Nowadays, companies all over the world are incorporating AI in their business process, with a view to streamlining them. Firms need to scale and industrlialize open source AI. Normally, they use tools like TensorFlow, Keras, Xgboost, Apache Code and also build custom code in open source languages like Python, R and Scale. But, the main issue is how to standardize the platform and increasing the productivity of data scientists. Now, let us understand what is IBM Cloud private for data.
What is IBM Cloud private for data
IBM Cloud Private Data is a tool which is used to automate and simplify how your company turns data into sights within a common design. It has four main functions
1) Collect – visualize for making data accessible and simple.
2) Organize – Create a genuine, trusted analytics foundation.
3) Analyze – scale insights demand with AI everywhere.
4) Infuse – AI is operationalized with efficiency.
1. Scalability and speed of cloud in a safe enviornment.
Nearly all the firms want their data centers to have the advantages of public cloud: scalability, elasticity, ease of use and rapid deployment. If you use IBM Cloud private for data, then you will get predictable cost, increased performance, flexible management options and enterprised focussed on control. Private cloud foundation is containerized platform.
2. Manage your resources securely.
1) Data sovereignity or the control or knowing where the data is.
2) Control of a fixed enterprise private cloud enviornment with the firm’s own governance and security policies.
3) Predictability of cost.
4) Hybrid capability – seamless integration to firm’s other enviornment other IT enviornment - data centres, public cloud providers and traditional on premises.
The methodology of IBM Cloud private for data
The first step is to let the team concerning AI to work with their tools and framework. Next, IBM team will help you to govern the machine learning models and scripts using an enterprise wide tool.
The third step is for automating the model management and deployment, so the data personnel are able to focus on model outputs and business logic.
These steps can be applied to all the business processes, so that every employee can feel the effect of IBM Cloud private for data.
Benefits of IBM Cloud private for data
By choosing IBM Cloud private for data, customers get the advantage of choosing how and where the open source stack needs to be deployed, like cloud, on premises. You should realize what open source is all about. IBM cloud private for data provides innovation, trust and control, so that the customers have the choice for realizing the value of their open source investments. The main advantages are stated below.
1) Multi tenant containers and orchestration which is sampled on Kubernetes for making microservices based applications.
2) Common catalog for open services and enterprise to increase developer productivity.
3) Choice of compute models for massive innovation in big enterprises including infrastructure
Cloud Foundry and Kubernetes.
4) Common base services for supporting the scalability of micro services including Istio, controlling with Prometheus, logging using ELK.
5) Automatic non disruptive and horizontal vertical scaling of applications.
6) Cloud storage policy based controls and network for application security and isolation.
7) Automated checking of health application and failure recovery.
8) Support including Open Stack and Vmware.
So, how can companies avail of this facility ?
1. Find out how your data scientists ,AI team, data engineers use open source today. Enquire from them, which open source technologies they are using, where they are using and what they are using.
2. Check simple open source data science workloads, only on IBM Cloud private for data. See which data science models or machine learning, you would like to add in your business at scale.It could be customer churn, risk analytics, financial analytics and customer analytics.
3. Add your open source data science models on IBM Cloud private for data. After installing on this platform, you can enhance these models.
Currently, companies are using this tool to run on existing frameworks and languages, their data science teams have been using. This is done by governing and managing models covering across
on premises, behind the firewall and maintaining that they can be easily transferred to cloud using
Kubernetes and containers foundation. IBM Cloud private for data creates a flexible, open source platform for building and modernizing a new generation of cloud-native applications which can tap their data, wherever it stays.