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More and more of us are using messaging apps. If it’s from WhatsApp to Telegram - they’ve become a part of our lives. As these messaging apps rise, chatbots are also rising. We are seeing more businesses and consumers benefiting from their capabilities.

What is ChatOps?

ChatOps stands for Chat Operations. It is the use of chatbots, chat clients or other real-time communication tools which help in facilitating software development and operational tasks. It is the automation of these operational tasks and some of these ChatOps have control of a company's entire infrastructure. 

There is a range of development and operational tools which are put into a process to provide a collaboration platform. This allows teams to communicate more effectively and have better management of their workflow. Examples of popular collaboration platforms are Slack and Microsoft Team. 

The primary goal of ChatOps is to move conversations from typical message applications such as emails to business chat tools, allowing an application to take on these tasks and take the workload off employees. 

ChatOps tools

Three main categorized tools for deploying a ChatOps environment are:

• Notification systems
• Chatbots - for example, Hubot 
• Chatroom integration tools - for example, Slack

The most typical form of ChatOps that a lot of people have had interaction with is Chatbots. They are an artificial intelligence system which enables user engagement through messages, text and speech. 

The pioneer of ChatOps was GitHub, as a way to automate most operations-related tasks with a chatbot.

The Difference Between ChatOps and Chatbots

ChatOps is all about focusing on how the company can streamline operations and increase its collaboration between teams. It helps guide conversation-driven collaboration tools, such as chatbots.

ChatOps aim is to help facilitate the steps in a workflow, and the actions required to come to a quick solution. Chatbots allow users to simply have a conversation and it directs them to exactly what they need.

Chatbots are not a requirement for ChatOps. They are a tool use to help create automated processes which you gained from your ChatOps insight. 

What is LMOps?

So now you’re probably wondering why I put LMOps in the title, right? Using data and statistical tests, chatbots have the ability to predict the probability of a sequence of words. They are built on language models. Hence the LMOps (Language Model Operations). 

LMOps is about using fundamental research and technology to build AI products and enable AI capabilities specifically on Large Language Models (LLMs) and Generative AI models.

Microsoft Research open-sourced LMOps, providing a collection of tools to help improve text prompts used as an input to generative AI models. 

Research has shown that as language models (LMs) get bigger, they naturally become more capable of learning in context.

If you would like to learn more about LLM’s, check out this blog: Top Free Courses on Large Language Models. You can also have a look at: Top Open Source Large Language Models.

A recent popular example of LLMs is the ChatGPT AI chatbot. And we all know how big that turned out to be. Everybody is talking about it. So is this the rise of ChatOps?

Natural Language Processing in ChatOps

Natural Language Processing (NLP) is a computer/software/application's ability to detect and understand human language, through speech and text just the way we humans can. 

NLP helps your chatbot and other chat tools to analyze and understand the natural human language, and how it can communicate back to the customer.

But you need to understand that NLP may not be important in every ChatOps - it depends on what you’re using it for. If you are using ChatOps to help with customer interactions, engagement, call centers, etc - you will need NLP. It allows for ChatOps tools to answer as many questions as possible, with accuracy. 

However, you will be able to build better and more accurate ChatOps tools using NLP and LLMs.

ChatOps are helping companies to automate tasks, and communicate better internally and externally. With the recent advances of LLMs, there is a high chance they will power the next generation of chatbots.

Benefits of ChatOps

Automation

Automating your tasks is a big time saver. If your documentation and resources were held in a repository, it can be computerized in a centralized communication platform. ChatOps tools can then execute tasks and operations which can be repetitive and focus on working more collaboratively. 

Contextual Collaboration

Having to skim through large amounts of files and documents to find what you want can be a big hassle and time consumer for many companies. The bigger your company is, the more documentation. 

Rather than placing your resources in multiple channels and having to gander through them to obtain contextual information. ChatOps allow you to easily reach up-to-date context within seconds. 

Productivity and Employee Engagement

The collaboration between different teams across a company makes the employees more productive and engaged. Tech companies have access to contextual data in real-time, saving them hours on their workflow. 

With ChatOps implementing automated tasks, it takes a lot of the tedious tasks off employees - allowing them to be more engaged and focused on other tasks. 

Wrapping it up

We’ve had the release of ChatGPT and learning more about GoogleBard. People have ran to the opportunity to try these Chatbots, with a lot of people and companies benefiting from them. With that being said, as chatbots stem off ChatOps, we are likely to see more going into ChatOps.
 
 
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.