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Sep 22 2021
How classes and objects are used in Python

Python is said to be a pure object oriented programming language and almost everything you come across in Python is an object. A class can be defined as a user-defined type that, by having its own methods (functions) and property (variables), define what object will look like. An object is a single instance of a class. You can create many objects from the same class type.

Creating a class in Python
class keyword is used in Python to create a class, an example is shown below. We create a class named Books with a property named total_count:

class Books:
  total_count = 5

Creating an object
class is just a blueprint. In order to use the class, we need to create an object. In below example, lets create an object called learn_python and using this we will print the value of total_count:

class Books:
  total_count = 5
python_book = Books()
print(python_book.total_count)

The init() Function
Let’s look at an important member of the class. We are talking about init() function. Infact all classes have this function, which is always called an object of the class in being created. This function is called automatically so you dont have to specifically call this. We use the init() function to initialize values or properties that needs to be assigned to the object immediately when created. In the below example, we create a class Books and using the init() function, we have automatically assigning title and the cost to the object:

class Books:
  def __init__(self, title, cost):
    self.title = title
    self.cost = cost

python_book = Books("Learn and Practice Python Programming", 136)
print(python_book.title)
print(python_book.cost)

Object Functions
A class will also have object functions or methods. Functions that are defined in objects, belong to object (and not to the class). It means that the objects will use these functions and the result returned will be specific to the objects. These are characterised by self keyword as first parameter being passed. Let’s add a function to our class Books which will print the title of the book. This is called object function because it knows which objects are referencing and the title value is linked to that object only.
Example:

class Books:
  def __init__(self, title, cost):
    self.title = title
    self.cost = cost
  def gettitle(self):
      print("Title of the book is: "+self.title)

python_book = Books("Learn and Practice Python Programming", 136)
print(python_book.title)
print(python_book.cost)
python_book.gettitle()

The self parameter used in object functions is to show the reference to the current object associated to the class.
Please note, it does not have to be named self, we can use any name we like it but whatever name we choose, that has to be the first parameter of the object functions.

We will end our discussion here. We will develop a mini application using classes in next article.

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Sep 21 2021
Python 3.10 – what can we expect from it?

Image above shows how CASE statement can be implemented in Python, wait you cant make it work today but surely can after 4th October, that is after Python 3.10 is made available for all of us. In the 3.10 version, we will see some of the most fascinating features like structural pattern matching, parenthesized context managers, more typing, and improved error messages to make our life easier.

In 2020, Guido van Rossum, the creator of Python, committed the first documentation showing the new switch-statements, which have been named Structural Pattern Matching. Python is not using the switch keyword for its Structural Pattern Matching as other programming languages do, instead, it will be using match keywords. So it might be possible that we would refer to it as a Python match case statement instead of a Python switch case.

The new structural pattern matching will help us to match variables against one or even one of a set of possible values. This will no doubt be very powerful feature and will make it possible to write code for a variety of scenarios. One example is offcourse the code added as an image above. Lets look at another example:

Let’s try to use match case Statement and implement the weekday calculator as example:

def weekday(day):
    match day:
        case 1:
            return "Sunday"
        case 2:
            return "Monday"
        case 3:
            return "Tuesday"
        case 4:
            return "Wednesday"
        case 5:
            return "Thursday"
        case 6:
            return "Friday"
        case 7:
            return "Saturday"
        #wildcard case
         case _:
            return "Please Enter a Valid Day Number"

 print(weekday(1))   #Sunday
 print(weekday(4))  #Wednesday
 print(weekday(7))  #Saturday
 print(weekday(11)) #Please Enter a Valid Day Number

Impressed? There is more to come. Using, what is called as structural pattern matching, we can perform the same match-case logic even based on whether the structure of our comparison object matches a given pattern.

