Kubernetes in Machine Learning and the Need for Enhanced Security

According to IBM, Kubernetes (K8s) is now the most popular container orchestration platform. Most of the top cloud service providers including Google Cloud, Amazon Web Services, and Microsoft Azure already offer managed K8s services. This popularity leaves little doubt as to the importance of K8s in machine learning development.

Kubernetes comes with a number of advantages that make it a preferable solution for containerization in machine learning development. However, it is not the perfect install-and-forget kind of solution, especially when it comes to security.

The relevance of Kubernetes in machine learning

Tech influencer Ajeet Singh Raina says that five powerful capabilities make Kubernetes a good match for machine learning development. These are multi-tenancy, scalability, data management, GPU support, and infrastructure abstraction. These make K8s a favorite tool for data scientists working on machine learning.

"ML is composed of a diverse set of workloads managed by separate teams. Kubernetes offers the 'namespaces' feature, which enables a single cluster to be partitioned into multiple virtual clusters. Finally, Kubernetes provides a single access point for diverse data sources and manages the volume lifecycle, enabling teams to provision exactly the cloud-based storage they require while reducing complexity," Raina shares.

AI expert Justin Bercich also supports the idea of K8s as an important tech for machine learning development. "One of the main reasons AI and ML have been able to continue their relentless march is because of specific open-source mathematical software such as Tensorflow (deep learning) and Kubernetes (distributed computing), which have made data science infinitely more efficient and effective," Bercich explains. The former AI chief at Lucinity adds that ideas and innovations flow and flourish to drive AI and machine learning advancement as more people become fluent with Kubernetes and other related solutions.

Meanwhile, John Spooner, former AI lead at H20.ai, emphasizes the security and scalability advantages of Kubernetes. "Kubernetes can meet many of the computational challenges by orchestrating this workflow through containers. It can be the single platform of choice for IT that provides a scalable and secure way of deploying software in a unified manner," Spooner says.

The need for more advanced security

Kubernetes has robust security features. As Spooner says, "it provides monitoring and governance capability for IT to make sure everything is working correctly and the capacity of the environment is optimized." However, this security can be improved further in response to more aggressive and complex threats.

This is where Kubernetes Security Posture Management (KSPM) plays an important role. K8s expands the cyberattack surface an enterprise has to deal with. In the hands of inexperienced developers and security teams that are unfamiliar with the technology, it would not be surprising to have Kubernetes become more of a liability than an advantageous addition to machine learning development endeavors.

KSPM provides a full range of tools and measures to automate security and compliance for all K8s clusters run by an organization. It is essentially the K8s specialized version of cloud security posture management. It focuses on four key areas, namely:

• The automation of security scanning across Kubernetes clusters
• K8s misconfiguration and configuration error detection
• The defining of security policies
• Assessment and sorting of threats

What makes enhanced Kubernetes security even more effective, with KSPM in particular, is that it integrates into continuous integration and continuous discovery pipelines. This results in seamless container and cluster security, which is advantageous for DevSecOps teams that are seeking to integrate security throughout the software development life cycle.

Securing portable, cloud-agnostic environments

Portable and cloud training environments are important in modern machine learning development. Using a single machine will eventually lead to bottlenecks as developers experiment with a multitude of variations of their training scripts. Also, large machine learning models with massive datasets cannot viably run on just a single machine. Not only will it take time, but it is also highly inefficient and counterproductive.

Machine learning developers need cloud environments that can support the large computing power requirement they need for development and training. At the same time, they require portability to take advantage of multiple systems (to run training), which may not have the same operating systems and kernel versions.

Cloud and portable environments benefit collaboration. They enable collaborative development, which is already the common practice for machine learning projects nowadays. It would be inconvenient to always have developers in the same location and use the same machines. They require the ability to share training scripts with good version control and consistent reproducibility without the need to share full execution environments.

Container orchestration for machine learning development under portable environments with Kubernetes is one of the best ways to make all of these happen. What's great is that securing portable environments is not that difficult. KSPM, as mentioned, can provide an effective and efficient solution to enhance K8s security, mainly through the following:

• The prompt identification of issues in role-based access control, particularly when it comes to enforcing the principle of least privilege
• The detection of possible violations of security standards such as ISO//IEC 27001, SOX, and HIPAA.
• The discovery of possible deviation from internal network security rules and policies
• Automated remediation or the suggestion of appropriate actions after security issues have been detected

As a cloud-agnostic solution, Kubernetes provides several advantages for machine learning development. Even better, it is easy to secure, and proven security solutions already exist to keep it safe from various cyber threats.

In conclusion

Traditionally, machine learning development got by with just having high-performance CPUs and GPUs, massive storage for large training datasets, source control, frameworks, and libraries. For years, developers did not find it crucial to use clusters and shared file systems. Times have changed, though, and machine learning developers deem it necessary to achieve faster execution and more rapid experimentation by running their codes on a cluster, similar to what high-performance computing researchers are accustomed to.

This calls for a development environment that is portable where training can also be easily reproduced on a cluster. This is why containerization is now an important factor in the progress of machine learning technology, which makes container orchestration platforms like Kubernetes correspondingly relevant.

Containers and clusters are not just an option; they are what developers want and need to accelerate machine learning development. However, it also comes with security challenges. It is reassuring to know that there are already existing solutions that address these security concerns effectively.