The scale and complexity of machine learning makes it difficult to deliver and effectively manage data and resources. This hinders and decreases productivity. The easiest way to approach the problem is serverless machine learning. This is a great solution to the problem of managing data center resources. Machine learning users face several daunting challenges that have a significant impact on their productivity and efficiency. One of the major challenges is the management of data center resources. The user has to manage the cluster – its size, type, and scaling logic, while paying for unused server power. Management of container logic is also done by the user, such as logging and processing multiple requests.
What is that?
The serverless approach to IT relies on lambda stateless functions that are submitted by developers and automatically used by the cloud infrastructure. The first advantage of serverless machine learning is that it is very scalable. It can stack up to 10,000 queries at the same time without having to write additional logic. It doesn’t take extra time to scale, which makes it perfect for handling random high loads. Second, with a serverless machine learning architecture, a person does not have to pay for unused server time. It can save a lot of money. For example, if a user has 50k requests per month, they are only required to pay for 50k requests. Thirdly, infrastructure management becomes very easy because a user does not have to hire a special person to review it, it can be done very easily by a backend developer. For example, AWS Lambda is one of the most popular serverless cloud services that has these advantages. It allows users to run code without managing servers. This saved developers from having to explicitly configure, deploy, and manage long-term IT units.
Serverless machine learning training does not require extensive programming knowledge. Basic knowledge of Python, Machine Learning, Linux, and Terminal as well as an AWS account is sufficient to start one. Knowledge of serverless machine learning will help the user to discover a very scalable, economical and fast way to deploy various machine learning models in production using their tools.
Serverless computing is also an incredible approach to the resource management problem that machine learning users face. It solves the problem of resource management by exposing low-level VM resources such as processors and storage capacity. This relieves a significant burden for machine learning users who face the challenge of resource management during development.
It solves the problem of resource over-provisioning that often occurs when developers over-provision resources for peak consumption, resulting in wasted data center resources. The problem also increases when developers constantly change settings during different stages of development.
Despite many advantages, the technology has certain limitations that put it in the background. All lambda functions, including AWS Lambda, have very small memory and very small local disk. For example, AWS Lambda can only access a maximum of 3 GB of local RAM and 512 MB of local disk, which is a very small amount of memory. Another disadvantage of serverless machine learning is its low bandwidth compared to VM. The highest bandwidth with AWS Lambda is 40MB / s, compared to 1Gb / s in mid-size virtual machines. Additionally, while the pay-as-you-go model is cheap, it is left out by the cluster when a user has no load spikes and the number of requests is really high. Lambda functions also have short and unpredictable launch times which are highly variable. The lack of fast shared serverless storage for the cloud, which is expected to be low latency and suitable for communications in machine learning workloads, also puts the technology at a disadvantage because they cannot connect to each other. Lack of shared storage space means serverless machine learning is only cost effective in models where communication is effective.
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