To demonstrate, lets take two dictionaries:

dict_a = {
    'id': 1,
    'meta': {
        'source': 'abc',
        'location': 'west'
    }
}

dict_b = {
    'id': 2,
    'source': 'def',
    'location': 'west'
}

 We could write a pattern to match dict_a like:

{
    'id': int,
    'meta': {'source': str,
             'location': str}
}

A pattern to match dict_b like:

 {    'id': int,
    'source': str,
    'location': str
}

We can now put both of these together in a match-case statement:

# loop through both dictionaries and a 'test'
for d in [dict_a, dict_b, 'test']:
   match d:
       case {'id': ident,
             'meta': {'source': source,
                      'location': loc}}:
           print(ident, source, loc)
       case {'id': ident,
             'source': source,
             'location': loc}:
           print(ident, source, loc)
       case _:
           print('no match')

Watch out for my next few articles in which I will cover:

  • Parenthesized context managers
  • More Python typing features
  • Display better error messages

Stay tuned. Do comment here if you liked this article.

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Sep 19 2021
Understanding the term Data Science?

Another article talking about the basic concept of machine learning and data science.

As the world has entered the time period of big data, so did the demand for data containers. Until 2010, it was the main threat and consideration for the corporate businesses. The primary focus was on developing a framework and data storage systems.
When is it going to happen? The secret sauce here is data science. Data Science can make all of the ideas that you see in Hollywood sci-fi movies a reality. The destiny of Artificial Intelligence is Data Science. As a result, it is essential to recognize what Data Science is and how it might benefit your company?


What is Data Science?
Data Science is a collection of tools, techniques, and deep learning fundamentals that aim to uncover hidden styles in original data. But how does this differ from what statistical methods have done for years? As shown in the preceding image, a Data Analyst typically explains what is happening on by tracing the data’s handling background.
A Data Analyst, on the other hand, not only performs exploratory analysis to glean insights from it, but also employs a variety of advanced machine learning techniques to predict the occurrence of a specific event in the future. A Data Scientist will examine the data from a variety of perspectives, including some that were previously unknown.


Do you need a data science certificate?
A certification on your resume is unlikely to help you land a job. Employers are interested in the skills you possess. A registration, by itself, tells an employer nothing about your abilities. It simply informs them that you researched a subject. Certifications, on the other hand, can be extremely valuable if they effectively teach you the skills you require.
Certification programmes and platforms can still be a great investment, but please remember that their value is in the skills they can instruct you. Employers will look at your skills, project portfolio, and transferable skills when they review your resume. A certificate is unlikely to sway their decision, so focus on developing the necessary skills and creating exciting projects.


Why is data science important?
Data science is essential in almost all aspects of business and techniques. For example, it offers data about consumers that enables businesses to create more effective marketing plans and targeted marketing in order to increase product sales. It aids in the management of financial risks, the detection of fraudulent purchases, and the prevention of equipment failure in production facilities and other industrial sites.
It aids in the prevention of cyber-attacks and other security threats in IT systems. Data science initiatives can improve operational management in the supply chains, product inventory levels, distribution channels, and customer service. On a more basic level, they point the way toward greater efficiency and lower costs.
Data science also allows enterprises to develop strategic initiatives based on an in-depth analysis of customer behavior, market trends, and competition. Without it, business owners risk missing out on possibilities and making bad choices.


Challenges in data science:
Due to the obvious advanced essence of the data analysis involved, data science is especially challenging. The massive amounts of data that are typically analyzed contribute to the complexity and lengthen the time it would take to execute tasks. Furthermore, data scientists regularly work with pools of big data that may encompass a mix of structured, unstructured, and semi-structured data, confounding the analytics platform even further.
Removing bias in data sets and advanced analytics is one of the most difficult challenges. This includes both problems with the original data and problems that data scientists unconsciously build into algorithms and prescriptive models. If such biases are not identified, they can skew analytics results, resulting in flawed findings and poor business decisions. Worse, they can have a negative impact on specific groups of people, as in the particular instance of ethnic partiality in AI technologies.


Why businesses need Data Science?
We’ve progressed from working in small frames of structured data to huge mining areas of unorganized and semi-structured info coming in from a variety of sources. When it tends to come to having to process this huge pool of unstructured information, conventional Business Intelligence tools fall short.
As a result, Data Science includes more sophisticated tools for working with large volumes of data from various sources such as economic logs, multimedia content, advertising forms, detectors and tools, and text files.


