AI News

AUGUST 29, 2023

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. Operationalisation needs good orchestration to make it work, as Basil Faruqui, director of solutions marketing at BMC , explains. “If

Data Ingestion 226

Data Ingestion Big Data Explainability ETL 226

AWS Machine Learning Blog

FEBRUARY 28, 2025

Automat-it specializes in helping startups and scaleups grow through hands-on cloud DevOps, MLOps and FinOps services. In this post, we explain how Automat-it helped this customer achieve a more than twelvefold cost savings while keeping AI model performance within the required performance thresholds.

DevOps 95

DevOps AI AI Deep Learning 95

Flipboard

MARCH 21, 2025

The following architecture diagram explains the overall flow of this solution. By providing developers expert guidance grounded in AWS best practices, this AI assistant enables DevOps teams to review and optimize cloud architecture across of AWS accounts. About the Authors Upendra V is a Sr.

Automation 164

Automation Large Language Models Generative AI DevOps 164

IBM Journey to AI blog

FEBRUARY 28, 2024

Perhaps the easiest way to explain it is by looking at the opposite scenario: what if you don’t have a managed DNS service in place? Infrastructure as code : Today’s networks are driven by DevOps, edge computingand serverless architectures, which require an API-first approach to infrastructure.

DevOps 288

DevOps Explainability Automation 288

IBM Journey to AI blog

SEPTEMBER 12, 2023

In today’s complex and dynamic environments, traditional manual approaches fall short in delivering the agility, accuracy and scalability demanded by site reliability engineering (SRE) and DevOps practices. “We’re accomplishing the goal that we set out to do,” Fite explains.

Automation 206

Automation DevOps Machine Learning Explainability 206

IBM Journey to AI blog

FEBRUARY 29, 2024

The system uses Docker images, which are read-only templates that are used for building containers, and Dockerfiles, which are text files that accompany and explain Docker images. Docker images and other container images require a space in which to run.

DevOps 267

DevOps Software Development Artificial Intelligence Artificial Intelligence 267

IBM Journey to AI blog

NOVEMBER 29, 2023

To deploy applications onto these varying environments, we have developed a set of robust DevSecOps toolchains to build applications, deploy them to a Satellite location in a secure and consistent manner and monitor the environment using the best DevOps practices. DevSecOps workflows focus on a frequent and reliable software delivery process.

DevOps 280

DevOps Automation Explainability 280

AWS Machine Learning Blog

NOVEMBER 7, 2024

Technical Info: Provide part specifications, features, and explain component functions. He has over 6 years of experience in helping customers architecting a DevOps strategy for their cloud workloads. Your main tasks are: Part Identification: Find specific parts based on vehicle details (make, model, year).

DevOps 127

DevOps Generative AI Python Automation 127

Unite.AI

SEPTEMBER 30, 2024

This article explains how AI in quality assurance streamlines software testing while improving product performance. AI-powered QA is also becoming central to DevOps. Unsurprisingly, Gartner reports that 88% of service leaders feel that today’s QA approaches don’t meet the mark. What is AI-powered Quality Assurance?

Automation 312

Automation AI AI DevOps 312

IBM Journey to AI blog

JUNE 26, 2023

Rob High explained the increasing importance of running applications in non-traditional places and on non-traditional devices, which we also often call “on the edge.” In short, hybrid cloud impacts every aspect of where and how we run IT solutions. The only guest I had who is not an IBM Fellow is Naeem Altaf.

DevOps 180

DevOps Explainability Automation AI 180

IBM Journey to AI blog

AUGUST 11, 2023

The paper suggested creating a systematic “MLOps” process that incorporated CI/CD methodology commonly used in DevOps to essentially create an assembly line for each step. MLOps vs. DevOps DevOps is the process of delivering software by combining and automating the work of software development and IT operations teams.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Unite.AI

JANUARY 15, 2024

In this CodePal review, I'll explain what CodePal is and who it's best for. From there, I'll briefly explain each of its tools so you know what it's capable of. However, I hope that by briefly explaining what each tool does, you'll better grasp what CodePal is capable of while finding the tool you're looking for more quickly.

