As AI and ML gets integrated into the various enterprise workflows the demand for shifting from batch processing to real-time data streaming simultaneously burgeons. AI/ML models can process large amount of data at once, also the validity of an insight generated by a model is directly correlated to the freshness of data. In this article, we will explore why it is important to move towards providing high quality data to an AI model from the traditional batch processing leveraging real-time data streaming.
How Real-Time Streaming Data Supercharges AI/ML
In today’s fast-paced world, getting insights based on stale data and taking decisions can cost enterprises very dearly. That’s where the fusion of streaming data and AI/ML comes in, offering a powerful advantage, especially in workflows where every second counts. By applying AI and machine learning to streaming data, organizations can react instantly, predict accurately, and optimize their model output continuously. Here’s a glimpse into how real-time data streaming in AI models is transforming industries:
Elevated Customer Experiences
Personalization is today’s key to customer success story in the eCommerce world. By analyzing customer interactions in real-time – every click, every call, every social media post – AI-powered systems can create models of customer preferences and provide personalized recommendations and support. AI chatbots that are able to analyze a customers prompt in natural language and offer real-time responses evokes empathy and human touch making the customer feel talking to an attentive and smart agent.
Predictive Maintenance and Operational Efficiency
Downtime is the enemy of productivity. Today sectors like manufacturing and energy leverage AI/ML algorithms alongside IoT sensors for predictive maintenance and detection of anomalies. An automotive plant, for instance, might monitor thousands of assembly line sensors, tracking metrics such as temperature, vibration, and performance. To track anomalies or pinpoint bottlenecks it is essential to be able to extract real-time data continuously and feed into an ML model trained for this goal. Operators in turn can proactively introduce necessary changes for operational efficiency or schedule maintenance of an equipment showing signs of breakdown.
Real-Time Fraud Prevention and Algorithmic Trading
In the financial world, milliseconds matter. By analyzing market data and transaction streams in real-time, ML algorithms drives trading strategies that capitalize on fleeting market opportunities, optimizing returns. AI/ML algorithms can also detect and prevent fraudulent activities, minimizing financial losses. Credit card companies rely on streaming data for fraud detection. Streaming data platforms allow these companies to analyze thousands of transactions per second to detect unusual activity and flag or block suspicious transactions.
Smarter Supply Chains and Logistics
The supply chain and transportation industries can leverage real-time data on traffic, weather, and vehicle locations to optimize routes, reduce fuel consumption, and ensure timely deliveries. A logistics provider can integrate real-time data from thousands of vehicles with weather and traffic datasets. Stream processors can then enable automated route optimization with minimal latency to help drivers avoid delays.
The 360-Degree Customer View
Inventories for ecommerce businesses are not being updated by ERP rather based on real-time customer trends. By integrating real-time data from diverse sources – phone calls, emails, texts, social media, point-of-sale systems, and even geolocation data – businesses can create a comprehensive, up-to-the-minute view of each customer. This 360-degree view empowers businesses to make informed decisions and deliver personalized experiences that drive loyalty and growth.
Travel Booking Systems
A chatbot designed to assist travelers with flight and hotel reservations requires access to real-time data sources to provide optimal service. This includes live updates on airline seat availability, current flight statuses, and hotel room inventories, as well as the latest pricing information. Traditional batch processing methods are inadequate in this context; the rapid changes in availability and pricing necessitate a system capable of continuous data ingestion and processing. By leveraging real-time data streams, the chatbot can adapt dynamically to fluctuations in the market, offering users the most current and relevant options and thereby improving the overall customer experience.
AI-Driven Systems Monitoring
In the realm of IT infrastructure management, AI-driven observability and monitoring solutions rely on the continuous analysis of internal metrics to detect anomalies and generate alerts. The value of such alerts is intrinsically linked to their timeliness. An AI model that detects an abnormal system metric must generate an immediate notification to operators. Delays in notification render the insight less actionable and potentially valueless. Real-time data streaming ensures that critical metrics are analyzed and communicated to operators with minimal latency, enabling prompt intervention and preventing potential system disruptions.
Above are just a few examples of how streaming data is revolutionizing industries when combined with the power of AI/ML. As data continues to flow at ever-increasing rates, the ability to process and analyze it in real-time will become even more critical for organizations looking to gain a competitive edge.
Importance of Real-Time Data Streaming in Generative AI
Real-time data streaming is increasingly recognized as a foundational element for Generative Artificial Intelligence (GenAI) applications. The capacity to process and analyze data as it is generated, rather than relying on batched or historical information, significantly enhances a GenAI model’s ability to adapt and produce outputs that are both accurate and contextually relevant. This dynamic integration enables Large Language Models (LLMs) to respond effectively to rapidly changing conditions, improving adaptability and performance across a range of tasks.
In this section, we will check out how real-time data streaming supports some core generative AI functions:
Enhancing LLM Accuracy with In-Context Learning
LLMs, while powerful, are inherently limited by their training data, which represents a snapshot in time. Unlike humans, they cannot inherently access or incorporate new information at the point of inference. Consequently, continuously fine-tuning or retraining LLMs to accommodate new data is often necessary. However, this approach is both resource-intensive and impractical, as the rate of new data generation frequently surpasses the speed at which models can be effectively updated. Moreover, LLMs are prone to generating responses that, while syntactically correct and coherent, may be factually inaccurate – a phenomenon often referred to as “hallucination.” This stems from their reliance solely on training data and a lack of real-time contextual understanding.
To mitigate these limitations, LLMs can leverage a technique known as “in-context learning”. This approach enables the model to learn from the data it receives as part of the input prompt, allowing it to generate more accurate and contextually relevant responses without requiring modifications to the model’s underlying weights. In-context learning can be effectively used to produce personalized answers or to ensure responses align with specific organizational policies.
