In today’s data-driven world, companies turn to machine learning (ML) for answers, to automate decisions, and to improve customer experiences. But we see that scaling and managing ML models is a complex and resource-intensive task. That is, until serverless machine learning, which puts together the features of serverless computing and ML to provide great scalability, efficiency, and flexibility.
Understanding Serverless Machine Learning
In the field of serverless machine learning, we see the implementation of ML models on top of serverless computing platforms. What is put forward by the developer is code, which in turn is run on a per-request basis. The cloud provider is in charge of the heavy lifting, which includes setting up, scaling out, and running the infrastructure.
In terms of Machine Learning, what we are seeing is the ability to train and deploy models that do away with the need to manage servers, and also the issue of runtime environments. Popular cloud players like AWS (which has Lambda and SageMaker), Google Cloud Functions, and Azure Functions report serverless ML solutions, which in turn allow data scientists and developers to put their focus on models instead of machines.
How Serverless Enhances Scalability?
In the case of serverless machine learning, we see great benefit in automatic scaling. In traditional machine learning, we had to set up virtual machines or containers, configure load balancers, and do manual scale-up of resources to deal with peaks in use. With serverless platforms, this is all done for you dynamically.
As demand goes up, which may be from more users, bigger data sets, or more complex requests, our servers will scale out, at which point we add more resources as needed. Also, each machine learning request or prediction which in turn triggers a separate function that runs at the same time as the others, which is very useful in real-time applications like fraud detection, recommendation engines, or chatbot responses.
Cost-Efficient and Resource-Optimized
In a serverless ML environment, organizations pay for the compute time that they use. We don’t have to maintain idle resources or over-provision for possible surges. This is especially true for ML models that are used sporadically or for batch processing. Resources are provisioned as required and shut down automatically when not in use.
Also, in that virtual players run the infrastructure we see a great reduction in the issue of updates, patching, and monitoring, which in turn results in lower operation overhead and faster time to market.
Faster Deployment and Experimentation
A server in which there is no server infrastructure required of the platform, which in turn enables what we see as very fast iteration and experimentation, which is key in the machine learning life cycle. Data scientists put out different model versions as separate functions, and they play around with them very easily, almost out of the box. Also, this architecture of modularity supports product testing and version control, which in turn allows teams to improve models without having any downtime or disrupting present services.
Use Cases Across Industries
Serverless ML is adopted in all industries. In health, we see real-time diagnosis, which comes from image analysis models. In finance, it supports scalable credit scoring systems. In retail, it powers personalized shopping experiences. Due to flexibility and scale, serverless ML is what both startups and established enterprises use to innovate fast.
Challenges and Considerations
Serverless ML has its benefits, but it also has some issues. What we see is that cold starts, which are delays at the time a function is triggered out of idleness, do in fact impact performance in applications that are very sensitive to latency. Also, we see that issues with execution time and memory, which in turn do not scale well for very large models or complex training processes.
