HPE EzmeralSimplify cloud-native application delivery
HPE Ezmeral efficiently handles large-scale containerized applications, encompassing both cloud-native and non-cloud-native monolithic applications, including machine learning workflows with persistent data.
Business Goals
Revenue Expansion
Boost revenue with the HPE Container Platform by optimizing application deployment and data management for new and existing streams.
Data-Driven Insights
Utilize data science and machine learning for valuable insights, enhancing decisions and creating new revenue opportunities.
Operational Efficiency
Improve app delivery agility to streamline operations, optimize resource allocation, and reduce downtime, thereby increasing enterprise value.
86% of organizations are struggling with the operationalization of data science models.
Hype cycle for AI, 2021, MLOps is at the peak of inflated expectations and will reach the Plateau of productivity in 5–10 years. This indicates that MLOps is a rapidly growing discipline with a bright future.
MLOps will play a crucial role in more than 50% of data science activities by 2024, up from less than 5% in 2019. This shows that MLOps is becoming more important in current data science endeavors.
Why businesses face a tough line-up of challenges
Despite its potential, HPE Ezmeral struggled with low adoption and conversion rates due to a poor user experience. HPE revamped the Ezmeral container to improve the user experience and achieve a more agile app delivery process.
I&O leaders face difficulties when comparing deployment models, features, and pricing offered by various vendors, often overwhelmed by the influx of new solutions.
Vendors emphasize the benefits of hybrid and multi-cloud solutions, promising reduced public cloud lock-in and workload flexibility, yet they often fall short in explaining the practical capabilities and ROI.
Software and service providers continually expand their offerings, enhancing container orchestration with features like distributed management, application life cycle integration, policy management, monitoring, and security.
Organizations grapple with the absence of standardized machine learning (ML) architectures and MLOps processes, complicating ML project management.
Building and managing ML pipelines is a complex and time-consuming task, adding to operational challenges.
The monitoring and performance validation of ML models are vital but often challenging tasks.
Design Sprint Workshop
Together with the HPE team, we held a Design Sprint Workshop to better understand the needs, come up with ideas and possible solutions as a group, and find interesting and engaging ways to present the research results.
Top Business Objectives for Enterprise Sales Leader
Intelligaia started the research work with the in-house Kubernetes team by understanding the current application's flow, personas, and journey gaps and competitive analysis.
Quick Glance
When I {want to see the current pipeline for the Solution}, help me {see it by Stage}, so I can {understand the adoption metrics}.
User Story Mapping
We create a detailed Journey Map and Value Stream Mapping for the responsive persona and related roles.Customer Journey Mapping
A visual representation of the user journey helps to identify the gaps in the customer journey, reveal the disconnected experience and task dependency between the different personas.
Implementing a seamless experience for all personas
We started correlating things with the HPE Core team and started building the Day 0 and Day 10 flows for all the key personas and hero screens/flows.
Design with speed and precision
Sketches helped us get the alignment towards the design solution and high-level approach.
High fidelity Mockups
We help HPE expand its design system to the next level. This also acted as their brand guide and strategy.
Software Engineering Support
Our skilled engineering team made significant contributions to HPE Ezmeral, utilizing React JS and Express JS for full-stack development. We harnessed Grommet, a React-based framework, to create a responsive UI. We also played a key role in integrating Apache Spark and Apache Livy, customizing the UI for seamless data processing enhancements.
