MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE CASE-STUDY

WHAT IS MACHINE LEARNING ?

* In layman’s term provding brain to a machine.

Machine learning is a branch of artificial intelligence that focuses on getting a computer to figure out how to solve a problem, instead of humans telling it how to do so.

APPLICATION OF MACHINE LEARNING

NETWORKING

SECURITY :- Systems can analyze patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help cybersecurity teams be more proactive in preventing threats and responding to active attacks in real time.

PERFORMANCE MANAGEMENT :- Tools equipped with machine learning can help both with moment-by-moment traffic management and with longer-range capacity planning and management. These tools can see if traffic is spiking in some places or failing to flow in others, and they can direct automated or manual management responses.

HEALTH MANAGEMENT :- machine-learning-driven analytics can help spot when a network component is in the initial stages of failure and predict when those initial stages will appear for currently healthy nodes. Network equipment vendors are increasingly weaving analytics like this into management tools.

CLOUD COMPUTING

CHAT-BOT :- When integrated with the cloud, chatbots and smart personal assistants will have a vast pool of data at their disposal to learn from. As a result, their learning capabilities will get a considerable boost. With time, chatbots and personal assistants will evolve to completely do away with any form of human intervention or support.

IoT Cloud :- IoT Cloud is a cloud platform specifically designed to store and process the data generated by the Internet of Things (IoT). Salesforce’s IoT Cloud is powered by Thunder — a “massively scalable real-time event processing engine. IoT Cloud can intake colossal amounts of data generated by connected devices, sensors, applications, websites, and customers and trigger actions for real-time responses. It can be used for various real-world scenarios. For instance, by connecting to personal devices at use, IoT could know the status of flights and rebooking flight tickets for passengers whose flights got delayed or cancelled.

Business Intelligence(BI) :- Machine Learning cloud computing, business intelligence (BI) services are also becoming increasingly intelligent. Cloud Machine Learning has two-fold benefits for BI. While the cloud platform can store vast volumes of customer and company data, ML algorithms can process and analyze that data to find innovative solutions.

4. Cognitive Cloud :- The cloud stores massive amounts of data which becomes the source of learning for ML algorithms. Since billions of people around the globe use cloud platforms to store data, it presents a wonderful opportunity for ML algorithms to leverage that data and learn from it. It other words, ML algorithms can shift the cloud paradigm from cloud computing to cognitive computing.

Cognitive computing pertains to technology platforms that are designed on the principles of AI and signal processing. It incorporates machine learning, natural language processing, speech/object recognition, human-computer interaction, and narrative generation. When infused with ML capabilities, the cloud becomes “Cognitive Cloud” that can make cognitive computing applications accessible for the common mass.

IBM Cognitive and Microsoft’s Azure Cognitive Services are excellent examples of this — these platforms allow you to develop intelligent apps without any hassle.

BIG DATA

By feeding big data to a machine-learning algorithm, we might expect to see defined and analyzed results, like hidden patterns and analytics, that can assist in predictive modeling. For some companies, these algorithms might automate processes that were previously human-centered.

DEVOPS

1. Predicting Build Time :- The ML program will collect information about the machine on which a build was run, number of lines of code (#lines) that went into the build, number of files of code (#files) and the time it takes to complete a successful build. The program will train a regression model using these input parameters of build machine, #lines, and #files. Using this trained model the program can now predict the build time for any build in advance. If the actual build takes more than 120% of this predicted build, the program will send an alert since it is very likely that there are some issues with the build.

2. Intelligent Ticketing System :-

Categorize the ticket automatically

*Find similar tickets

*Automatically assign the ticket to the right owner and

* Automatically resolve the underlying issue, if possible.

All these activities will be performed by doing Natural Language Processing (NLP) of the content of the tickets and the history.

3. Predicting Release Time :-

Any release typically has numerous criteria e.g.

*No priority 0 defects

*80% code coverage

*All user stories completed

*90% automation achieved

*Not more than 40 total defects etc.

It would be good if one can effectively predict how long it will take to complete the current release at any given time. To achieve this, in addition to above-mentioned parameters we will capture;

*Number of developers on the team

*Number of QA engineers on the team

*#lines

*#files

*#total defects