What does a data scientist really do?
Algorithms are created and used by data scientists to analyses data. In general, this process entails using and developing machine learning tools and personalized data goods to interest of business and clients in interpreting data in a useful manner.
They also aid in the breakdown of data-driven reports in order to gain a better understanding of the clients. Overall, data scientists are involved in every stage of data processing, from processing it to creating and maintaining facilities, testing, and analyzing it for real-world applications.

All these are more information are available in the book “Data Science and Machine Learning with Python” by Swapnil Saurav. Available on Amazon here

You can find more articles by the author on the topic here.

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Sep 19 2021
What is artificial intelligence?

In continuation to our earlier discussions, and talking about basic terms first before we get into higher level of discussions, in this article I talk about what is AI from my perspective.

Artificial intelligence (AI) is a subfield of computer science concerned with the simulated world of human intelligence in machines. Another way to think about AI is as a quest to create machines that can perform specific tasks that require human intelligence. AI has the potential to free us from monotonous tasks, make quick and accurate decisions, act as a catalyst for accelerating discoveries and inventions, and even complete dangerous processes in extreme environments. There is no magic in this place.
It’s a group of intelligent algorithms attempting to mimic human intellect. AI employs techniques such as machine learning and deep learning to learn from data and improve on a regular basis. And AI is more than just a subfield of computer science. Rather, it incorporates elements of statistics, mathematics, intelligent systems, neuroscience, cybernetics, psychology, philology, philosophy, economy, and other disciplines.


Different types of AI:
At a very high level, artificial intelligence can be split into two broad types:

Narrow AI:
Narrow AI is what we see everywhere us in computing systems. It refers to expert machines that have been taught and learned how to perform specific tasks without being pattern recognition to do so. This class of machine intellect is visible in the Siri digital assistant on the Apple iPhone’s communication – language acknowledgment, visual acuity systems on ego cars, and recommendation engines that accurate recommendations you might like predicated on what you’ve purchased in the history. These processes, unlike humans, can only learn or be taught how to perform specific tasks, which is why they are referred to as narrow AI.

General AI:
General AI is very unique and is the a kind of flexible intellectual ability found in people, a flexible form of intelligence smart enough to learn how to hold out massively various tasks, such as hairdressing, building spreadsheet applications, or reasoning about a wide range of topics based on its collective experience. This is the type of AI seen in films, such as HAL in 2001 or Skynet in The Terminator, but it does not exist today – and AI specialists are divided on the how shortly it would become a actuality.

How Artificial Intelligence (AI) Works?
Constructing an AI system is a meticulous process that involves reverse-engineering human characteristics and abilities in a machine and then using its supercomputing prowess to outperform what we are competent of. To comprehend How Artificial Intelligence Works, one must first delve into the various subsites of Ai Technology and comprehend how those domains can be applied to various areas of business. You can also enroll in an artificial intelligence course to develop a full understanding.

What is the Purpose of Artificial Intelligence?
The goal of Artificial Intelligence is to augment human ability and assist us in making complex decisions with far-reaching consequences. From a technical standpoint, that is the answer. From a philosophical standpoint, Artificial Intelligence is the ability to help human’s live more good lives free of hard labor, as well as to assist in managing the complex web of interconnected individual people, businesses, states, and nations to function in a way that benefits all of civilization.
Currently, the objective of Artificial Intelligence is communicated by all the various techniques that we’ve created over the last long time – to optimize human effort and assist us in making important choices. Artificial intelligence has also been dubbed our “Final Discovery,” a conception that would create ground-breaking tools that would tremendously change how we live our lives, ideally eradicating conflict, unfairness, and human misery.

