Introducing Machine Learning to Business

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Introducing Machine Learning to Business

Machine learning is all about understanding statistical data with a meaningful prediction from the raw function. Smart machines and applications have steadily become a daily phenomenon that helps to get work done faster and easy. This blog will introduce Machine Learning with AI and its business aspects.

As with 2018 at end of the quarter, giant Software Companies are spending more than 30% of their IT budget and expecting to raise the number in the future. Mainly it is headed for growth shoot with the help of Artificial Intelligence that atomizes the directed work. When business comes to picture, technology is a key to fly high in the market. Whereby humans are down by slow biological evolution, couldn’t compete, and would be superseded.


“There is denying the fact that AI is driving the next generation technology revolution”, stated by Cognitive Solution and IBM Research in 2016. With the same aspects, I want to draw your attention towards quote of Bill Gates:

“A breakthrough in machine learning would be worth ten Microsoft”

Till the end of last year, Google paid $400 million to London based AI outfit known as DeepMind, a specialist in deep learning research to get more precise about data and artificial intelligence.

Google is using such a far above the ground technology in study and prototyping for self-driving vehicles to know what poses a danger on the roads.

Undergoing the Process of ML

Learning Machine is proven as result-oriented aspect for scalability of business and that urge to utilize human’s time for other prioritize work function. It is a device whose actions are influenced by past experience. Loop is been generated while operating the machine so continuous workflows along with new learning mechanism line to line. The algorithm with analytical pedagogy is placed to go with machine learning and to formulate with the subject line.

Statistical Data

Machine Learning refers to the methodology concerned in dealing with huge amounts of statistical information in an intellectual way to drive actionable insights. It uses statistical data to predict the value of target variables using a set of entered figures. ML is possible with Artificial Intelligence covering a board category of an Internet of Things (IoT) including media, investment, transportation, research & technology, food, and more.

Google provides with Machine Learning Tool Kit that allows adding various features, especially for developers with an easy package to use and implement. It helps out-of-the-box solutions that will run on the device depending on requirements. Tensorflow is a computational framework for building machine learning models that allow the creation of different models through a toolkit.

Basic APIs: effortlessly create apps of machine learning
  • *Text Recognition
  • *Face detection
  • *Landmark detection
  • *Barcode scanning
  • *Image Labeling
  • *Smart reply
Let’s see some of the well-known brands that have taken initiative to implement Machine Learning with AI

Coca-Cola covered another milestone level by installing a SELF-SERVICE SOFT DRINK MACHINE that allows customers to make their drinks by choosing the combination of their choice from the machine. To basic beverages, customers can now add various flavors of their choice.
The healthcare sector is one of the top sectors where Machine Learning is implemented and highly used for the Diagnosis of ailments. Major Biopharma companies are using AI to research & develop therapeutic treatments in multiple areas. Currently, research is going on prostate cancer through Artificial Intelligence & ML.

It also helps to identify preliminary Drug discovery from initial screening to the prediction of drugs based on biological factors. Radiography, a Smart electronic health record, is another example of healthcare which is considered based on R&D.

ML in the cloud for unrivaled scalability, an organization is adopting the cloud with more than 60% of organizations. Boarder approaches to machine learning and providing

companies with the resources to create their own models. The development of full artificial intelligence could spell the end of the human race.
The best example that I must quote here is AUDI, which unveiled an autonomous A7 that uses a Nvidia processor to perform Object recognition & other aspects.

As Machine Learning is a subset of AI, both work together to refer to a methodology going fast for future aspects. Companies like Google, IBM, Microsoft, Facebook, and more are spending millions researching advanced networks and deep machine learning to get computers smarter. David Wood, co-founder of Symbian and now a futurist at Delta Wisdom, explains with different examples using face reorganization.

Business is picking up steam with AI. Initially, organizations were hesitant to apply all Artificial Intelligence, but after a couple of years, people are ready to go with AI and Machine Learning by viewing its drastic change over the industry workflow. Software that mimics the work of human is what organizations asking for. AI is an umbrella full of technology, including robotics, Machine Learning, Speech recognition, Virtual Assistance, and more with a broader sense.

Now a question arises when and how to draft Artificial Intelligence, and what aspects should be considered for Machine Learning?

The business nowadays goes with the research-based theme to identify proper aspects and major possibilities to implement. After testing, choose one segment as a testing ground to avoid big failure. Once the research is done, you can analyze the area of Artificial Intelligence to enhance your business. By accepting the challenge and moving ahead, one can implement over the industry with knowledge-based Machine Learning. Welcome to Future World with Agile Infoways.

Adoption of Artificial Intelligence depends on the readiness of a business by following below steps:

  1. Knowledge of AI with Machine Learning
  2. Deep analysis of Business with AI-driven solutions
  3. Further cost estimation
  4. IT machine infrastructure
  5. Deciding and allocating a segment for an experiment
  6. A soft launch of the segment
A process of teaching machines

Every teaching step involves a structural process where every stage builds a better version of the machine.

Collecting Data for Input Data: Data consisting of documents, files, and images; SQL data, spreadsheets, etc. are used to process a task.

Data Preparing & Abstraction: Elementary learning takes place in this step. As the machine understands Structural data, inputted data undergoes an algorithm to represent the structural format.

Generalization: A process of work starts with practicing from experience. The previous step is followed to extend an imminent.

Benefits of ML with AI, according to some of Fellow Tech Experts

Generate new services: A pure extension of the human ability to solve various problems and to generate new ideas. We can see the advantage at an unpredicted rate in the coming couple of years. Robotics and AI together will affect business operations with new services and will significantly impact efficiency by adding more customers.

Increase security: With Machine learning aspects and the Internet of Things, Security is getting better with the algorithm process of work tied with a loop to access the flow. AI plays a key role in security by connecting through a network that avoids mistakes that are obvious to humans.

Future aspects: Some new industries we are related are developing Machine Learning that is implemented through Artificial Intelligence and the Internet of Things. Learning Machines help predict behavior and future processes with existing and new data.

Facilitate sustainability: Changing Climate, environmental issues, urbanization, and other such aspect of sustainability in the 21st century are becoming more at the forefront in our mind. We can sustain by mapping the areas to improve.

Make us smarter: More deep to technology makes us smarter by involving it to make a live aspect with the power of computational. AI helps us to make a projection on future behavior and events to train models at a higher level of knowledge based.

Live Implementation of AI with ML:

  1. Chatbots in applications to reduce the waiting process & connect with customers
  2. Fast food machines to deliver food as fast as possible
  3. Financial services to grab huge data from the marketing database
  4. ML saves lives by Infervision into healthcare by reducing the tedious work of radiologists
  5. Product manufacturing helps to predict parts would fail by observing vehicle performance
  6. Media project talking with machines allows listeners to join in for two-way conversations through smart speaker are few latest examples one can go through to understand all about AI & ML.
  7. Big data analysis, as predicted by Netflix, is one of the live examples we are enjoying by watching are some of the latest examples of ML via Artificial Intelligence implemented by IoT.

Concluding Statement

Machine Learning is moving so fast that it’s hard to predict earlier, but with IoT and AI, one can visualize upcoming new aspects to apply with any industry to atomize the workflow with a continuous loop of an algorithm that functions effortlessly and reduces tedious human work by introducing smart work. “Computers are able to see, hear, learn & perform”.