Whenever we talk about data science, we always focus on the model and its performance. But let’s not forget that the ‘best data science comes with the best engineering practices’. The performance, integrity, accuracy and scalability of data science depends on the choice and implementation of the infrastructure of the model. Whether you choose to deploy it on AWS or Heroku will make a difference in terms of performance, error documentation, governance and scalability. In this article, we’ll review the importance of infrastructure in data science.
Infrastructure that is easy to debug
- One of the most crucial aspects of your infrastructure is its ability to allow for quick and easy debugging. This can happen in multiple ways but one of the easiest ways is for the system to generate intuitive logs of all errors that get transported back to a governance dashboard. Unfortunately, most providers like Heroku do not provide such intuitive logs that are easy to get. For Heroku, one has to clone the repository and obtain the tail of the logs to find errors.
- Another important feature is the system’s ability to track granular errors, classify them and group them such that processing and fixing of errors becomes easier. Some systems do not provide enough detail in error logs which makes it harder to debug problems.
Scalability along multiple dimensions
- When we talk about the scalability of data science, we always think about faster data processing. But the infrastructure should allow for scalability along multiple factors such as speed of data transfer, speed of processing, speed of file transfer back to the source, scalability in the number of ports and processing power, and the ability to parallelize your workflows.
- On the topic of scalability, another important consideration is the limits of scalability. One can never anticipate the growth of incoming data and thus, they must be prepared to improve their model capabilities dynamically. AWS provides an extensive set of instance sizes from very small and inexpensive ones to large, quick and expensive instances. But AWS is not a good choice if you’re planning to implement a model for a longer period of time as it may start to become costlier than hosting your own service.
Security and integrity of the infrastructure
- Not all providers are secure. Some infrastructure providers are fairly insecure and do not provide enough detail about the data handling process inside the machines. Here’s a rule to follow – ‘If you get a lot of unexpected authentication errors, it is more likely that the security layer is working well and quickly blocks unexpected requests.’. When it comes to security, you should always prioritize robustness of the security layers over ease of login.
- The infrastructure must also be free from human interference. This means that, once the model is deployed, nobody should be able to touch or extract the data. One may extract metrics from the model that would help them govern it but the infrastructure should protect the model’s engineering frameworks and proprietary information at all costs.
Ease of automation and connectivity with other services
- When it comes to data science, the focus should be on automation. The infrastructure should allow for automated connections with service providers, databases, and machines that are essential to your business. If the infrastructure has limited support for services like MongoDB or PostgreSQL, then it would be hard for your models to scale up and be connected to the necessary sources of information.
- The infrastructure should also support different kinds of hosting services such as Docker or Kubernetes that make it easy for deployment and management of the models. With limited support, the infrastructure might become a liability rather than an asset.
Use of standardized practices and software choices
- The model should also allow for easy transfer of information or data from a different source. If the infrastructure isn’t built with standardized practices, Eg. an instance built with an older version of RedHat, then transferring your model and data would become trickier as packages would have expired and it wouldn’t support new packages or services that your model actually needs.
Choose an infrastructure with enough use cases and documentation of successful deployment
- Make sure to do enough research into the deployment of the model so that you understand the intricacies of deploying your model. Read up articles such as ‘How to deploy model xyz on an EC2 instance’ so that you get a sense of the amount of work needed to deploy the model. It may be the case that there is no such article for your choice. Without documentation, maintenance and deployment become 10x harder so make sure that there is enough documentation beforehand.
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This Blog Post was written by Sohit Miglani. Connect with me here: