Here Are the Metrics you Need to Understand Operational Health

In recent polls we’ve conducted with engineers and leaders, we’ve found that around 70% of participants used MTTA and MTTR as one of their main metrics. 20% of participants cited looking at planned versus unplanned work, and 10% said they currently look at no metrics. While MTTA and MTTR are good starting points, they’re no longer enough. With the rise in complexity, it can be difficult to gain insights into your services’ operational health.

In this blog post, we’ll walk you through holistic measures and best practices that you can employ starting today. These will include challenges and pain points in gaining insight as well as key metrics and how they evolve as organizations mature.

Pain Points for Creating Useful Metrics

It’s easy to fall into the trap of being data rich but information poor. Building metrics and dashboards with the right context is crucial to understanding operational health, but where do you start? It’s important to look at roadblocks to adoption thus far in your organization. Perhaps other teams (or even your team) have looked into the way you measure success before. What halted their progress? If metrics haven’t undergone any change recently, why is that?

Below are some of the top customer pain points and challenges that we typically see software and infrastructure teams encounter.

  • Lack of data: Your data is fragmented across your APM, ticketing, chatops, and other tools. Even worse, it’s typically also siloed across teams that run at different speeds. A lot of it is tribal knowledge, or it simply doesn’t exist.
  • No feedback loop: There’s limited to no integration between incidents, retrospectives, follow-up action items, planned work, and customer experience. It’s challenging to understand how it all ties together as well as pinpoint how to improve customer experience. You’re constantly being redirected by unplanned work and incidents.
  • Blank slate: Traditional APM and analytics tools are great for insights, but without a baseline of metrics that are prescriptive and based on operational best practices, it’s hard to know where to start.
  • One-size-fits-all: What works for one team won’t necessarily work for another. Everything needs to be put in the right context to provide truly relevant insights.

With these pain points in mind, let’s look at some key metrics other organizations we’ve spoken to have found success with.

#devops #metrics #site reliability engineering #site reliability #site reliability engineer #metrics monitoring #site reliability engineering tools

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Here Are the Metrics you Need to Understand Operational Health
Ray  Patel

Ray Patel

1619565060

Ternary operator in Python?

  1. Ternary Operator in Python

What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a condition in a single line replacing the multiline if-else code.

syntax : condition ? value_if_true : value_if_false

condition: A boolean expression evaluates true or false

value_if_true: a value to be assigned if the expression is evaluated to true.

value_if_false: A value to be assigned if the expression is evaluated to false.

How to use ternary operator in python here are some examples of Python ternary operator if-else.

Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.

#python #python ternary operator #ternary operator #ternary operator in if-else #ternary operator in python #ternary operator with dict #ternary operator with lambda

Here Are the Metrics you Need to Understand Operational Health

In recent polls we’ve conducted with engineers and leaders, we’ve found that around 70% of participants used MTTA and MTTR as one of their main metrics. 20% of participants cited looking at planned versus unplanned work, and 10% said they currently look at no metrics. While MTTA and MTTR are good starting points, they’re no longer enough. With the rise in complexity, it can be difficult to gain insights into your services’ operational health.

In this blog post, we’ll walk you through holistic measures and best practices that you can employ starting today. These will include challenges and pain points in gaining insight as well as key metrics and how they evolve as organizations mature.

Pain Points for Creating Useful Metrics

It’s easy to fall into the trap of being data rich but information poor. Building metrics and dashboards with the right context is crucial to understanding operational health, but where do you start? It’s important to look at roadblocks to adoption thus far in your organization. Perhaps other teams (or even your team) have looked into the way you measure success before. What halted their progress? If metrics haven’t undergone any change recently, why is that?

Below are some of the top customer pain points and challenges that we typically see software and infrastructure teams encounter.

  • Lack of data: Your data is fragmented across your APM, ticketing, chatops, and other tools. Even worse, it’s typically also siloed across teams that run at different speeds. A lot of it is tribal knowledge, or it simply doesn’t exist.
  • No feedback loop: There’s limited to no integration between incidents, retrospectives, follow-up action items, planned work, and customer experience. It’s challenging to understand how it all ties together as well as pinpoint how to improve customer experience. You’re constantly being redirected by unplanned work and incidents.
  • Blank slate: Traditional APM and analytics tools are great for insights, but without a baseline of metrics that are prescriptive and based on operational best practices, it’s hard to know where to start.
  • One-size-fits-all: What works for one team won’t necessarily work for another. Everything needs to be put in the right context to provide truly relevant insights.

With these pain points in mind, let’s look at some key metrics other organizations we’ve spoken to have found success with.

#devops #metrics #site reliability engineering #site reliability #site reliability engineer #metrics monitoring #site reliability engineering tools

Abdullah  Kozey

Abdullah Kozey

1617738420

Unformatted input/output operations In C++

In this article, we will discuss the unformatted Input/Output operations In C++. Using objects cin and cout for the input and the output of data of various types is possible because of overloading of operator >> and << to recognize all the basic C++ types. The operator >> is overloaded in the istream class and operator << is overloaded in the ostream class.

The general format for reading data from the keyboard:

cin >> var1 >> var2 >> …. >> var_n;

  • Here, var1var2, ……, varn are the variable names that are declared already.
  • The input data must be separated by white space characters and the data type of user input must be similar to the data types of the variables which are declared in the program.
  • The operator >> reads the data character by character and assigns it to the indicated location.
  • Reading of variables terminates when white space occurs or character type occurs that does not match the destination type.

#c++ #c++ programs #c++-operator overloading #cpp-input-output #cpp-operator #cpp-operator-overloading #operators

Erwin  Boyer

Erwin Boyer

1624593300

Indian AI-based Mental Health App Wysa Receives Funding From Google Assistant

Wysa, an Indian-based AI-enabled mental health application, has announced that the startup has secured an undisclosed amount of investment from the Google Assistant Fund.

Launched in 2018, the Google Assistant Investments program was designed to assist early-stage startups in advancing their digital assistant ecosystem and innovating new ideas. Wysa has also recently topped the list of best apps in India for 2020.

Wysa was also among the first startups to be included in the Google For Startups launchpad program in India in 2018.

#news #ai-based chatbot #chatbot mental health #chatbots #google assistant #google assistant fund #mental health #mental health apps #mental health chatbots #wysa #wysa funded by google assistant fund #wysa funding

Erwin  Boyer

Erwin Boyer

1624682160

Chatbots In Mental Health. Friendly But Not Too Friendly. 

Mental health is the proverbial elephant in the room that no one wants to address. India is on the verge of a mental health epidemic, yet one would hardly find a public discourse on ways to prevent it or treat it. There are hardly any steps being taken at the scale required to manage this the increasing number of people with mental health issues. Corona is to blame for this massive spike in the number of cases. The isolation because of lockdown, fear and uncertainty due to job cuts and general discomfort due to the inability to control several aspects of life has triggered severe mental trauma in people across the country. The issue is even more complicated by the fact that no one has any idea when things will ever return to normal, if at all.

There is a huge gap in the treatment that should be available and of the help available at hand, easily and cost-effectively. Even in developed countries, the ratio of psychiatrists, psychologists, psychiatric social workers, and mental health nurses to patients is 1: 10,000. The lacuna in the system ensures that most people suffering from mental health issues are never able to get the help they need.

Many digital interfaces are emerging as viable complementary services to fulfil some Artificial intelligence-based solutions are developed by working closely with healthcare professionals to provide a person with assistance, and often, some sort of companionship. This can also bring down the cost of psychiatric diagnosis and treatment. Most people have faced stigma prevalent in our society when it comes to its psychiatric disorders, which often hampers with effective treatment.

Chatbots are Natural Language Processing(NLP) based frameworks that interact with human users using spoken, written, and visual languages. Built specifically to communicate with people struggling with mental problems, Chatbots have the potential to be useful tools. According to experts, “suicide prediction and prevention, identification of predictors for a response, and identifying which particular drug is best suited for a particular patient are some of the areas where AI has been found to be useful in psychiatry.”

#opinions #chatbot #chatbot mental health #chatbots in mental health. friendly but not too friendly. #health