Delivering AI/ML without proper Dataops is just wishful thinking!

DataOps Team Process

Given the iterative nature of AI/ML projects, having an agile process of building fast and reliable data pipelines (referred to as DataOps) has been the key differentiator in the ML projects that succeeded (unless there was a very exhaustive feature store available which is typically never the case).

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How to set-up Azure DevOps CI/ CD pipelines for Android

DevOps & CI/ CD are buzz words for a while now and they have really proven their value in today’s fast moving world and Agile development process. One understands the true value only when they have actually been a process of it and see for themselves the immense amount of time and head ache it saves.

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What‌ ‌is‌ ‌DataOps,‌ ‌and‌ ‌why‌ ‌it’s‌ ‌a‌ ‌top‌ ‌trend‌

Platform Ops for AI

DataOps‌ ‌emerged‌ ‌seven‌ ‌years‌ ‌ago‌ to refer to ‌best‌ ‌practices‌ for ‌getting‌ ‌proper‌ ‌analytics,‌ ‌and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just‌ as‌ ‌the‌ ‌DevOps‌ ‌trend‌ ‌led‌ ‌to‌ ‌a‌ ‌better‌ ‌process‌ ‌for‌ ‌collaboration‌ ‌between‌ ‌‌developers‌ ‌and‌ ‌operations‌ ‌teams,‌ ‌DataOps‌ ‌refers‌ ‌to closer collaboration between various teams handling data and operations teams deploying data into applications.

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What is MLOps? Machine Learning Operations explained

Until recently, all of us were learning about the standard software development lifecycle (SDLC). It goes from requirement elicitation to designing to development to testing to deployment, and all the way down to maintenance. Now, we are at a stage where almost every organisation is trying to incorporate Machine Learning (ML) – often called Artificial Intelligence – into their product.

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What is MLOps - Everything you must know to get Started

MLOps graphic

Until recently, all of us were learning about software development lifecycle(SDLC). Now, we are at a stage where almost every other organisation is trying to incorporate AI/ML into their product. This new requirement of building ML systems adds/reforms some principles of the SDLC to give rise to a new engineering discipline called MLOps.

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[Tutorial] Introduction to DevOps Automation Tools

Vagrant Ansible Docker

In this lab we are going to automate the process of creating the Infrastructure. We will also create a CI/CD Pipeline which will deploy a web application in a container-based environment. We’re going to accomplish this using some interesting automation tools. Let’s get started!

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Top 6 CI/ CD practices for End-to-End development pipelines

Continuous deployment

In this article, we’ll talk about some often-misunderstood development principles that will guide you to developing more resilient, production-ready development pipelines using CI/CD tools. Then, we’ll make it concrete with a tutorial about how to set up your own pipeline using Buddy.

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Agile Manifesto: 20 years on and Agile remains elusive

Agile-dancer

Last month marked the 20th anniversary of the Agile Manifesto. The manifesto emerged from meeting representatives from extreme programming, SCRUM, DSDM, adaptive software development, Crystal, feature-driven development, pragmatic programming and others sympathetic to the need for an alternative to documentation driven, heavyweight software development processes.

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Measuring the value of DevOps-as-a-Service (DaaS)

DevOps collage

More and more enterprises are embracing the idea of providing DevOps capabilities, such as pipelines, tools, hardened containers and tech stacks, as a centralized service to support their many application teams. The question then becomes, “What metrics should be used to show the value of DevOps-as-a-Service (DaaS)?”

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Comparing DevSecOps and Systems Engineering principles

Systems DevOps principles

Software developers and sustainers are seeing significant improvement by adopting Lean, Agile and DevSecOps iteration-based approaches. Now similar approaches are being proposed for more complex projects, including embedded software systems and software-driven systems of systems. The interaction of these two disciplines is not well understood, and experience from early application suggests model clashes between them. In this blog post, I look at the underlying principles espoused by each of these disciplines.

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The importance of incorporating DesignOps into DevOps

Business structure

Design is a critical software project element that often goes unnoticed by those writing code. After all, coders and implementers tend to take the design process for granted and leave the look and feel of applications to the designers. In the days of waterfall based development, design was one of the first steps in the development sequence of creating a new application. However, with Agile and DevOps development practices, design became disconnected from the overall process.

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5 steps to successful DevOps culture

Colored chairs

The late business management guru Peter Drucker coined the phrase, “Culture eats strategy for breakfast,” and the phrase has endured through the years. Your people must be passionate and enthusiastic about executing business strategy, or it – and your business – is doomed to fail. But remember that company culture itself needs a strategy to complement the business.
DevOps, too, will not succeed without the right culture…

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DevOps myths debunked

Truth maze

Leaders need not be DevOps experts, but they need to distinguish DevOps myths and realities to lead their digital transformation projects. As I indicated in a recent article, Engineering Practices Can Overcome DevOps Challenges, leaders need to set an inspiring DevOps directional vision for the organization, and proactively stimulate and sponsor team activities toward goals. […]

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DevOps infused with Artificial Intelligence avails more opportunities

Woman on laptop

With data being added every minute, it is impossible to manage all of them without the help of technology. Especially, monitoring a DevOps environment involves a high degree of complexity. The exploding amount of data is giving a hard time to DevOps teams to effectively absorb and apply information to address and resolve customer issues. However, when artificial intelligence (AI) and DevOps work together, they can boost their productivity by saving a lot of time.

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A framework for DevSecOps evolution and achieving Continuous-Integration/ Continuous-Delivery (CI/CD) capabilities

DevSecOps workflow

The benefits of operating a development environment with continuous-integration and continuous-delivery (CI/CD) pipeline capabilities and DevSecOps practices are well documented. Leveraging DevSecOps practices and CI/CD pipelines enables organizations to respond to security and reliability events quickly and efficiently and to produce resilient and secure software on a predictable schedule and budget.

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