MLOps, short for Machine Learning Operations, is an emerging practice that brings together the disciplines of machine learning and DevOps to streamline the entire lifecycle of machine learning models, from development to deployment and beyond. One of the key aspects of MLOps is the use of automation to improve the efficiency, reliability, and quality of machine learning pipelines. In this tutorial, we will explore how to use Continuous Integration (CI), Continuous Delivery (CD), and Continuous Testing (CT) to automate the deployment of machine learning models. Before we dive into the details of MLOps automation, let's briefly explain the three key concepts that underpin it: MLOps automation typically involves a series of steps that automate the entire machine learning pipeline, from data preparation to model deployment. To automate this process, we can use a combination of CI/CD/CT tools and techniques.

Companies struggle to continuously develop and deploy AI models to complex production systems due to AI characteristics while assuring quality. To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required. This paper includes a Multivocal Literature Review where we consolidated 151 relevant formal and informal sources. In addition, nine-semi structured interviews with participants from academia and industry verified and extended the obtained information. Based on these sources, this paper provides and compares terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle management, and CD4ML. Furthermore, the paper provides an aggregated list of potential triggers for reiterating the pipeline, such as alert systems or schedules. In addition, this work uses a taxonomy creation strategy to present a consolidated pipeline comprising tasks regarding the continuous development of AI. This pipeline consists of four stages: Data Handling, Model Learning, Software Development and System Operations. Moreover, we map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.

Continuous integration (CI) is the process of all software developers merging their code changes in a central repository many times throughout the day. A fully automated software release process is called continuous delivery, abbreviated as CD. Although the two terms are not interchangeable, CI/CD is a DevOps methodology and fits in that category. A continuous integration/continuous delivery (CI/CD) pipeline is a system that automates the software delivery process. CI/CD pipelines generate code, run tests, and deliver new product versions when software is changed.

In the age of cloud, cloud-native, and continuous delivery, IT automation is an approach to managing infrastructure to the benefit of developers, allowing them to continue enhancing the customer experience. Recently, as a marketer in the HPE Pointnext Services team, I was asked to work with HPE's Global Sales Engineering team to bring the HPE Pointnext Services point of view on IT automation adoption. You can imagine the technicality around automation adoption, so simplifying it for those who are interested in the topic but are not technicians was an enjoyable task. The start-point, though, must be: What is automation, and what does it do for you? Ultimately automation adoption benefits from a check-list of things taken into account.

Training ML Models and exporting it in more optimized way for Edge device from scratch is quite challenging thing to do especially for a beginner in ML space. Interestingly Azure Cognitive Services will aid in heavy lifting half of the common problems such as Image Classification, Speech Recognition etc. So in this article, I will show you how I created a simple pipeline(kind of MLOps) that deploys the model to an Edge Device leveraging Azure IoT Modules and Azure DevOps Services. Blob Storage – For storing images for ML training 2. Logic Apps – To respond Blob storage upload events and trigger a Post REST API call to Azure Pipelines 3. Cognitive Services – For training Images and generate a optimized model specifically for edge devices. Containerized Az Devops Agents will be running inside this, orchestrated using K3s Kubernetes Distribution.

AI-driven organizations are using data and machine learning to solve their hardest problems and are reaping the rewards. "Companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with 120% cash flow growth,"1 according to McKinsey Global Institute. Machine learning (ML) systems have a special capacity for creating technical debt if not managed well. They have all of the maintenance problems of traditional code plus an additional set of ML-specific issues: ML systems have unique hardware and software dependencies, require testing and validation of data as well as code, and as the world changes around us deployed ML models degrade over time. Moreover, ML systems underperform without throwing errors, making identifying and resolving issues especially challenging.

The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software. By now, most organizations understand DevOps is a substantial discipline that they must adopt – according to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.

This is usually as true for the delivery of software as it is for anything else, but mounting pressure to digitally transform and continuously deliver updates has made speed a default requirement for most organisations. This leaves a choice between quality and cost, which often comes down to a decision about testing. Testing--especially unit testing--has been an underappreciated stage in the software delivery lifecycle (SDLC) for decades. It's historically been slow, resource-intensive, and less interesting than the development of new features, which may be why the primary motivation to write unit tests for many developers is external pressures, e.g. Within organisations that enforce code coverage targets, mandated manual testing can feel a lot like being told to eat your vegetables because they're good for you.

Data science and ML are becoming core capabilities for solving complex real-world problems, transforming industries, and delivering value in all domains. Therefore, many businesses are investing in their data science teams and ML capabilities to develop predictive models that can deliver business value to their users. This document is for data scientists and ML engineers who want to apply DevOps principles to ML systems (MLOps). MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management. Data scientists can implement and train an ML model with predictive performance on an offline holdout dataset, given relevant training data for their use case. However, the real challenge isn't building an ML model, the challenge is building an integrated ML system and to continuously operate it in production.

DevOps is a software development and delivery process that helps in emphasizing communication along with cross-functional collaboration between product management, software development, and operations professionals. We've curated a list of the Top 12 DevOps Tools, along with their features, based on our decade-long experience in the IT industry, dealing with infrastructure for the significant part. We've taken great care in selecting, benchmarking, and continuously improving our tool selection. That apart, the article also covers the DevOps transformational roadmap as well as the step by step implementation guide. The popularity of DevOps, in recent years, as robust software development and delivery process has been unprecedented.