Pickl AI

APRIL 17, 2023

How can a DevOps team take advantage of Artificial Intelligence (AI)? DevOps is mainly the practice of combining different teams including development and operations teams to make improvements in the software delivery processes. So now, how can a DevOps team take advantage of Artificial Intelligence (AI)?

DevOps 52

DevOps Artificial Intelligence Artificial Intelligence Automation 52

Marktechpost

MAY 20, 2024

The platform aims to support various application forms, including process automation and search functionalities, to meet the evolving needs of enterprise scenarios. The post Bisheng: An Open-Source LLM DevOps Platform Revolutionizing LLM Application Development appeared first on MarkTechPost.

DevOps 57

DevOps LLM Large Language Models Data Quality 57

IBM Journey to AI blog

MAY 17, 2024

It identifies the technologies and internal knowledge that an organization has, how suited its culture is to embrace managed services, the experience of its DevOps team, the initiatives it can begin to migrate to cloud and more. The model’s five stages revolve around the organization’s level of security automation.

DevOps 207

DevOps Automation AI AI 207

IBM Journey to AI blog

JANUARY 10, 2024

This helps with continuous business support through applications automating essential workflows. Traditionally, applications and their hosting infrastructure align with DevOps and CloudOps. Typically, DevOps initiates requests, scrutinized by CloudOps, NetOps, SecOps and FinOps teams.

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DevOps Automation 178

IBM Journey to AI blog

OCTOBER 16, 2023

They have become more important as organizations embrace modern development techniques such as microservices, serverless and DevOps, all of which utilize regular code deployments in small increments. Containerization helps DevOps teams avoid the complications that arise when moving software from testing to production.

DevOps 198

DevOps Automation 198

Unite.AI

NOVEMBER 28, 2023

Despite being in its infancy, artificial intelligence (AI) is already irrevocably impacting the IT industry and the way people across teams. On the one hand, tech pros feel a need to act with urgency to implement AI-powered solutions to enjoy the benefits of increased productivity and efficiency.

DevOps 277

DevOps AI AI Natural Language Processing 277

IBM Journey to AI blog

JANUARY 11, 2024

And there’s no reason why mainframe applications wouldn’t benefit from agile development and smaller, incremental releases within a DevOps-style automated pipeline. When AIs are trained with content found on the internet, they may often provide convincing and believable dialogss, but not fully accurate responses.

Generative AI 271

Generative AI AI AI DevOps 271

AWS Machine Learning Blog

MARCH 7, 2024

However, Amazon Bedrock and AWS Step Functions make it straightforward to automate this process at scale. Amazon Bedrock offers the generative AI foundation model Amazon Titan Image Generator G1 , which can automatically change the background of an image using a technique called outpainting.

Automation 118

Automation Generative AI DevOps Explainability 118

IBM Journey to AI blog

MARCH 15, 2024

These improvements are further underscored by the solution’s automated discovery of dead code or unreachable code and its ability to identify microservices in potential target states. Cloud services configuration: Automate the build-out and configuration of the cloud platform and the required cloud services for application workloads.

Automation 249

Automation DevOps 249

IBM Journey to AI blog

JUNE 26, 2023

By infusing artificial intelligence (AI) into IT operations , you can leverage the considerable power of natural language processing and machine learning models to automate and streamline operational workflows. To address this waste, consider implementing FinOps (Finance + DevOps).

DevOps 140

DevOps Automation Natural Language Processing Artificial Intelligence 140

IBM Journey to AI blog

FEBRUARY 14, 2024

When thinking of artificial intelligence (AI) use cases, the question might be asked: What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. We’re all amazed by what AI can do.

Computer Vision 221

Computer Vision Automation Robotics AI 221

IBM Journey to AI blog

DECEMBER 19, 2023

As practices like DevOps , cloud native , serverless and site reliability engineering (SRE) mature, the focus is shifting toward significant levels of automation, speed, agility and business alignment with IT (which helps enterprise IT transform into engineering organizations). Patterns (on paper) only as prescriptive guidance.

DevOps 179

DevOps Automation Software Development 179

Becoming Human

APRIL 19, 2024

The use of Generative AI is one of the most exciting technological developments that is changing the pattern for software development. We will examine all aspects of generative AI in this section, as well as how its implementation is changing the method for creating software.

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Software Development Generative AI DevOps Automation 103

IBM Journey to AI blog

OCTOBER 25, 2023

Implementing generative AI can seem like a chicken-and-egg conundrum. In a recent IBM Institute for Business Value survey, 64% of CEOs said they needed to modernize apps before they could use generative AI. Organizations that have mastered hybrid cloud are well positioned to implement generative AI across the organization.

Generative AI 224

Generative AI Automation AI AI 224

Unite.AI

NOVEMBER 17, 2023

Integrating AI into the app development lifecycle can significantly enhance security measures. From the design and planning stages, AI can help anticipate potential security flaws. During the coding and testing phases, AI algorithms can detect vulnerabilities that human developers might miss.

Artificial Intelligence 299

Artificial Intelligence Artificial Intelligence Automation DevOps 299

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. What is MLOps?

Data Science 144

Data Science DevOps Machine Learning Data Scientist 144

IBM Journey to AI blog

FEBRUARY 13, 2024

Optimize performance through automation Turbonomic revolutionizes application performance optimization by leveraging AI and machine learning algorithms to analyze real-time performance data and give insight into application response time and transaction time.

Automation 180

Automation DevOps Machine Learning Algorithm 180

IBM Journey to AI blog

DECEMBER 19, 2023

Stage 1: Development automation Infrastructure automation (IaC) and pipeline automation are self-contained within the development team, which makes automation a great place to start. Automate firewall via generation of resource files from IaC execution & importing them to firewall systems as described here.