For example, consider a chatbot application designed to provide users with information about flight and hotel availability. Data events related to real-time inventory and pricing changes can be continuously ingested into a streaming storage engine. A stream processor then filters, enriches, and transforms these data events into a consumable format, creating a dynamically updating snapshot. By querying this snapshot and incorporating the resulting information into the user prompt, the LLM receives the most current data in context. This enables the model to adapt to the latest changes in price and availability, providing users with accurate and up-to-date information. In essence, in-context learning bridges the gap between the LLM’s static knowledge base and the dynamic nature of real-world data.
Enhancing Generative AI with Near Real-Time Customer Profile Updates
Retrieval Augmented Generation (RAG)-based GenAI applications, while capable of generating responses based on their training data and the knowledge base, are often limited in their ability to provide truly personalized, near real-time interactions. This limitation becomes particularly apparent when an application is expected to consider a user’s specific circumstances, such as current bookings, available inventory, or individual preferences. Moreover, the unified customer profile – the comprehensive collection of data relevant to a particular user – is subject to frequent change. A reliance on batch processing to update the GenAI’s user profile database can result in outdated information, leading to customer dissatisfaction.
To address this challenge, consider the application of stream processing to enhance a RAG solution, enabling real-time access to unified customer profiles and organizational knowledge.
In most organizations, customer records are distributed across a variety of data stores. For a GenAI application to provide a relevant, accurate, and up-to-date customer profile, it is crucial to establish streaming data pipelines capable of performing identity resolution and profile aggregation across these disparate sources. Streaming jobs continuously ingest new data to synchronize systems and can efficiently perform enrichment, transformations, joins, and aggregations across defined time windows.
Change Data Capture (CDC) events, which detail source record updates and associated metadata, play a critical role in this process. CDC events contain information about the origin of the data, the type of change (insert, update, or delete), the timestamp, and other pertinent metadata. By leveraging CDC events within a streaming data pipeline, a RAG-based GenAI application can maintain a near real-time understanding of individual customer profiles, enabling more personalized and effective interactions.
Maintaining an Up-to-Date Organizational Knowledge Base for Generative AI
Similar to customer data, an organization’s internal knowledge base – encompassing company policies, procedural documentation, and other crucial information – is often dispersed across various storage systems. This data is typically unstructured and subject to non-incremental updates. To effectively leverage unstructured data in AI applications, vector embeddings are employed. This technique represents high-dimensional data, such as text files, images, and audio, as multi-dimensional numerical vectors.
While a batch process can initially convert the knowledge base content to vector data and store it in a vector database, this approach necessitates periodic reprocessing of documents to synchronize the vector database with changes in the knowledge base. AWS offers several vector engine services, including Amazon OpenSearch Serverless, Amazon Kendra, and Amazon Aurora PostgreSQL-Compatible Edition with the pgvector extension, which facilitate the storage and retrieval of vector embeddings.
With a large number of documents, this interval-based reprocessing can be inefficient. During the periods between reprocessing cycles, users of the GenAI application may receive responses based on outdated information, potentially leading to inaccurate or irrelevant answers.
Stream processing presents an effective solution to these challenges. A stream processing system can generate events based on existing documents initially and subsequently monitor the source systems for changes, creating a document change event as soon as they occur. These events can be stored in a streaming storage system and processed by a dedicated streaming job. The streaming job reads the change events, loads the content of the modified document, and transforms the content into an array of related word tokens. Each token is then converted into vector data via an API call to an embedding foundation model (FM). Services like Amazon Bedrock and Amazon SageMaker provide access to pre-trained models and enable the creation of private endpoints for this conversion.
Finally, the resulting vector embeddings are stored in the vector storage system via a sink operator, ensuring that the knowledge base remains continuously up-to-date and readily accessible to the generative AI application.
Continuous Improvement Through Feedback Analytics and Fine-Tuning
To ensure the ongoing effectiveness and user satisfaction of a generative AI application, it is essential for data operations managers and AI/ML developers to gain comprehensive insights into its performance and the underlying foundation models (FMs). This requires establishing robust data pipelines that calculate key performance indicators (KPIs) based on user feedback, application logs, and relevant metrics. This information provides stakeholders with real-time insights into FM performance, application efficacy, and overall user satisfaction. Furthermore, collecting and storing the complete conversation history enables the continuous fine-tuning of FMs, enhancing their proficiency in domain-specific tasks.
This continuous improvement loop is ideally suited to a streaming analytics approach. The application should be designed to store each conversation in a streaming storage system. To gather user feedback, the application can prompt users to rate the accuracy of each response and their overall satisfaction, utilizing formats such as binary choices or free-form text. This feedback data can then be ingested into a Kinesis data stream or a Managed Streaming for Apache Kafka (MSK) topic and processed in real-time to generate relevant KPIs.
Sentiment analysis on user interactions can also be performed leveraging FMs. By analyzing each response and assigning a user satisfaction category, FMs can provide valuable insights into the overall user experience, further informing the fine-tuning process. This holistic approach to feedback analytics enables continuous optimization, ensuring that the GenAI application remains effective, relevant, and aligned with user needs.
Conclusion : Real-Time AI/ML: A Strategic Imperative for User-Centric Applications
The capacity to deliver real-time predictions within user-facing applications is no longer a mere advantage but an increasingly critical requirement. As AI/ML models are deployed closer to end-users, the ability to incorporate real-time context from streaming data into the decision-making process is becoming a strategic differentiator. Data streaming platforms provide the essential infrastructure and support, empowering AI/ML developers to efficiently leverage and adapt existing models for effective deployment in production environments. This agility and responsiveness is paramount to creating superior user experiences and maintaining a competitive edge.