Where is Artificial Intelligence (AI) Used?
AI is used in a variety of domains to provide insights into user behavior and make suggestions based on data. Google’s forecasting search algorithm, for example, used previous user data to determine what a user would type next in the search field. Netflix uses past user data to suggest what movie a user should watch next, keeping the user on the forum and increasing watch time. Facebook uses past user data to instantly suggest tags for your friends predicated on their facial characteristics in their images. AI is being used by large organizations all over the world to make the lives of end users easier.
AI could be used to rapidly and conveniently complete tasks that humans find tedious, such as trying to sort through huge amounts of data and recognizing styles. It also enables machines to become cleverer in ways that would make them more convenient, easier to use, and competent of accomplishing more than ever before. This applies to our personal devices and larger industry technologies, as well as virtually everything between.

All these are more information are available in the book “Data Science and Machine Learning with Python” by Swapnil Saurav. Available on Amazon here

You can find more articles by the author on the topic here.

admin 0 Comments
Sep 18 2021
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Sep 18 2021
What is machine learning

Hello again. Welcome to my blog, this is my third post. I will talk about different applications of Machine Learning as well as share code and applications in future posts. But before that, I would like to talk about basics so those you are looking to understand the concepts would find it very useful. In this post, we will see what is machine learning.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Machine learning, at its most basic, is the method of training a computer system to make correct estimates when given data. Those forecasts could include determining whether a piece of fruit in a photograph is a banana or an apple, detecting people crossing a road in front of a self-driving car, determining whether the use of the term book in a statement refers to a printed book or a hotel reservation, determining whether an email is spam, or accurately recognizing speech to start generating subtitles for a Youtube clip.

The main difference between this and conventional computer software is that a human creator did not write the code that tells the scheme how to discern the difference between a banana and an apple. Instead, a machine-learning model has been trained to reliably distinguish between the fruits by being educated on a large amount of data, most likely a huge number of images labeled as comprising a piece of fruit.


Why Do We Need It?
As the name implies, machine learning is an active process in which computers learn and analyses data fed to them in order to determine the future. There are various types of learning, such as supervised, unmonitored, semi-supervised, and so on. Machine learning is a stepping stone to artificial intelligence; it learns from algorithms based on databases and originates answers and comparisons from them. Equipment and digital conversion are inextricably linked, and machine learning is at the heart of both.
Google announced its graph-based machine learning tool in 2016. It connected data clusters based on the similarities using the semi-supervised active learning. Machine learning algorithms assists industries in identifying market dynamics, possible risks, customer requirements, and business insights. Today, business analytics and mechanization are the norms, and machine learning is the foundation for achieving these goals and increasing your operational productivity.


How Machine Learning Works?
Machine Learning is without a hesitation one of the most intriguing subsets of Artificial Intelligence. It performs the tasks of data studying by providing specific inputs to the machine. It is critical to recognize how Machine Learning works and, as a result, how it could be used in the long term.
The Machine Learning process begins with the input of training data into the chosen algorithm. To see if the machine learning model is working properly, incoming data information is passed into it. The forecasting and the results are then cross-checked. If the prediction and results do not match, the methodology is re-trained several times until the data scientist obtains the desired outcome.  


Why is Machine Learning Important?
Recognize the ego Google car, cyber fraud prevention, and online suggestion engines from Facebook, Netflix, and Amazon to gain a better understanding of Machine Learning’s applications. All of these things can be enabled by machines by filtering valuable information and cobbling it all together premised on the patterns to produce reliable data.


Different Types of Machine Learning
 

Supervised Learning:
The machine learning model in supervised learning is recognized or labeled data. Because the data is known, the knowledge is monitored, i.e. directed toward successful implementation. The input data is processed by the Machine Learning algorithm, which is then used to train the model.
Once the model has been trained on existing data, you can feed unknown data into it to get a reasonable response. In this particular instance, the model attempts to determine whether the data is an apple or another type of fruit. Again when the model has been properly trained, it will recognize the data as an apple and respond accordingly.


Unsupervised Learning:
The training set in unsupervised classification is unidentified and unidentified, implying that no one has previously examined the data. The contribution cannot be steered to the automated system without the component of known data, which is where the term “unsupervised” comes from.
This information is fed into the Machine Learning algorithm, which is then used to train a model. The model attempts to find a pattern and provide the expected reaction. In this case, it frequently appears that the algorithm is attempting to break code in the same way that the Enigma machine did, but without the human brain involved directly, but rather a machine. In this case, the unknown data consists of apples and pears that resemble one another. The trained model attempts to group them all so that you get the same things in similar organizations.