Automation 205

Automation DevOps Software Development 205

IBM Journey to AI blog

NOVEMBER 24, 2023

Application modernization is the process of updating legacy applications leveraging modern technologies, enhancing performance and making it adaptable to evolving business speeds by infusing cloud native principles like DevOps, Infrastructure-as-code (IAC) and so on. Let us explore the Generative AI possibilities across these lifecycle areas.

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Generative AI Auto-complete DevOps Automation 248

IBM Journey to AI blog

SEPTEMBER 19, 2023

DevOps , SRE , Platform, I TOps, and developer teams are all under pressure to keep applications performant while operating faster and smarter than ever. Automation for efficient operations Instana was built with automation in mind, understanding that manual intervention leads to delays, inefficiencies and potential errors.

Automation 219

Automation DevOps Artificial Intelligence Artificial Intelligence 219

Becoming Human

MAY 11, 2023

Machine learning operations, or MLOps, are the set of practices and tools that aim to streamline and automate the machine learning lifecycle. MLOps projects are becoming increasingly popular as companies seek to leverage the power of AI to gain a competitive edge. Both can be useful in implementing MLOps projects.

Machine Learning 78

Machine Learning DevOps Software Development Automation 78

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. 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.

Data Science 110

Data Science Data Drift DevOps Auto-complete 110

IBM Journey to AI blog

JANUARY 15, 2024

Digital transformation trends that drive a competitive advantage Trend: Artificial intelligence and machine learning We’re entering year two of widespread adoption of generative AI tools. Trend: Automation Like AI and ML, automation will be a huge driver of human productivity.

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Automation Machine Learning DevOps Artificial Intelligence 193

IBM Journey to AI blog

NOVEMBER 14, 2023

The platform can be automated through a standardized framework validated for Financial Services, leveraging the IBM Cloud Security and Compliance Center service (SCC). It combines the power of the z-Mod stack with secure DevOps practices, creating a seamless and efficient development process.

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DevOps Automation Data Integration Generative AI 255

IBM Journey to AI blog

JUNE 2, 2023

Automate routine tasks to free up time to provide personalized services and build relationships with families. IBM Operational Decision Manager (ODM) enables businesses to respond to real-time data by applying automated decisions, enabling business users to develop and maintain operational systems decision logic.

Auto-complete 220

Auto-complete DevOps Automation Knowledge Model 220

IBM Journey to AI blog

OCTOBER 24, 2023

Organizations need simplified, integrated and automated solutions to help optimize IT spend, improve operations and drive greater financial returns. The result of the implemented solutions was increased automation and improved budgeting. They began in the initial stages of the FinOps journey and maturity.

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DevOps Automation 209

Mlearning.ai

JULY 31, 2023

Extension Of Devops MLOps is an extension of DevOps. DevOps aims to streamline the development and operation of software applications, while MLOps focuses on the machine learning lifecycle. MLOps extends DevOps by including data science practices, like model training and data preprocessing.

DevOps 52

DevOps Machine Learning Data Scientist Automation 52

IBM Journey to AI blog

MAY 31, 2023

This allows for greater automation and optimization of production processes, leading to increased efficiency, productivity and flexibility in manufacturing. We assume readers are familiar with Industry 4.0, For more information about the concept, see the link below. Learn more about Industry 4.0

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DevOps Robotics Automation 244

IBM Journey to AI blog

MAY 15, 2024

Enhanced capabilities to drive growth FinOps is closely intertwined with DevOps and can represent a radical transformation for many organizations. To maximize benefits and realize the potential of FinOps, organizations must forge a clear path and avoid common mistakes.

DevOps 144

DevOps Automation 144

ODSC - Open Data Science

DECEMBER 1, 2023

Over the course of 3+ hours, you’ll learn How to take your machine learning model from experimentation to production How to automate your machine learning workflows by using GitHub Actions. You’ll explore the use of generative artificial intelligence (AI) models for natural language processing (NLP) in Azure Machine Learning.

Data Science 52

Data Science Large Language Models DevOps Machine Learning 52

IBM Journey to AI blog

JULY 11, 2023

Announcing IBM Instana Observability Business Monitoring That’s why we are so excited to announce IBM Instana Observability Business Monitoring , an automated solution designed to provide organizations with an end-to-end view of their business processes and applications.

DevOps 160

DevOps Automation 160

IBM Journey to AI blog

FEBRUARY 2, 2024

Is it automated? Today’s networks are driven by DevOps, edge computing, and serverless architectures, all of which require an API-first approach to infrastructure. NS1 takes a different approach, offering a separated redundant system that you can manage from the same control plane. The mechanics of failover matter.

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IBM Journey to AI blog

FEBRUARY 15, 2024

In DevOps , the concept of observability has evolved to refer to the end-to-end visibility of a system state as dictated by telemetry data. It’s the ability to diagnose and understand why a system is behaving in a particular way, which is vital to troubleshooting, deciphering performance issues and improving system design.

Automation 184

Automation DevOps 184

IBM Journey to AI blog

DECEMBER 13, 2023

Read the blog: How generative AI is transforming customer service Customer service types that organizations should prioritize By offering different types of customer service and several customer support channels, organizations demonstrate they are investing in customer care.

Chatbots 203

Chatbots Generative AI DevOps Conversational AI 203

AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. Machine learning operations (MLOps) applies DevOps principles to ML systems. It’s much more than just automation.

Machine Learning 104

Machine Learning ML Engineer DevOps ML 104

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. And everybody agrees that in production, this should be automated.” So that’s on the vendor side.

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Data Ingestion Explainability Big Data ETL 161

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

Machine Learning 112

Machine Learning DevOps Data Quality Data Scientist 112