AI-ML implementation in your product
When your business card bears the presidential seal, mistakes that otherwise won’t matter can snowball into catastrophe in the blink of an eye.
The adoption of Artificial Intelligence and Machine Learning is running rampant across industries and businesses. In the history of technological advancements, “rare” and “unprecedented” are perhaps the words that go with AI and ML.
Business leaders and Decision makers have been long evaluating how the inclusion of AI and ML into their products and services can drive growth.
While the ideas like Autonomous cars and robots definitely steal the limelight and become undisputedly the “girl-everyone’s-looking-at-prom” whenever a debate on the potential of Artificial Intelligence is triggered, there are a plenty of other use cases and opportunities for a myriad of diverse fields.
Dawn Of Disruption: Artificial Intelligence powered products and Services
Every company, group, and teams are intrigued by the idea of software having the ability to self-learn and process data to contribute to revolutionary products and services.
Cloud Computing and Machine Learning algorithms have made the creation of products powered by Artificial Intelligence even more practical. That’s the reason we put up this post to walk you through the process of AI-ML implementation in your product.
Part of the reason they’re important is that they allow the interpretation of overwhelming data volumes possible at manageable costs.
The most crucial internet-based and Data-intensive industries are the ones truly ripe for AI disruption.
Speaking of these industries, be it the Finance & Insurance along with Healthcare, or even the industries like Manufacturing and Education, the percent of time spent by the people in processing and collecting data, as revealed by a Mckinsey survey, ranges from 23% to a whopping 50%.
AI-Based Apps to pop-up
Even the simplest mobile apps of past yearn for the holy touch of AI to be “smart” now. Be it the Google Assistant or Google Photos by the behemoth, Alphabet Inc. Or the interesting case of Amazon Alexa, no wonder the gas pedal of AI inclusion in the software industry has a foot over it.
So, have you considered the idea of making your product better, more user-friendly and obviously “more business-friendly” yet?
Whether it’s a Damn-Yes or Not-Yet-No, you need to read on so you can come to an informed and calculated decision.
Disclaimer: This post is about pondering over the possibility of AI-ML Implementation in your product. And if you’re convinced, we’ll discuss how to go about it.
Also Read: Converting a Full Objective C App to SWIFT
Great, let’s get rolling
First of all, we shall study the most promising and revolutionary use-cases that AI, ML and whatever comes with these has to offer.
User behavior: Able to analyze a large set of data
Determining User Behavior has become somewhat a normal expectation from any successful company. Part of the reason is the incredible insights that you can generate from user’s data is just invaluable, which often translates to revenue and sales generated.
The importance of ability to analyze large sets of data to get a hold of user behavior has seen tremendous realization partly because of the explosion we saw in the number of smartphones, social media users and other medium which tend to generate data about a person and his likes, dislikes, preference and other things about selves.
Marketers and businessmen around the world are betting big on the capability of AI-ML for the tools to identify and target the right audience with the right content at the right time.
Image Recognition is another hot-shot use case of Machine Learning which can be used to visualize, share and gain insights from the data.
Image Recognition is essentially a step ahead version of Computer Vision which was about recognizing discrete objects in certain images.
However, with the advent of AI and the technologies it has impacted, it is easy to understand and even describe the content in the form of images and videos.
From making robust safety features in Automobile to even assisting the visually impaired folks, humans have a tremendous amount of advantages to tap in.
Not only this, the availability and easy access (Pascal VOC and ImageNet) to training data is another positive edge for the developers and product managers who are looking to create models for image recognition applications.
Some of the popular open-source frameworks and software libraries that you need to know before you kickstart your image recognition project are as follows:
- Facebook AI Research
- Google TensorFlow
- UC Berkeley’s Caffe.
Natural Language Processing
NLPs are important because of one reason:
Majority of human activities are performed through language, and language is what NLPs are about.
