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Making A Case For Serverless Machine Learning

Making A Case For Serverless Machine Learning

  • The serverless approach to computing relies on stateless lambda functions.

The scale and complexity of machine learning make it hard to provide and manage data and resources efficiently. This hinders and decreases productivity. The easiest way to approach the problem is serverless machine learning. It is an excellent solution to the problem of data center resource management. Machine learning users face several daunting challenges that have a significant impact on their productivity and efficiency. One of the major challenges is data center resource management. The user has to manage the cluster- its size, type, and logic for scaling along with having to pay for unused server power. The management of container logic is also done by the user such as logging and handling multiple requests.

What is it?

The serverless approach to computing relies on stateless lambda functions that are submitted by developers and automatically used by the cloud infrastructure. The first benefit of serverless machine learning is that it is very scalable. It can stack up to 10k requests at the same time without having to write any additional logic. It doesn’t consume extra time to scale which makes it perfect for handling random high loads. Secondly, with a pay-as-you-go architecture of serverless machine learning a person doesn’t have to pay unused server time. It can save an enormous amount of money. For example, if a user has 50k requests a month, he is obliged to pay only for 50k requests. Thirdly, infrastructure management becomes very easy as a user doesn’t have to hire a special person to look into it, it can be done very easily by a backend developer.  For instance, AWS Lambda is one of the most popular serverless cloud services that has these advantages. It lets users run code without managing servers. It obviated the need for developers to explicitly configure, deploy, and manage long-term computing units. 

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Training in Serverless Machine Learning does not require extensive programming knowledge. Basic knowledge of Python, Machine Learning, Linux, and Terminal along with an AWS account is enough to get one started. The knowledge of Serverless Machine learning will help the user discover a very scalable, cost-effective, and quick way of deploying various machine learning models to production by using its tools.

Serverless computing is also an incredible approach to the problem of resource management that Machine learning users encounter. It solves the problem of resource management by exposing low-level VM resources such as CPUs and storage capacity. It lifts a significant burden from Machine learning users who face the challenge of resource management while developing.

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It solves the problem of over-provisioning of resources which often happens when developers over-provision resources for peak consumption which leads to a waste of resources of the data center. The problem also increases when developers constantly change parameters during different stages of development.

Limitations

Despite many advantages, the technology has some limitations which put it at a backfoot. All lambda functions including the AWS Lambda have very small memory and local disk. For example, the AWS Lambda can only access at most 3GB of local RAM and 512MB of the local disk which is an extremely small amount of memory. Another disadvantage with Serverless machine learning is its low bandwidth as compared to VM. The highest bandwidth with AWS Lambda is 40MB/s as compared to 1GB/s in medium-sized VMs. Also, though the pay-as-you-go model is cheap, it is left behind by cluster when a user doesn’t have peak loads and the number of requests is really high. Lambda functions also have short and unpredictable launch times that are highly variable. The lack of a shared fast serverless storage for the cloud that should be low-latency and apt for communications in machine learning workloads also puts the technology at a disadvantage as they cannot connect between themselves. The lack of shared storage space means that Serverless Machine Learning only pays off in models where there is efficient communication

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