In an era where seconds can dictate the success of a user's digital experience, the pursuit of minimal latency is more than just a
technical challenge—it's a critical business imperative. At CarWiz, as the Co-founder & CTO, I am exploring a multitude of strategies to ensure
that our car recommendation platform — currently fully hosted on Microsoft Azure — delivers real-time responses to user queries.
Here's an insight into our roadmap for latency optimization.
The Importance of Instantaneity
Users come to CarWiz seeking prompt guidance in navigating the sea of automotive options. They input their preferences and expect
instant Wiz-like recommendations that resonate with their needs. We are aware that the efficacy of our AI-driven advice is measured
not only by its relevance but also by the swiftness of its delivery.
Latency Optimization Techniques Under Consideration
Model Pruning: Model pruning is a technique I am exploring to streamline our machine learning models on Azure ML. Approaches thus far
have been inspired by the paper
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
(Han, Mao, & Dally, 2015), and it highlights the process of simplifying machine learning models by removing parts of the model that
contribute little to its output (such as weights close to zero in neural networks). This can result in faster model inference times
and reduced model sizes, which are beneficial for deployment in environments where resources are limited, like mobile devices or edge
computing scenarios.
Quantization: Quantization involves converting a model from using floating-point numbers to using lower-precision
representations, such as integers. This can greatly reduce the computational resources required for running the model, as operations
on integers are faster than those on floating-point numbers. This one has also been inspired by Han, Mao & Dally's referenced above, as well as the paper
Compressing Deep Convolutional Networks using Vector Quantization (Gong et. al., 2014). From prelimiary results and their analysis, I
expect a drop in accuracy from this strategy but don't foresee it being noticeable, given the current size of our user base and how early we are in our ML & AI journey.
Data Representation: In the technological architecture of CarWiz, we've made a strategic decision to employ Azure SQL for
structured data and tables, while utilizing Azure Blob Storage for our image data. This approach is grounded in optimizing performance,
cost, and scalability by aligning the type of storage to the nature of the data.
Caching Strategies: Intelligent caching mechanisms, such as Azure Cache for Redis, are being designed to provide instant
responses for common queries. Caching this way could mean storing frequently accessed data in memory for quick retrieval.
This is particularly useful for our kind of recommendation system where many users might have similar queries, and I foresee it ultimately
preventing the need for the model to compute the same recommendation multiple times. However, given the current stage of our AI & ML journey,
more time is needed to determine which queries are most popular among our users.
Load Balancing: Even though we haven't experienced excessive demand on our servers yet, Load balancing is a consideration I have on
my radar for the coming months. Per current subscription plans and server choices, most of our resources should scale
as demand increases. However, depending on how costly these current choices become, we might need to look into distributing workloads across
multiple computing resources to ensure that if there's ever any downsizing, no single one becomes a bottleneck during a user's end-to-end experience.
In the context of Azure, this could be managed automatically by services like Azure Load Balancer, which can distribute incoming traffic across multiple targets,
ensuring high availability and reliability.
Measuring the Impact
The strategies under consideration have the potential to not only enhance the user experience but also to foster trust in our
platform's ability to deliver timely and relevant advice. By reducing latency, we aim to increase user engagement and ensure
that CarWiz becomes the go-to platform for personalized car recommendations.
Continuous Evolution
The roadmap for latency reduction at CarWiz is an evolving document, shaped by the latest in technological advancements and
user feedback. We remain vigilant and ready to integrate new methods that promise to bring speed gains to our platform's
recommendations.
In conclusion, CarWiz is committed to delivering a real-time, data-driven car recommendation experience that caters to the modern
user's expectation of speed. Stay tuned as we continue to innovate at the crossroads of AI and user experience.