Pros and cons of using serverless computing for deep learning
Training deep learning models typically involves using particular hardware, which causes the entire process to be time-exhaustive and resource-intensive. Serverless computing solves this problem and provides an alternate way to train our models. In this Answer, we’ll explore the use of serverless computing for training deep learning models and its benefits.
In serverless computing, resources are obtained through a cloud provider on demand rather than acquiring and managing physical infrastructure. In these cases, users will be charged for each transaction that occurs on these resources.
Serverless computing has revolutionized how applications and APIs are built and deployed by abstracting server management. There is a wide variety of serverless platforms, such as AWS, Azure, and Google Cloud. Serverless architectures provide cost-efficiency, automatic scaling, and event-driven capabilities, making them ideal for modern, agile development and reducing operational overhead. It allows developers to focus solely on writing code as they can create backends, process data, construct IoT applications, and much more.
Advantages
The advantages of such a system are highlighted in the table below:
Advantages | Description |
Reduced management of infrastructure | When compared to using physical resources, serverless computing includes minimal maintenance as most of it is done on the cloud providers end. This also leads to more experimentation, which is vital in deep learning. |
Scalable | Serverless computing is scalable by nature, as any further resources can be provided instantly when needed. The same isn’t the case when handling physical resources. |
Costs | Serverless computing only charges for the transactions that occur. This is more cost-effective, especially if our physical resources are underutilized, as we’ll still be charged even if they aren’t in use. |
Along with these advantages, serverless computing provides flexibility to users training deep-learning models. With less management of resources, more time can be attributed to training their data models.
Disadvantages
Serverless computing isn’t without its drawbacks, the most obvious being latency. As the services are being acquired through a cloud provider, there may be latency during the training process. There are also fewer customization options when compared to using physical hardware, as we’ll have to ensure that our cloud provider supports the unique configurations our data models may have.
If our needs are resource-intensive or significantly large models that come with cold start latency, serverless platforms may not be suited. Long-lasting training processes that need consistent low latency may be better suited using regular hardware. Additionally, running 24-hour-a-day deep learning models may be cost-intensive, which leads to higher costs when compared to traditional hardware.
In conclusion, its scalability and cost-effective nature make serverless computing an attractive prospect for training data models. While it comes with its drawbacks, serverless platforms will undoubtedly evolve to address these issues.
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