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Oops! You might lack these Ops

7 minute read
Devops vs ML ops

It’s important to make sure you have the right Ops team in place to support your company’s growth

If you’re running a tech company, but still struggle with achieving your goals from year to year, there’s a good chance you don’t have the Ops team you need. Ops roles are critical to scaling a business, but they can be difficult to fill.

In this post, we’ll teach you about the different Ops roles and how they can help your company. We’ll also show you the examples of AI, ML, Data, Git, and other Ops and explain how you can use them to scale your business.

Why just DevOps is no longer enough

The DevOps role appeared as an answer to the interaction problems between development and operation teams. A few years ago, releases were often delayed because code creators and code maintainers shifted the blame to each other whenever program errors appeared.

Developers believed that if they ran the code locally, they could also run it seamlessly in production. However, if problems arose, the operation team wanted the developers to figure them out.

DevOps became an interlink that assumed responsibility for safe code delivery from producers to the consumers. They combined the roles of build, release, and automation engineers. For a time, the DevOps movement revolutionized the way companies develop and deploy software. But now DevOps alone is not enough for effective software management. The reasons for that are:

  • A larger number of participants in the development process.  DevOps removed silos between development and operations. However, in most organizations, there are many other silos, such as product owner-development, business development-sales, data management-development, etc.
  • The emergence of big data. While DevOps focuses on the rapid and continuous delivery of working software, they do not work on data orchestration, updating, and secure access to it.
  • Growing usage of Git. Traditional DevOps CI/CD pipeline workflow is gradually replaced with Git operations. For example, the teams may commit their changes to Git and then directly deploy a program using Kubernetes.
  • Rise of machine learning. Traditional DevOps instruments may not cover the needs of ML applications and workflows. However, special frameworks like MLOps AWS or Microsoft address the full lifecycle of the machine learning models, including development, testing, deployment, operation, and monitoring.
  • Variety of IT infrastructure tools. This requires choosing the most effective IT instruments from the price/quality perspective. While DevOps is well-versed in the technical aspects, FinOps may better handle the money-planning responsibilities.

First DevOps siblings: DataOps and GitOps

DevOps has been around for a few years now, and many companies have seen success with the methodology. But over time, DevOps has started to evolve, with new “siblings” joining the family. So, today, we have DataOps and GitOps on different software projects.

DataOps is all about managing and processing data. The main responsibility of the DataOps engineer is to ensure that data is of high quality and is used effectively by the business. DataOps unites business leaders, IT developers, and data analysts to process and deliver data faster. Unlike a DevOps engineer who focuses on seamless delivery of software features to the user, DataOps takes care of non-stop data delivery through automation in data acquisition, curation, integration, and modeling.

Data ops processes and requirements

Some of the best DataOps practices are to: 

  • standardize and reuse core data pipeline components: ingest, transform, clean, etc.
  • create data science sandboxes on demand 
  • deploy models automatically 
  • create app logic and data permissions that promote easy but secure data access
  • assign agile teams to business groups to create more efficient programs

GitOps focuses on the continuous deployment of a cloud application using a version control system Git. The purpose of Git is to keep track of every change in software code and simplify team collaboration during code writing. 

According to a recent study, Git is the most widely used version control system with 73% of all repositories using it on an ongoing basis. So, if you compare DevOps vs. GitOps, the main difference is that the former takes care of software delivery regardless of what tools the team uses, and the latter relies on a particular tool Git to store code and commit changes.

GitOps processes

MLOps, AIOps, FinOps, NoOps, and other jobs aka Ops

The rise of new technologies and multiple financial flows led to the creation of new Ops roles. Today, companies are increasingly hiring MLOps, AIOps, FinOps, or NoOps specialists to establish effective processes in the chosen business area and cut costs related to poorly organized workflow.

Here’s a quick overview of the mentioned roles so that you can decide if you need to hire ML/AI Ops in your team.

MLOps

MLOps is responsible for building and maintaining ML models and pipelines. They help businesses develop Data Science and implement quality ML models faster, better, and with fewer costs.

MLOps closely cooperates with the development team and usually undertakes the following duties:

  • chooses technologies to develop and train ML models
  • adjusts the environment to run ML models
  • simplifies communication between data scientists and data engineers
  • manages model risk
  • configures the model monitoring system
MlOps examples

AIOps

AIOps is a relatively new concept introduced by Gartner in 2017. It implies managing IT operations with the help of AI tools in order to improve the workflow efficiency within the organization. 

Even though the terms AI and ML are often used as a pair, there is a big difference between MLOps and AIOps specialists. While the former takes care of creating ML models, the latter uses AI to enhance IT processes within companies. 

There are three benefits that enterprises expect from the adoption of AIOps practices:

  • full automation of routine tasks 
  • fast detection of security issues 
  • close interaction between data center groups and teams
AI Ops examples

FinOps

Hiring a FinOps engineer is intended to reduce the monetary burden on the organization, while still using the most effective and powerful cloud tools. FinOps establishes collaboration between engineering, finance, and business teams to highlight the financial side of cloud spending and make data-driven business decisions in time.

A FinOps specialist is a mix of technician and financier who uses the following processes to benefit the organization:

  • technology analysis
  • trends studying
  • cost forecasting
  • benchmarking
  • variance analysis
Finops responsibilities

NoOps

In recent times, the concept of “no operations”, i.e. NoOps has become increasingly common. It assumes that the IT environment can be completely automated with no need for an operating team to manage it. Notably, the NoOps automation can execute not only routine and repetitive functions but also perform higher-level operations undertaken by people today.

Given the above, the main responsibility of the NoOps engineer is to set, test, and launch a platform for automating IT tasks. Note that NoOps is not a ready-made product that you can buy or rent somewhere. Instead, it is a customizable approach that uses specific tools for adjusting automation based on the needs of a specific organization.

NoOps cases vs. DevOps cases

Which Ops suits your project best?

It’s important to understand that Ops team members may undertake different roles depending on the peculiarities of the company workflow. Even if you hire a DevOps engineer, their responsibilities will likely differ from the same position in a different company.  

In small companies, you may only need one or two specialists who will ensure the smooth operation of the program, delivering it from developers to consumers. You may also need a FinOps expert who will control costs and promptly respond to the appearance of more affordable but technologically advantageous options.

At the same time, large enterprises may find that the DevOps role no longer meets their growing expectations for productive IT infrastructure. For such companies, it makes sense to differentiate the DevOps vs. MLOps vs. AIOps roles, as well as consider the NoOps platform development for IT task automation.

If you have realized that you need help with hiring Ops engineers, don’t hesitate to get in touch. We’re here to guide you through the process of adding DevOps, DataOps, MLOps, GitOps, and other domain experts to your team.

July 8, 2022
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