Reinforcement Learning:
In this case, the algorithm, like in conventional kinds of data assessment, uncovers data through trial and error and then makes the decision which action leads to higher rewards. Learning algorithm is made up of three major elements: the agent, the surroundings, and the behavior.
The student or judgment is the agent, the environment encompasses everything with which the agent comes into contact, and the activities are what the representative does. Reinforcement learners learn when the agent selects actions that maximize the immediate value over a specified time period. This is simplest to accomplish when the agent works within a solid policy structure.

Machine Learning types and different algorithms

I am leaving you with this picture, we will talk about these in coming posts.

For detailed discussion, you can refer my book on Machine Learning (using Python examples):
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(first appeared on Lambda and Sigma post – read here)

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Sep 18 2021
What Is IT Service Management?

Hello all, I am back with my second article. Couple of days back, I had posted my first article in which I briefly spoke about Machine Learning applications in IT Service Management (ITSM). I got great response from the readers, thank you so much for that. Some of you had asked about ITSM and what are its main functions. So I decided to write my this article talking about ITSM itself. Please read what I think ITSM is all about. When you’re brand new to IT service management (ITSM), it’s difficult to know where to start, much less even how to succeed. In this blog article, we’ll define terms like ITSM, ITIL, and others that may be unfamiliar to you. Most importantly, we’ll share 8 very easily achievable steps we suggest taking to enhance your ITSM and deliver exceptional IT service across your organization.

Why Do I Need IT Service Management?
Ad hoc IT services are often sufficient for very small organizations. For example, a company with a separate office may only require one ‘IT person,’ who accomplishes work and resolves problems as they arise. This method, however, quickly would become an obligation as organizations grow.
Comparatively tiny IT teams often struggle to keep track of all that remains to be improved on an ad hoc basis. Essential tasks and responsibilities begin to fall through the cracks, putting the organization’s overall efficiency at risk. At this point, most organizations begin their ITSM journey by detailing ways of implementing simple tools to assist them in managing the shipment of IT hardware, applications, and assistance.

What is an ITSM Ticketing Tool?
Ticketing is a critical element of any ITSM tool that every organization requires. ITSM ticketing tools record all conversations among a helpdesk and its internally or externally clients. A ‘ticket’ is simply a permanent record of an IT activity or accident that contains pertinent details about what occurred, who reported the problem, and what was work to fix it. This ensures that no incidents are ‘lost’ or forgotten, and it aids in maintaining a consistent level of service for all helpdesk consumers.
The majority of IT services have contracting SLAs that specify how rapidly new occurrences must be resolved. Ticketing systems keep records of these performance measures by instantly recording the time and date when a ticket is revised and providing easy-to-access reporting. While necessary, ticketing is only a minor component of ITSM tools. This means that, while all ITSM tools include a ticket booking module, a pure-play ticket booking tool is insufficient to ensure effective and efficient IT processes.

How to enhance ITSM with easy steps?
The right ITSM strategy is an important component of ongoing digitalization efforts and can improve both effectiveness and persistence throughout the organization. Following the eight key steps outlined below can assist businesses in increasing ITSM sophistication.
1.    Assessing current ITSM maturity
2.    Setting clear goals
3.    Securing executive buy-in
4.    Establishing a plan
5.    Assembling the right team
6.    Implementation automation
7.    Finding the right software
8.    Implementing Continual Service Improvement (CSI) Success

What does ITSM do for your business?
ITSM provides a variety of structures for companies to use in developing quality management for IT service quality and customer service practices. Quality management, software development, project management, security vulnerabilities, and widely used management structure standards are all covered.
They are intended to bring structure and framework to service-oriented IT divisions by aligning IT goals with company needs and requirements. It is used as a guide to assist businesses in effectively aligning IT goals and business goals, particularly for customer service enterprises.

