Describing Enterprise Artificial Intelligence: Platform as a Service With Current AI Infrastructure

If I will talk about the Enterprise AI, then it is hard to think of an application that doesn’t use a database. If you see from mobile to web to the desktop, every modern application relies on some of a database. Some apps use flat files while others rely on memory or NoSQL database.

If I will talk about the traditional enterprise applications, then they interact with large database clusters running Microsoft SQL, Oracle etc. The fact is that every application needs it.

Like databases, Artificial Intelligence (AI) is moving towards becoming a core component of modern applications. In the coming months, almost every application that we use will depend on some form of AI.

Enterprise AI simulates the cognitive functions of the human mind — learning, reasoning, perception, planning, problem solving and self-correction — with computer systems. Enterprise AI is part of a range of business applications, such as expert systems, speech recognition and image recognition.

Figure 1

1. Start Consuming Artificial Intelligence APIs

This approach is the least disruptive way of getting started with AI. Many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, natural language processing, and video search API.

Let’s look at a concrete example of analysing the customer sentiment in a customer product requirement demo session. Almost all the Customer calls to the service team are recorded for random sampling.

A supervisor routinely listens to the calls to assess the quality and the overall satisfaction level of customers. But this analysis is done only on a small subset of all the calls received by the customers to the service team. This use case is an excellent candidate for AI APIs. Each recorded call can be first converted into text, which is then sent to a sentiment analysis API, which will ultimately return a score that directly represents the customer satisfaction level.

The best thing is that the process only takes a few seconds for analysing each call, which means that the supervisor now has visibility into the quality of all the calls in near real-time. This approach enables the company to quickly escalate incidents to tackle unhappy customers and rude customer service agents.From CRM to finance to manufacturing domains, customers will tremendously benefit from the integration of AI. There are multiple AI platforms and API providers like (With link):

• Amazon AI Services (https://docs.aws.amazon.com/aws-technical-content/latest/aws-overview/artificial-intelligence-services.html)
• Google Cloud ML Services (https://cloud.google.com/ml-engine/reference/)
• IBM Watson Services (https://console.bluemix.net/catalog/?category=ai)
• Microsoft Cognitive Services (https://docs.microsoft.com/en-us/azure/cognitive-services/)
• Clarifai (https://clarifai.com/developer/guide/)

2. Build and Deploy custom AI models in the Cloud

While consuming APIs is a great start for AI, it is often limiting for enterprises.

We have seen the benefits of Integrating Artificial Intelligence with applications, customers will be ready to take it to the next level.

This step includes acquiring data from a variety of existing sources and implementing a custom machine learning model. It requires creating data processing pipelines, identifying the right algorithms, training and testing machine learning models and finally deploying them in production.

Similar to Platform as a Service that takes the code and scales it in the production environment, Machine learning as a service offerings take the data and expose the final model as an API endpoint. The benefit of this deployment pattern lies in making use of the cloud infrastructure for training and testing the models. Customers will be able to spin up infrastructure powered by advanced hardware configuration based on GPUs and FPGAs.

Platforms that offers Machine Learning as a Service:

• Amazon ML (https://docs.aws.amazon.com/machine-learning/index.html#lang/en_us)
• Azure ML Studio (https://docs.microsoft.com/en-us/azure/machine-learning/)
• Google Cloud ML Engine (https://cloud.google.com/ml-engine/docs/)
• Bonsai AppNexus (https://wiki.appnexus.com/display/api/Welcome)
• BigML (https://bigml.com/api)

3. Run Open Source AI Platforms On-Premises

The final step in AI-enabling applications is to invest in the infrastructure and teams required to generate and run the models locally. This is for enterprise applications with a high degree of customization and for those customers who need to comply with policies related to data confidentiality.

If ML as a Service (MLaaS) is similar to PaaS, and running AI infrastructure locally then it is comparable to a Private Cloud. Customers need to invest in modern hardware based on SSDs and GPUs designed for parallel processing of data. They also need expert data scientists who can build highly customized models based on open source frameworks. The biggest advantage of this approach is that everything runs in-house. From data acquisition to real-time analytics, the entire pipeline stays close to the applications. But the flipside is in the OPEX and the need for experienced data scientists.

Customers implementing the AI infrastructure use one of the below open source platforms for Machine Learning and Deep Learning:

• MXNet (https://mxnet.incubator.apache.org/api/python/index.html)
• Microsoft Cognitive Toolkit (https://docs.microsoft.com/en-us/cognitive-toolkit/)
• Tensorflow (https://www.tensorflow.org/api_docs/)
• Theano (http://deeplearning.net/software/theano/library/index.html)

If you want to get started with AI, explore the APIs first before moving to the next step. For developers, the hosted MLaaS offerings may be a good start.Artificial Intelligence is evolving to become a core building block of contemporary applications. AI is all set to become as common as databases. It’s time for organizations to create the roadmap for building intelligent applications.

AI Data evolutions like Data Processing and Neural Networks.

Now in present time we are feeding loads of data to the computer, so the computer will learn about Deep learning technologies and the reason for behind this to take AI Initiative.

Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. Neural networks learn by example to solve complex signal processing and pattern recognition problems, including speech-to-text transcription, handwriting recognition and facial recognition.

Data processing is, generally, “the collection and manipulation of items of data to produce meaningful information.” In this sense it can be considered a subset of information processing, “the change (processing) of information in any manner detectable by an observer.”

AI data processing is the need for high-quality data. While data quality has always been important, it’s arguably more vital than ever with AI initiatives.

In 2017, research firm Gartner described a few of the myths related to AI. One is that AI is a single entity that companies can buy. In reality, it’s a collection of technologies used in applications, systems and solutions to add specific functional capabilities, and it requires a robust AI infrastructure.

Another myth is that every organization needs an AI strategy or a chief AI officer. The fact is, although enterprise AI technologies will become pervasive and increase in capabilities in the near future, companies should focus on business results that these technologies can enhance.

Conclusion

In, the Conclusion I would say everyone has to use the Artificial Intelligence Applications and as we found that many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, NLP and video search API.

As we have seen earlier a supervisor can do the Random Sampling at the time of Customer Product Requirement demo session. So, what supervisor wants to listen in the last about the customer satisfaction and how better we can use the AI APIs to convert the call into text for the sentiment analysis. As, I have mentioned above in this use case we can definitely find out the score directly what is the satisfaction level of customer.

So, with the help of AI APIs we can easily understand the customer problems and give a better product to them for their use.

As, I have mentioned above for the same that we have to build and deploy the custom AI models in the cloud and why it is more useful and beneficial for the customers.

So, if I will define why is it more useful and beneficial as we have seen above the AI APIs will take to the customer to the next level. AI APIs will acquires data from variety of sources and help in to create data processing pipelines and identifying the right algorithm and deploy in production environment.

So, in the last I would say use the AI APIs applications to reduce the pain of customer and help them to reduce the complexity and make the process more effective with the help of AI APIs.