With a brilliant digital innovation in the Redis server, managing ‘big data’ like Artificial Intelligence and Machine Learning is becoming convenient. Most enterprises and developers prefer the Redis server and its various applications for their core operations. The Redis server is a state-of-the-art NoSQL database system that doubles as a key-value session storage system and an excellent data caching technology in the form of the Redis cache. Using its system cache, the Redis server is implying tremendous cached data meaning for modern applications. Apart from innovations like the Azure Redis Cache, which provides an in-memory data store, several other applications are being worked upon the open-source Redis software. Developers feel that these innovations will introduce groundbreaking technologies for the digital community based on the open-source Redis software.
AI interference and the AI-Interference engine- A brief introduction:
While a general-purpose CPU would take days on end to complete a single cycle of model training, the GPU technology can train deep-learning models exceptionally faster. This knowledge gave rise to the AI boom. Since then, GPU manufacturers like Intel, NVIDIA, and others have improvised and created their AI-optimized GPUs to improve the accuracy and predictions of training models as a part of AI development. With enterprises and startups seriously contemplating involving AI from a research and scientific phase to deal with real-world-based problems and their applications, Machine Learning and Deep-Learning are being moved to production. It has also paved the way for introducing numerous approaches that manage the Machine Learning pipeline lifecycle.
Among these, AI serving refers to an extremely crucial step in the Machine Learning pipeline at a very high phase. It is generally managed and performed by AI interference engines. A floodgate of opportunities has opened with AI-interference engines being responsible for the model deployment and performance monitoring in the Machine Learning pipeline. They determine whether applications can employ AI technologies to enhance their operational efficiencies and solve business problems in the real world.
The challenges that exist in the construction of AI interference engines meant for real-time applications:
The introduction of the RedisAI- an innovation established with the primary Redis server, has led the Redis Enterprise customers to comprehend the challenges that exist in AI production and its architectural requirements.
● Swift end-to-end interferencing and serving:
People are strongly relying on the Redis server’s engine for instant-experience applications. Therefore, they mention that the addition of AI functionality to the stack has a negligible effect on application performance.
● Zero downtime:
Since every transaction involves some level of AI processing, maintaining the same level of Service-level agreements for mission-critical applications using mechanisms like auto-cluster recovery, data persistence, replication, active-active geo-distribution, and periodic backups become immensely essential.
Applications are often constructed to serve peak usage. They need immense flexibility to scale-out or scale-in the respective AI-interference engines depending on the expected and actual load in such cases.
● Multiple-platform support:
The AI interference engines are required to serve deep-learning models for cutting-edge platforms like PyTorch or TensorFlow and Machine-Learning models like random-forest and linear-regression that offer good predictability in use cases.
● Ease in the deployment of the new models:
Updating a model must be done frequently according to market trends and should not hinder application performance.
● Performance monitoring and retraining:
An AI interference engine should monitor the model and its performance against default models by integrating A/B Testing.
● Ease in deployment at multiple locations:
AI interference engines should have the flexibility to get established and train and serve or interfere at multiple locations. These locations could be the vendor’s cloud, multiple clouds, hybrid clouds, on-premises, or edges.
How can the Redis server solve the complications and challenges in AI-Interference?
According to the Redis server developers, the primary motivation of using a storage caching technology was to serve customers in solving complex problems and computations in the order of milliseconds. They have also outlined several ways to achieve fast end-to-end AI interference/serving.
● AI interference chipsets:
Usage of highly optimized AI interference chipsets will accelerate interference processing such as AR/VR, audio, video, etc. by enhancing the processor’s memory bandwidth and parallelism.
● In-memory session storage:
Running the AI-interference engine in the database stores most of the reference data of a latency-sensitive application. Achieving low-latency AI interference requirements requires the reference data to be stored in memory. That is where the brilliance of a data caching system like the Redis server comes into play.
● Serverless platform integrated with the database:
A purpose-built, serverless platform that runs in the database where the AI interference engine is deployed could be an effective solution.
Solving the problems of AI in-production requires immaculate architectural decisions for latency-sensitive applications to integrate the AI capabilities in each transaction flow. Developers feel that these critical decisions will be easy to implement over Redis server’s applications because of its ease in scalability, replications, data persistence, support of multiple data models, etc. Also, the presence of the cutting-edge Redis cache meant for data caching will significantly reduce the runtime for bringing the reference data to the AI interference engine for AI-processing.