ITSM service desk:
The service desk, as defined in the ITIL manual, is one primary discipline that falls under ITSM. Support desks are viewed as a Single Point of Contact (SPOC) by ITIL, which can help to simplify interaction within an organization or business unit. Service desks serve as a central point for clients and stakeholders to contact well-trained staff in order to resolve issues in an integrated and efficient sort of way.

ITSM frameworks:
ITIL is the most widely used ITSM structure, but there are numerous other ITSM methodologies that business owners could use. A few of these paradigms are geared toward specific sectors or business requirements, such as healthcare, gov’t, and telecommunication services. If your company has technology requirements that are unique to your industry, you should look for a structure that discusses your particular challenges.

ITSM certification:
You can obtain a credential in the ITSM field of study, and there are choices for company education and training as well as personal training and certification. However, before you can find the right certification programme, you must first understand the framework you intend to use. While ITSM as a discipline can be certified, most programmes are focused on a particular structure.

The importance of ITSM:
ITSM is advantageous to your IT team, and managed services fundamentals can benefit your entire organization. ITSM increases efficiency and productivity. A organized concept of service planning also aligns IT with business goals by standardizing service delivery based on expenditures, resources, and results. It lowers costs and risks while also improving customer familiarity.

ITSM processes:
This broader approach more accurately reflects the realities of modern organizations. We won’t get into the slight variations in terminology used for practices or procedures here. What matters, and is true irrespective of the structure your team uses, is that contemporary IT service teams use organizational resources and follow repeatable procedures to provide consistent, efficient service. In fact, the ability to leverage practice or process is what differentiates ITSM from IT.

Final Thoughts:
ITSM is at the heart of organizational modernization. As the adoption of software-powered services grows, IT service teams are facilitating staff members across organizations to give value more rapidly. The IT team’s role has shifted from providing the necessary support to trying to distinguish the company. It is time to shift ITSM strategies to emphasize cooperation, ease of use, and quicker customer satisfaction.

(Appeared first at Lambda and Sigma, read here)

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Sep 16 2021
IT service management (ITSM) and machine learning

 IT Service Management (ITSM) is a form of strategy and operation for executing, delivering, and organizing IT services for end-users that meet the stated needs of the end-users and the stated goals of the business. Technology has enhanced the way companies operate in all industries around the world. At the same time, traditional IT service management (ITSM) solutions have failed to maintain customer satisfaction levels and meet the growing expectations of consumers in the fast-paced digital world.

Machine learning

Machine learning is an artificial intelligence application that enables systems to learn and improve from experience without explicitly programming spontaneously. Machine learning (ML) focuses on developing computer software that can access data and use it to learn for themselves. Machine learning has already begun to make a difference in our daily lives more than anyone could have imagined. Say, for example, a pair trains their sprinkler system to turn on automatically when cats are prevented from entering their lawn. In simple words, Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to be more exact in predicting results without explicitly programming them.

ITSM

The service desk management is about creating the “one-go-to” place for all the IT related needs, helps and issues. The desk is responsible for managing incidents or service disruptions, fulfill any IT related requests, and changes. The service desk scope of work is generally enormous and wide-ranging hence it needs to be managed effectively and efficiently. There are some benefits of ITSM.

1. ITSM makes it easy for teams to provide quick, proactive, shock-free responses to unforeseen events, new opportunities, and competitive threats.

2. By authorize enhanced method concerts, better accessibility, and fewer service barriers, ITSM helps clients work harder and do more business.

3. By systematically accelerating incident resolution, minimizing incidents and problems, and preventing or resolving issues automatically, ITSM helps businesses achieve greater productivity at a lower cost than IT infrastructure.

4. By incorporating observance into IT service aim, delivery, and management, ITSM can get better compliance and trim down risk.

5. ITSM helps the institute set and convene a sensible outlook for the service, leading to superior clearness and enhanced customer contentment.

Benefits of machine learning

There are some benefits of machine learning.

