Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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3 Steps To Embedding Artificial Intelligence In Enterprise Applications
In the context of contemporary applications, it's hard to think of an application that doesn't use a database. From mobile to web to the desktop, every modern application relies on some form of a database. Some apps use flat files while others rely on in-memory or NoSQL databases. Traditional enterprise applications interact with large database clusters running Microsoft SQL, Oracle or DB2. Irrespective of the kind of database, the fact is that every app 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.
Artificial Intelligence is all set to become the new database for the next generation applications.
Here are three steps to start AI-enabling enterprise applications.
Step 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 analyzing the customer sentiment in a contact center. Almost all the inbound calls to the contact center 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 call center. 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 minutes for analyzing 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 call center agents.
The above scenario is just an example of how AI transforms enterprises. From insurance to finance to manufacturing domains, customers will tremendously benefit from the integration of AI.
There are multiple AI platforms that expose simple APIs at an affordable price point. Below is a sample list of the API providers.
Step 2 - Build and Deploy Custom AI Models in the Cloud
While consuming APIs is a great start for AI, it's often limiting for enterprises. Having 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 the machine learning models, and finally deploying them in production. This is when the enterprise should start investing in a data engineering and data science team.
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.
Below is a sample list of platforms that offer Machine Learning as a Service:
Step 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 and data sovereignty.
If ML as a Service (MLaaS) is similar to PaaS, running AI infrastructure locally 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:
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. Continue Reading>>