Data Science Platform

What is a Data Science Platform?

A data science platform is a software solution that enables data scientists to effectively plan strategies, discover actionable insights from interpreting data, and communicate their insights across the entire organization—all within a single environment. A data science platform includes machine learning and other advanced analytics.

When is a data science platform needed?

In order to perform data science at scale, organizations look to data science platforms that unify the data science team needs in a centralized environment so data scientists can pool resources and team up effectively, speeding up the process of deploying multiple models instantly and simultaneously. Data scientists turn to data science platforms while running experiments to test new ideas and different ideas, as well as review output and make necessary changes. A data science platform is also necessary when operationalizing data science in order to gain value from the outcomes of analysis.

What is needed in a data science platform?

The key elements of a data science platform include data storage, data servers, and data architecture. Data ingestion needs, data consolidation, and the Extract-Transform-Load (ETL) process are also components of a data science platform. Together, they deliver real-time business insights through analytics in a cost-efficient, scalable, secure manner.

What are the benefits of a data science platform?

Data science platforms can make data scientists more productive by helping them deliver models more quickly and with fewer errors. They help data scientists work with larger volumes and varieties of data. And they deliver trusted, enterprise-grade artificial intelligence without bias. The results are auditable and reproducible. Data science platforms also enhance collaboration, offering integrated products and components that help throughout all stages of the data science lifecycle. Without a data science platform, data scientists have greater difficulty managing different tools with different releases. Complications could arise when sharing code and models. Time and costs involved in the integration and maintenance of open-source tools could skyrocket.

HPE and data science platforms

HPE data science platforms are purpose-built, hybrid cloud solutions for data science and analytics workloads. We can help you build and accelerate modern data analytics initiatives at scale with our complete orchestrated Kubernetes container platforms, as well as built-in persistent storage layers and ML Ops specifically for data science workflows.

· Powered by HPE Ezmeral Runtime Enterprise, HPE GreenLake for Containers delivers a unified control plane for cloud-native and non-cloud-native apps to automate and manage on-premises, public cloud, and hybrid container deployments.

· HPE GreenLake for Data Fabric combines dative S3 objects, files, streams, and databases in a unified platform that spans on-premises, cloud, and edge.

· HPE GreenLake for ML Ops speeds up the time to value with operationalized ML across your business, helping you reduce the typical six-to-nine-month infrastructure procurement cycle to a matter of just a few weeks.