The computers of the modern age are an amazing tool for bridging the gaps of linguistic acumen among the humans.
You must have noticed the automatic replying options you get in your e-mails, or that you’re prompted whenever you mistakenly try sending an empty mail, or without recipients. That’s all NLPs.
From Automatic Summarization for making reading and studying of huge piles of textual information fast-paced to assisting the decision makers to get a hold of sentiment analysis, NLPs are proving out to be extremely helpful and even instrumental for a multitude of tasks in modern businesses, which signals to the chance that you may use NLPs for AI-ML implementation in your product too.
The sharp rise in the number of malware and other fringe elements in the networks around the world is indeed a disturbing truth today.
In the day when almost everything hinges on data security and other information privacy and protection practices, AI and modules based on it are just in time to save the day. The various problems that the people are facing in the data security and other related turfs like that of employees interacting with the suspicious websites to hackers figuring ways into their databases easily, AI-powered solutions are the answer to all of them.
However, a lot of studies have shown that almost all the malware pieces tend to have almost the same code as the previous versions.
Now, AI and ML-based models can easily predict which files stand the chance of being a part of malware attacks.
A number of products like IBM Qradar are there as an example of AI-Based data security models. There are a few other readymade models in data security which make the idea of AI-ML implementation in your product a practical one.
AI Powered Writers
Content Creation is another very important part of your marketing efforts. Writing Blogs, articles and listicles is something that every business is investing in.
Do you have a blog too? Maybe you need AI-ML Implementation in your product.
The intriguing example of AI Powered tools to assist with grammar correcting, or even brainstorming the topics themselves may not be intriguing anymore when you realize that the latest AI-powered tools are even able to write complete blogs and articles.
Tools like Automated Insights are able to use natural language generation (NLGs) to make the best use of tools since it helps the production of stories and blogs just by feeding data and topics to it.
Using the already built solutions to build your platform.
There have been a lot of tools that the developers can tap into for integration of AI in their products.
Have a look at some of the tools that you can consider integrating with your platform:
IBM Watson ML –
It’s an IBM Cloud offering which makes it easy for the developers and product managers to integrate predictive capabilities into their products. The best part about this IBM offering could be the ability to use command line interface and Python in order to manage the artifacts. Watson Machine Learning REST API can be extended an artificial intelligence application, too.
Google’s ML KIT –
Google launched a new SDK which aims at bringing Google’s Machine Learning expertise to the mobile developers in a developer-friendly package on Firebase.
The ML Kit by Google is helpful in providing just the right starting torque that the developers need when they set out for their first ML product integration.
The Google’s ML Kit provides you with five ready-to-use APIs for common mobile use cases:
- Text Recognition
- Face Detection
- Barcode Scanning
- Image Labeling
- Landmark Recognition.
One of the best things about Google’s ML Kit is that it provides on-device as well as Cloud APIs with a simple interface.
Not only this, the features like that of A/B Testing and Remote Config from Firebase can be leveraged too since the ML Kit is available through Firebase.
Azure Machine Learning Studio –
It is a Microsoft cloud-based product that allows the developers to build and tweak otherwise complex machine learning models.
Design, development, and deployment of ML solutions can be executed without any obstacles.
The convenient web browser-based interface and other extension capabilities add to its appeal for developers.
These are mainly the readymade tools that you can use for your AI-ML implementation in your product. Now, it is important to understand that solving product-defining problems using AI-ML implementation in your product is something the-tough-turf you don’t want to take on unless you’re an expert in Artificial Intelligence.
Acknowledge the Capability Problem and bring in experts
Solving problems related to great predictive ability models call for an in-depth experience and understanding, which is not feasible for every team to have in-house members with these qualities. However, there’s no reason for you to feel that AI-ML implementation in your product is tough.
Sodio is here for you!
We have experienced and talented professionals with a knack for Artificial Intelligence products who can create just the predictive models and creations that you need for the AI-ML implementation in your product.
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