Automation for everything

One of the most potent benefits of machine learning is its ability to automate various decision-making tasks. It gives developers more time to use their time for more productive use. For example, we see some expected benefits in our daily lives: social media emotion analysis and chatbots. A chatbot responds immediately as first-level customer support when a negative tweet is related to a company’s product or service. Machine learning is shifting the world with its computerization for approximately everything we can think of.

Recommending the Right Product

Product recommendation is an essential aspect of any sales and marketing strategy, including up-selling and cross-selling. ML models will analyze a customer’s purchase history, and based on that; they will identify products from your product inventory that the customer is interested in. This process is known as non-supervised learning, which is a particular type of ML algorithm. Such a model would enable businesses to make better product recommendations for their customers, thus encouraging product purchases. Therefore, supervised learning helps to create a high product-based recommendation system.

 Application of Machine Learning in ITSM

We need to understand that in this ever changing world of technology, the traditional IT service management (ITSM) solutions and practices have become inefficient and does not work effectively to keep the customer satisfaction levels at the highest hence its obvious that the organizations are moving towards ML application to enhance their business scalability and improve business operations. Machine Learning algorithms have enabled the ITSM pratice to improve its speed and quality of service while keeping the cost low. Still there is lost of scope for the usage. In this short article, I will cover top 7 use cases which can elevate the service level of service desk.

 a) Predictive analytics – The first application of ML in any field that comes up for discussion is predictive analytics. What kind of predictive analytics can be make in ITSM? Here we can predict the number and nature of incidents, problems and issues. Even the risks associated with proposed changes, measure and predicting the future levels of customer satisfaction across different types of service desk offerings can be other areas of implementing predictive analytics.

 b) Demand planning – This use case is an extension of predictive analytics itself where we use machine learning algorithms to predict the future demand for both IT services and IT support capabilities which can help the management in the budgeting decisions as well as manage the entire process effectively with minimum cost. The result from demand planning can also help us to gauge the required levels of variables like cost, pricing, benefit, capacity, stock, etc.


 c) Predictive maintenance – We can treat this as extension of demand planning on engineering side than operations, which is the theme in demand planning. Machine learning algorithms enables to selectively apply maintenance to the IT infrastructure and critical business services to prevent disruptions in services or failures. The various processes that are being designed around this is mostly about the ability to track important parameters in real time and with the help of algorithms working on these real time data and give us the trigger to take prevention actions.

 d) Improved search capabilities – This might sound a trivial capability but its one of the important features as we are talking beyond the traditional search options and results. We are talking about intelligent search capabilities, which can predict the search criteria and provide the data related to relevant search keywords. This would give us number of relevant search result with a high degree of accuracy to use the content for our work.

 
 e) Providing recommendations – Everyone is probably aware of the product recommendations algorithm usage by Amazon to predict what we might want to buy. Similarly, Netflix and Spotify can give us best entertainment content. Extending the similar logic, the product recommendations in ITSM field can provide self-help in recommending knowledge base or solutions for service desk agents, or for end users. Thus, this can help us in speeding up processes to deliver resolutions or services more quickly and accurately which can not only help to improve customer satisfaction but also improve employee efficiency.

 f) Identifying and filling knowledge gaps – Machine learning algorithms capabilities are generally linked to identifying  and distribution of knowledge from the data but we can also use it to create the knowledge. Some of the top applications under this use case, we can think of are identification of knowledge-article gaps based on the analysis of aggregated incident ticket data. While converting a  resolution note of documented ticket into knowledge, algorithms can be used to identify the most pertinent and valuable information from which to create a new knowledge article.

 g) Intelligent autoresponders – Probably we might be trying to stretch the capabilities little too far in this case but its possible to achieve what we are trying. Depending upon the issue type and the nature of the problem, we could use technology to work on a ticket, understand the problem, find the solution, apply it and close the ticket without any human intervention with highest accuracy level. It’s a high-value use case scenario hence even we can use technology to close 1-2% of the issues created, it can give us a great benefit in terms of time, cost and convenience.

First published on BlogPost

https://lambdaandsigma.blogspot.com/2021/09/it-service-management-itsm-and-machine.html

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