3 ways IBM Cloud Private for Data can accelerate your AI and machine learning journey

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The last few months have been really exciting to be part of Big Data and Analytics at IBM. We launched IBM Cloud Private for Data, an integrated data science, data engineering and app building platform back in May 2018. Our broader offering, IBM Cloud Private is helping our clients and partners to harness everything cloud has to offer, but off of the public cloud, securely and behind their firewalls.

It seems like CEOs all over the world are realizing that to be competitive, they have to embrace technologies like private cloud and artificial intelligence (AI). IBM Cloud Private for Data empowers organizations to be AI-ready with an integrated analytics platform. It’s a single platform that brings it all together—with no “assembly” required—that helps prepare a company’s data stores for AI. You can unleash powerful machine learning and data science technologies that can help turn data into game-changing insights and bring you closer to AI.

IBM Cloud Private for Data can be deployed in minutes, not weeks. It is an application layer deployed on Kubernetes open-source container software. Using microservices, it can form a truly integrated environment for data science use cases and more efficient application development. IBM Cloud Private for Data can run on all clouds, as well as be available in industry-specific solutions, for financial services, healthcare, manufacturing and beyond.

IBM Cloud Private for Data offers many advantages. But the following are what I see as its top three benefits.

1. Gain shorter cycle time from idea to production

What could your business do if you could reduce development cycle from months to days? What if you could introduce data science, AI and machine learning use cases this quickly? Imagine the impact you could make on the bottom and top lines of your business.

IBM Cloud Private for Data empowers you to do exactly that with reduced provisioning time for data and analytics stacks required for these use cases. It brings the power of IBM data management, information governance and integration and data science offerings to your private cloud. And, because it is based on the open source Kubernetes technology stack, it can significantly reduce the inefficiencies of maintaining and managing multiple data science analytics tools in different environments. There’s no need to use different tools to manage data access, preparation, exploration, statistics, machine learning and other data services.

2. Enjoy freedom with multi-cloud portability and elasticity

We have all encountered scenarios where one or another cloud environment might not be suitable for certain data workloads—for example, because of security. Many companies stretch applications across private, public or hybrid clouds. They do so for many reasons, including backup, resilience or regulatory constraints. By building data apps on microservices-based containerized architecture, you can make sure that apps are portable between different cloud environments.

Having a single, cloud-agnostic data platform can make it possible to best use multiple clouds for machine learning and data science use cases. In addition, open source data science tools are continually evolving. Businesses need a comprehensive, integrated platform to avoid the traps of traditional monolithic data architecture or vendor lock-in.

IBM Cloud Private for Data can help you build the necessary foundation to develop data architecture that is fully portable, elastic, governed, data-catalogued and gives federated access to all of your data sources.

3. Integrate security and governance into your data insights

An integrated containerized platform approach makes sure there is little room for security breaches and data risks. This is a fundamentally better approach than wiring the data portfolio together from discrete products. I strongly believe that security isn’t a problem you ever fully solve. Bad actors and sophisticated adversaries constantly evolve. With an integrated data platform, businesses can keep improving techniques to stay ahead and detect unknown patterns of threats faster.

In addition, IBM Cloud Private for Data provides the following robust data governance characteristics:

  1. Understand, cleanse and prepare your data to create data preparation pipelines visually
  2. Grant user access levels and enforce business policies
  3. Index for search, visualize consumers and producers of assets with lineage, metrics and quality profiles
  4. Find data and analytics assets in the Enterprise Catalogue

There are many more ways to realize value by using containers or Kubernetes stack for your data analytics workloads. Explore how to accelerate your journey to advance analytics with AI, machine learning and data science to power your digital transformation journey.

Ready to make your data ready for the cloud? Join us at the Gartner Symposium/ITxpo from Nov 13th to 16th in Goa, India.

Click to know more #IBMatGartner  or engage with an expert.


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