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.
