Enabling end-to-end machine learning workflows with Iguazio

iguazio image
Dell Technologies

Across a broad spectrum of industries, organizations are working to deploy artificial intelligence applications that turn massive amounts of data into business value. And while the promise of machines surpassing human intelligence may not be possible anytime soon, AI-driven processes supported by machine learning techniques are already helping organizations cut costs, increase efficiency, improve customer interactions and more.

That’s all the upside. Then there is the issue of how you get there. Many companies struggle with the complexities of building and deploying the systems used to train models. Many also struggle to maintain data security during the research phase, which involves extensive data sharing. Additionally, there is the need to integrate data from disparate sources, both historical and streaming to deploy machine learning models in production environments.

If your organization is facing these challenges, chances are you could benefit from an integrated AI/ML solution that accelerates the deployment of applications in a secure manner — like the Iguazio® Data Science Platform.

Iguazio provides an extensive toolset for developing and deploying AI and ML models on one platform, where the action can take place close to the data source. Iguazio speeds and simplifies the deployment of these applications by building in essential frameworks, such as Kubeflow, Apache Spark and TensorFlow, along with well‑known orchestration tools like Docker and Kubernetes.

In another important benefit, the Iguazio software platform enables simultaneous access through multiple industry‑standard APIs for streams, tables, objects and files that are all stored and normalized once, so you can launch new projects quickly and then consume, share and analyze data faster.  Along the way, Iguazio delivers fine‑grained security using multi‑layered network, identity, metadata or content‑based policies.

By unifying the end-to-end data pipeline, Iguazio reduces the latency and complexity inherent in many advanced computing workloads, effectively bridging the gap between development and operations. Your data scientists can run queries on large datasets and securely share data and algorithmic models with authorized users during the training phase. Once the containerized models are ready for production, you can easily move them from development environments to operational environments.

If your organization is looking to capitalize on Iguazio, you would do well to look to Dell Technologies and Intel. Together with Iguazio, Dell and Intel deliver a new solution architecture for the Iguazio Data Science Platform on Dell EMC infrastructure with the latest Intel® Xeon® Scalable processors and NVMe storage, along with Intel-optimized AI libraries and frameworks. This Dell EMC Reference Architecture for Iguazio helps your organization implement optimized machine learning and deep learning projects faster and manage and scale them more easily.

The components of this jointly engineered reference architecture include leading-edge products from Dell Technologies, Intel and Iguazio:

iguazio table1 Dell Technologies

For added flexibility, the engineering-validated design for Iguazio uses a flexible building‑block approach to system design, where individual building blocks can be combined to build a system that is optimized specifically for your unique workloads and use cases.

Key takeaways

The Iguazio Data Science Platform on Dell EMC infrastructure with the latest Intel Xeon Scalable processors and NVMe storage enables your organization to implement optimized machine learning and deep learning projects faster and manage and scale them more easily. And together, Dell Technologies, Intel and Iguazio make it easy to get there with a validated reference architecture — so you can get AI-driven applications into production sooner, rather than later.

To learn more

For a deeper dive, see the solution brief and reference architecture white paper at Dell EMC PowerEdge Reference Architectures.

Copyright © 2020 IDG Communications, Inc.