Explainability 144

Explainability DevOps AI AI 144

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. Model monitoring tools will merge with the DevOps monitoring stack. Not a fork.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

MARCH 21, 2025

Renu has a strong passion for learning with her area of specialization in DevOps. /samples/2003.10304/page_5.png" However, the lower and fluctuating validation Dice coefficient indicates potential overfitting and room for improvement in the models generalization performance.

Metadata 115

Metadata Convolutional Neural Networks Generative AI Data Scientist 115

Towards AI

JULY 20, 2023

Why I should have opted for Visual Studio Code for DevOps Visual Studio 2019 is one of the best tools on the market for building applications. From my own experience, this is overkill for a variety of reasons that I will explain in detail. I developed my very first web… Read the full blog for free on Medium.

DevOps 98

DevOps Explainability AI AI 98

AWS Machine Learning Blog

DECEMBER 3, 2024

The analysis results in a summarized alert intelligence report that identifies and explains root causes of checkout service issues. It examines service performance metrics, forecasts of key indicators like error rates, error patterns and anomalies, security alerts, and overall system status and health.

DevOps 103

DevOps AI AI ML 103

Unite.AI

SEPTEMBER 25, 2024

The funding round was led by Flint Capital and Glilot Capital Partners , with notable industry figures such as Yochay Ettun, CEO of cnvrg.io (acquired by Intel), and Raz Shaked, Head of DevOps at Wiz, among the investors. Traditional Infrastructure as Code (IaC) tools, like Terraform, often struggle to scale efficiently in such setups.

DevOps 130

DevOps Automation Explainability Generative AI 130

O'Reilly Media

FEBRUARY 25, 2025

So instead I spent all those years working on a versatile code visualizer that could be *used* by human tutors to explain code execution. In particular, theyre great at generating and explaining small pieces of self-contained code (e.g., Add code comments to explain your changes. and Explain what this code does line-by-line.

Generative AI 82

Generative AI Python LLM AI 82

AI News

JULY 11, 2024

Software development emerges as the most popular area for AI investment (59%), followed by quality assurance (44%) and DevOps and automation (44%). This explains why many are investing despite the uncertainty about ROI. They see the potential for long-term cost savings but need a well-curated plan to implement the changes.

Big Data 142

Big Data Software Development DevOps AI 142

IBM Journey to AI blog

SEPTEMBER 20, 2023

IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows. IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

FEBRUARY 27, 2024

Model explainability Model explainability is a pivotal part of ML deployments, because it ensures transparency in predictions. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team. For a detailed understanding, we use Amazon SageMaker Clarify.

Machine Learning 134

Machine Learning DevOps Data Scientist Data Quality 134

IBM Journey to AI blog

OCTOBER 25, 2023

It can also aid in platform engineering, for example by generating DevOps pipelines and middleware automation scripts. The tools in watsonx.governance will also help organizations efficiently drive responsible, transparent and explainable workflows across the business.

Generative AI 243

Generative AI Automation ESG AI 243

AWS Machine Learning Blog

JANUARY 5, 2024

Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice.

Data Science 132

Data Science Data Drift DevOps Auto-complete 132

Unite.AI

NOVEMBER 9, 2023

Our platform today leverages the power of AI to enhance detection of risks, simplify investigations, and speed up remediation – saving cloud security, DevOps, and development teams time and effort, while significantly improving security outcomes. Can you explain how Orca leverages AI and what benefits it brings?

DevOps 157

DevOps Explainability Responsible AI Automation 157

Unite.AI

DECEMBER 7, 2023

It offers coding guidance, explaining the why behind a flagged issue, and how to fix it, to ensure that the code being written is MISRA-compliant. Consistent code quality is something every manager or technical director aims to maintain. What is your vision for how AI will transform coding in the future?

