Managing data science projects can be a nightmare

Tracking data science projects can be a nightmare. Making sense of a sea of experiments, models that are all scattered across multiple workstations with no sense of order, different software environments, and other complexities create ever more hurdles to making sense of your data. Then when you add in limited documentation availability plus the intricate interplay of the different technologies being leveraged it's no wonder that reproducing results becomes a tricky task. Fortunately, machine learning (ML) platforms are helping to automate and manage these complexities, leaving data scientists and data science managers to solve the real problem – getting value from the data.

Polyaxon makes developing models easier

Polyaxon is a platform for developing machine learning and deep learning models that can be used on an enterprise scale for all steps of the machine learning and deep learning model development process: building, training, and monitoring. Polyaxon accomplishes this by leveraging a built-in infrastructure, set of tools, trusted algorithms, and industry models, all of which lead to faster innovation. Polyaxon enables data scientists to easily develop and manage experiments and manages the entire workflow with smart containers and advanced scheduling. It is also language and framework agnostic, allowing data scientists to work with popular libraries and frameworks such as R, Python, SAS, Jupyter, RStudio, Tensorflow, and H2O.

Managing multiple data scientists and experiment artifacts

 One feature that data scientist managers will find especially useful is Polyaxon’s ease of knowledge distribution. With fast onboarding of new team members and a documented and searchable knowledge base, any new hire can quickly pick up where others left off using each project's artifacts and history. Additionally, Polyaxon includes risk management capabilities and a built-in auto-documentation engine to remove risk and create a searchable knowledge base, avoiding the problem of laptop-centric and scattered scripts-oriented development.

For the executives of an organization, Polyaxon provides improved insights on model development and measuring time to market. By enabling a virtuous experimentation life cycle and giving data-driven feedback, all based on a centralized dashboard, Polyaxon optimizes and the time spent on projects. This means data science teams spend more time producing value, rather than trying to maintain infrastructure and documentation.

Deploying Polyaxon with Omnia

Omnia is an open‑source framework for deploying and managing high-performance clusters for HPC, AI, and data analytics workloads. Omnia not only automates the installation of Slurm and/or Kubernetes for managing your server infrastructure, it also deploys and configures many other packages and services necessary for running diverse workloads on the same converged solution. It also automates the deployment of ML platforms, like Polyaxon. This gives IT infrastructure teams the ability to quickly spin up and offer new capabilities to an organization’s data science and applications teams, giving them more time to do the company’s business.

Automation is key to any data-driven organization

The ability to automate the infrastructure stack, from the server, storage, and network resources up to the data science platforms that help you derive value from their data, is key to the success of modern data-driven organizations. Tools change quickly and frequently, and spending weeks deploying IT solutions for a company’s data science teams is time not spent finding critical value. Omnia simplifies the process of infrastructure deployment, allowing IT groups to get their data science teams up and running in minutes. What could be more transformative than that?

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Learn more about Polyaxon

Learn more about Omnia

High Performance Computing (HPC), in which clusters of machines work together as one supercomputer, is changing the way we live and how we work. These clusters of CPU, memory, accelerators, and other resources help us forecast the weather and understand climate change, understand diseases, design new drugs and therapies, develop safe cars and planes, improve solar panels, and even simulate life and the evolution of the universe itself. The cluster architecture model that makes this compute-intensive research possible is also well suited for high performance data analytics (HPDA) and developing machine learning  models. With the Big Data era in full swing and the Artificial Intelligence (AI) gold rush underway, we have seen marketing teams with their own Hadoop clusters attempting to transition to HPDA and finance teams managing their own GPU farms. Everyone has the same goals: to gain new, better insights faster by using HPDA and by developing advanced machine learning models using techniques such as deep learning  and reinforcement learning. Today, everyone has a use for their own high-performance computing cluster. It’s the age of the clusters!

Today's AI-driven IT Headache: Siloed Clusters and Cluster Sprawl

Unfortunately, cluster sprawl has taken over our data centers and consumes inordinate amounts of IT resources. Large research organizations and businesses have a cluster for this and a cluster for that. Perhaps each group has a little “sandbox” cluster, or each type of workload has a different cluster. Many of these clusters look remarkably similar, but they each need a dedicated system administrator (or set of administrators), have different authorization credentials, different operating models, and sit in different racks in your data center. What if there was a way to bring them all together?

That’s why Dell Technologies, in partnership with Intel, started the Omnia project.

The Omnia Project

The Omnia project is an open-source initiative with a simple aim: To make consolidated infrastructure easy and painless to deploy using open open source and free use software. By bringing the best open source software tools together with the domain expertise of Dell Technologies' HPC & AI Innovation Lab, HPC & AI Centers of Excellence, and the broader HPC Community, Omnia gives customers decades of accumulated expertise in deploying state-of-the-art systems for HPC, AI, and Data Analytics – all in a set of easily executable Ansible playbooks. In a single day, a stack of servers, networking switches, and storage arrays can be transformed into one consolidated cluster for running all your HPC, AI, and Data Analytics workloads.Omnia project logo