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With the volume and complexity of data increasing dramatically every day, the job of a Data Scientist—to effectively use this data to drive better business decisions—becomes even more challenging. To handle the rapid pace of data growth, architects around the world have been working hard to find new solutions.
Kubernetes (k8s) has the capacity to scale processing power dynamically, making it instrumental in processing large quantities of data. Moreover, open-source software Kubeflow (KF) has increased interest in the data science community, as it simplifies some of the routine operationalization of the data science pipeline.
Even with the advent of Kubernetes and Kubeflow, creating and maintaining a k8s cluster and installing the KF takes up to several weeks, even for an experienced data scientist.
Our platform, Ulap Data Platform, removes this roadblock: users can quickly spin up a secure Kubernetes cluster with pre-installed Kubeflow, MLflow, Spark or any other tools, to kick-start collaborative data science work with just a few clicks. In this blog, we will highlight the efficiency barriers data scientists face, and how Ulap offers solution to these challenges.
Current Pain Points For Data Scientists
One of the most tedious and time consuming tasks for a data scientist kick-starting a new project is making sure all required tools are present in the environment. When data scientists work in the scalable k8s environment, this task becomes even more daunting, as different k8s environments need specific versions of the applications to work properly.
Additionally, open source projects like Kubeflow are an attractive choice for many data scientists, but there is often a lack of pedagogical documentation for newbies to use these tools effectively.
Often, a team decides to move from on-premise to cloud-based k8s environment or to a different cloud environment. This poses new challenges, as teams must set things up entirely in different environments before any meaningful progress is made.
Additionally, when a complex machine learning pipeline includes steps that may need different compute resources (CPU, GPU etc.), effectively allocating the correct resources to the appropriate steps becomes difficult.
There are other important steps to ensure the data science workflow is executed properly, including but not limited to: load balancing, confirming that each cluster is secure, and appropriately distributing the jobs to multiple nodes. Most data scientists do not have expertise in these areas, and unless a company has strong DevSecOps resources available, this lack of mastery becomes a bottleneck to meet an organization’s goals.
How Does Ulap Remove These Pain Points?
Ulap is designed to tackle these challenges so that data scientists can focus their valuable expertise on digging through and finding relevant trends in the data, offering an edge to their organization’s bottom line. With Ulap, it only takes a few clicks and a few minutes: any company can swiftly spin a k8s cluster in the environment of their choice and effortlessly deploy necessary tools.
Ulap utilizes a straightforward user interface (UI) to deploy variety of tools such as Kubeflow, MLFlow, Jupyterlab, Spark, and Minio. The flexibility to use a variety of tools is crucial in the early stages of a project, so that developers can fine-tune what works best for their scenario. Ulap makes the process of deploying and using any tool within the cluster effortless.
Each team member can be given customized access to modify different resources within the cluster. Within any organization, Ulap allows multiple teams to work on separate projects, each with a specialized access based on their needs.
Ulap handles all aspects of cluster management, including security, load balancing, and maintaining replicas for high availability, completely in the backend. A data scientist can add, remove, and auto-scale different node pools in accordance with their needs, to avoid unnecessary costs when the resources are not in use.
Upcoming blogs will provide step-by-step tutorials for using Ulap UI for a seamless data analytical experience.
If you are interested, please register for early access to Ulap here.
