How you can Reduce Costs of Data Science and MLOps Development Pipelines with k0s and Jupyter Notebooks

Data science teams constantly grapple with the challenge of building robust data analytics pipelines that can handle the complexities of huge datasets with DLP and HIPPA, FIPS, and/or Financial Industry regulatory compliance requirements.? And somehow, teams are expected to execute these projects while staying within a finite budget, leveraging hybrid Cloud/ on-prem Hadoop Custers, IBM X mainframes and massive datastores. I will describe what I think may be a means of leveraging a minimalist workflow strategy, where data scientists can easily develop, test, benchmark, and deploy these pipelines without breaking the bank. The powerful trio of k0s, Jupyter notebooks, and Google Cloud Platform (GCP) are what I will use for this use case example.

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On-Prem to Cloud Integration Options

1. Shared Jupyter Environment on-prem: The first step is to set up shared workspace machines, by team, that have Jupyter, container-d/docker, and k0s installed. This could just as well be the case instead, setting up a Jupyter instance on GCP's Compute Engine or utilizing a managed service like Vertex AI Workbench for AI/ML workloads. Within this shared environment, data scientists access virtual notebooks that are either pre-configured with k0s, docker and containerd or have the flexibility to spin up their own using tools like k3d or kind.

2. Pipeline Development & Testing: Now, the developers can test all of their code, applications, pipelines and containers in these notebooks(that they can shutdown when they are not using them)::

• Data Ingestion: Data scientists write code to seamlessly ingest data from diverse sources, such as BigQuery or Cloud Storage, directly within the notebook environment.
• Preprocessing: Leveraging libraries like Pandas or Spark, they preprocess the data within the k0s cluster, harnessing the power of distributed computing for enhanced performance.
• Model Training: Powerful machine learning models are trained using frameworks like TensorFlow or PyTorch, with the option to utilize GPUs or TPUs for accelerated computation, all within the k0s environment.
• Tabular Analytical Report Simulations: Using tools like Tableau or other similar tools to test out the SQL and other various coding to create visualization and data reports.
• Pod Validation: Staging and testing applications, network, security, api's, load balancing, and training models that are containerized and deployed as services within the k0s cluster, enabling pod testing, bootstrap auto-healing, and validation.

3. Deployment to GCP (DEV-0): Once the pipeline components have been rigorously tested within the notebook's k0s cluster, k0s' tooling allows for the export of the entire cluster configuration. This configuration is then employed to deploy to k0s clusters, along with the complete production pipeline, to a dedicated GCP DEV project. From here, the pipeline can be further integrated with GCP services like Cloud Dataflow, Pub/Sub, endpoint testing, container/pod testing, and Vertex AI for seamless production deployment.? The beauty here is, this environment can be spun up to only test all of the security, whitelisting, networking, and integration with other applications.

3. (OPTIONALLY) Deployment to GCP (DEV-1, PRE-PROD etc): Once the k0s DEV-0 pipeline components have been rigorously tested within the notebook's k0s cluster in the cloud, k0s' tooling allows for the export of the entire cluster configuration to a k8s production cluster via terraform pre-templated scripts, which can use the same components created for the DEV-0 environment. This configuration is then employed to deploy an identical equivalent of what the k8s production cluster will be, along with the complete pipeline. This DEV-1 environment will be used for gke upgrade/update testing/staging, app update staging/testing, capacity testing, disaster recovery testing and can act as last resort fail-over of the Production environment in the event of a cartographic failure. It can be kept at a bare minimum deployment configuration for reduced cost or shut down when not in use.

Reaping the Cost Benefits

For enterprises that deploy clusters at large scale, these workflows yield substantial cost savings:

• Reduced Cloud Infrastructure: By running the lightweight k0s on smaller, cost-effective GCP instances during development and testing, teams can significantly reduce compute costs compared to using a full-fledged Kubernetes cluster.
• Efficient Resource Utilization: The shared Jupyter environment on GCP fosters collaboration and optimizes costs by allowing multiple developers to leverage the same underlying infrastructure.
• Early Bug Detection: Identifying and addressing bugs early in the development cycle, within the notebook environment, prevents costly issues that might arise later in production.

Additional Considerations

While this approach offers numerous advantages, it's crucial to keep the following factors in mind:

• Security: Implement robust security measures, including role-based access control (RBAC) and network security, to safeguard sensitive data and code within the shared Jupyter environment and k0s clusters on GCP.
• Container/Pod Testing:? The ability to completely and quickly spin up containers and architect the Pod strategy for the deploymen wih jFrog, Artifact Register or DockerHubt.
• Scalability: As your data analytics pipelines grow in complexity, leverage GCP's autoscaling capabilities to dynamically scale the k0s clusters and JupyterHub instance to meet demand.
• CI/CD Integration: Seamlessly integrate this workflow with GCP's CI/CD tools, such as Cloud Build, Terraform Cloud, Ansible, github enterprise/cloud, ArgoCD, and Cloud Deploy, to automate the testing, building, and deployment of your pipelines to production environments.

Advantages Beyond Cost

Beyond cost savings, this approach offers several other compelling benefits:

• Streamlined Workflow: It provides a frictionless experience for data scientists, allowing them to transition seamlessly from experimentation in notebooks to deployment on GCP.
• Realistic Testing: k0s creates a near-production environment right within the notebook VM, enhancing the reliability of tests and ensuring a smooth deployment to GCP.

Conclusion

By harnessing the synergy of k0s, Jupyter notebooks, and GCP, data science teams can unlock a powerful, cost-effective, and scalable environment for building and deploying data analytics pipelines. This approach fosters collaboration, accelerates time-to-market, and optimizes cloud resource utilization, empowering your team to achieve greater success in the data-driven world.