Software Development 243

Software Development DevOps Explainability AI 243

Becoming Human

MAY 11, 2023

DevOps and DataOps: DevOps and DataOps are related approaches that emphasize collaboration between software developers and IT operations teams. DevOps focuses on automating the software development and deployment process, while DataOps focuses on the data management process.

Machine Learning 78

Machine Learning DevOps Software Development Data Scientist 78

Heartbeat

FEBRUARY 1, 2023

Comparing MLOps and DevOps DevOps is a software development method that brings together multiple teams to organize and conspire to create more efficient and reliable products. One thing that DevOps and MLOps have in common is that they both emphasize process automation. Learn more lessons from the field with Comet experts.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

MARCH 20, 2025

Then Amazon Bedrock agent will ingest the troubleshooting, explain the root cause of the issue to the user, and suggest remediation steps based on the AWSSupport-TroubleshootEKSWorkerNode output, such as updating the worker nodes IAM role or resolving network configuration issues, enabling them to take the necessary actions to resolve the problem.

Automation 87

Automation DevOps Metadata Generative AI 87

AWS Machine Learning Blog

DECEMBER 19, 2023

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. Conclusion In summary, MLOps is critical for any organization that aims to deploy ML models in production systems at scale.

Machine Learning 128

Machine Learning ML Engineer DevOps ML 128

AWS Machine Learning Blog

JUNE 27, 2023

That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market.

DevOps 116

DevOps ML Machine Learning ML Engineer 116

AWS Machine Learning Blog

NOVEMBER 19, 2024

These sessions, featuring Amazon Q Business , Amazon Q Developer , Amazon Q in QuickSight , and Amazon Q Connect , span the AI/ML, DevOps and Developer Productivity, Analytics, and Business Applications topics. Hear from Availity on how 1.5 Explore examples to estimate the severity and likelihood of potential events that could be harmful.

ML 93

ML Generative AI AI AI 93

Unite.AI

MARCH 21, 2024

After that, I worked for startups for a few years and then spent a decade at Palo Alto Networks, eventually becoming a VP responsible for development, QA, DevOps, and data science. Can you explain the concept of ‘data democracy' in the context of today's AI-driven business environment?

DevOps 144

DevOps Data Quality Machine Learning Generative AI 144

AWS Machine Learning Blog

MARCH 3, 2025

Prior to AWS, he worked as a DevOps architect in the e-commerce industry for over 5 years, following a decade of R&D work in mobile internet technologies. Before migrating any of the provided architecture to production, we recommend following the AWS Well-Architected Framework. He serves as a technical advisor to startups building on AWS.

Conversational AI 78

Conversational AI Deep Learning AI AI 78

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 98

Machine Learning Software Development Data Drift Data Science 98

AWS Machine Learning Blog

MARCH 7, 2024

We also explained each element of the solution in detail. He is a technology enthusiast and a builder with a core area of interest on generative AI, serverless, and DevOps. Outside of work, he enjoys watching shows, traveling, and music.

Automation 137

Automation Generative AI DevOps Explainability 137

AWS Machine Learning Blog

OCTOBER 10, 2023

You can use natural language with a conversational chat interface to perform tasks such as creating narratives, reports, and blog posts; answering questions; summarizing notes and articles; and explaining concepts, without writing a single line of code. Gavin Satur is a Principal Solutions Architect at Amazon Web Services.

Generative AI 126

Generative AI IDP DevOps Machine Learning 126

AWS Machine Learning Blog

OCTOBER 9, 2023

Next, we explain how to review the trained model for performance. Finally, we explain how to use the trained model to perform predictions. His expertise spans application architecture, DevOps, serverless, and machine learning. Aaqib Bickiya is a Solutions Architect at Amazon Web Services based in Southern California.

Machine Learning 101

Machine Learning Categorization ML DevOps 101