Machine learning operations (MLOps) give your company a competitive advantage, and yet, many companies are hesitant to adopt this technology for many reasons, including disjointed services and budget constraints.
To make matters worse, traditional DevOps is not suitable for machine learning operations, as ML operates by running multiple iterations. On the other hand, managed service providers often do not offer end-to-end solutions, posing a major disadvantage.
HPE developed Ezmeral ML Ops as a solution in this space. Ezmeral is unified software for data processing, model development and training, visualization, and security and governance issues that offers operational machine learning using containers.
Ezmeral provides flexibility and scalability while deploying ML models and assists the entire ML life cycle as a much-needed unified framework. The workflow supports zero trust and data security as part of its secure environment.Â
And it features built-in security measures for volume permissions, impersonation, policy-based security, and external KMIP Keystore that can further help you comply with various laws and norms.
HPE Ezmeral ML Ops is an exciting asset met with a positive response. Let’s take a deeper look at the Ezmeral to understand how it can serves your needs and what you could gain by adopting it:
HPE and the ML market
AI software revenue is predicted to grow to $126 billion by 2025, according to a prediction by Tractica.
HPE’s philosophy is to make the most out of information to gain an insight into the various operational processes. HPE Ezmeral ML Ops carries this vision by solving the hurdles that a data scientist faces in day-to-day operations.
“The HPE Ezmeral software portfolio fuels data-driven digital transformation in the enterprise by modernizing applications, unlocking insights, and automating operations,” said Kumar Sreekanti, CTO and head of software, HPE.
The solution has the ability to help customers in accelerating innovation and reducing costs as well as promising enterprise-grade security, according to Sreekanti. It eliminates lock-ins and licensing models used in legacy systems. Sreekanti gives the credit to the 8,300 software engineers at HPE to innovate the edge to cloud portfolio.
Sreekanti said Ezmeral software, along with HPE GreenLake cloud services, will “disrupt the industry by delivering an open, flexible, cloud experience everywhere.”
Key features
HPE Ezmeral ML Ops is designed to work as a unified framework that attends to the entire ML life cycle, beginning with data preparation, deployment, and monitoring. Consequently, it has different features that work at each step of the process:
- Model building:Â provides sandbox environments with many data science tools, allowing simultaneous experimentation with multiple ML frameworks.
- Model training:Â provides access to scalable environments to help you meet workload requirements for development, testing, or production workloads.
- Model deployment: deploys the model’s native run-time image into an HTTP endpoint that is secure and flexible. The integrated model registry facilitates version tracking and easy updates.
- Model monitoring: complete visibility into the memory and processing resource usages, such as GPU, CPU, and memory utilization. You can track, measure, and report the performance and use third-party integrations, such as ParallelM, to monitor model performance.
- Collaboration: Integration of Ezmeral with GitHub allows source control, ease of collaboration, and lineage tracking for audibility.
- Security and control: integrates with enterprise authentication mechanisms, enabling multi-tenancy and data isolation for logical separation between each subgroup in the organization. It allows sharing the access to shared enterprise data with security and access control.
- Hybrid deployment: runs the on-premises infrastructure, multiple public clouds, or a hybrid model, providing flexibility and cost reduction.
Key benefits
Faster time to value
With HPE Ezmeral ML Ops, development and product management take minutes instead of the days it would take for a legacy system. You can onboard new data scientists and avoid siloed development environments for new tools and languages. Faster time to value means you get results faster, which can, in turn, impact critical business processes.
Improved productivity
Ezmeral works with multi-tenant environments by design, so data scientists can focus on building models and running analyses with accuracy. It encourages teams to collaborate and improves the reusability of code, project, and model repositories.
Reduced risk
With Ezmeral ML Ops, you get enterprise-grade security and tools for model governance and audibility that help you with regulatory compliance due to lineage tracking. It also provides high availability deployments that ensure business continuity. Access control with separation for compute and storage makes sharing enterprise data sources secure.
Flexibility and elasticity
Ezmeral supports deployment to on-premises, cloud, or hybrid models that auto-scales clusters per dynamic workloads, making it easier to scale your operations and add new architecture.
Use cases
Wargaming
Online games rely heavily on ML models to improve player experience. Games generate billions of data points daily. Ezmeral containerizes these ML environments and analysis for insights that lead to improvements in efficiency.
The Advisory Board Company
Operationalization of machine learning is a challenge for health care organizations that generate vast amounts of data. Ezmeral helps data scientists with faster time to insights and reduced costs.
IDC
IDC is a global marketing intelligence firm that uses Ezmeral ML Ops to operationalize the ML life cycle from proof of concept (POC) to deployment and monitoring. HPE’s container-based solution supports a range of ML operations to drive business optimization.
Differentiators
DevOps-like speed and agility
HPE Ezmeral ML Ops’ DevOps approach supports efficiency in building, training, and deploying models. It operationalizes an end-to-end process for the ML life cycle for shorter data model timelines and time to market. It makes sense, considering DevOps teams will be using it to accelerate their workflow.
Pre-packaged tools
Ezmeral can respond to dynamic business requirements in a range of use cases. It aids with operationalizing ML models at an enterprise scale by delivering a cloud-like experience with pre-packaged tools. Ezmeral can also quickly generate environments based on the data science tools of your choice to explore and experiment with multiple machine learning frameworks.
Unified platform
Ezmeral provides a unified platform assisting in every step of the ML life cycle, from data prep to deployment and monitoring. Unification has been a hurdle to ML adoption, as most cloud providers’ services are distributed and difficult to use in conjunction due to low interoperability.
User reviews of HPE Ezmeral ML Ops
The Ezmeral ML Ops service has received a positive market response upon release and continues to attract new businesses transitioning to data-driven organizations:
| PeerSpot | 3.5 out of 5 |
Here’s an example of a review:
“If customers focus on primary apps or are using them for data science, this is a good solution,” a user says at PeerSpot. “Easy to install with good support, but the deployment of applications could be simplified.”
Conclusions
Companies need an MLOps tool to aid their data scientists in model development, training, and monitoring. The complexity of cloud-based solutions and lack of support for on-premises data centers can discourage some companies. They also demand heavy investments to set up and run.
Amid this scarcity of resources, HPE launched its Ezmeral ML Ops, which has been positively met and is solving many challenges faced by data-backed organizations, getting insight out of the information.
As we transition to the age of insight, we also shift our focus from digitization to data-first, with the movement already in progress. Ezmeral ML Ops is an important solution that will ease the process for both new and old organizations.
