realize your AI-powered applications and requirements.
Harshita Singh currently works as a Senior Software Engineer at KritiKal Solutions. She is proficiently skilled in software development, designing, developing and implementing SAP UI5/Fiori applications, and more. With her ability to work efficiently in teams and extensive experience of working with software development, she has assisted KritiKal in delivering various projects to some major clients and enhancing user experience.