IBM Journey to AI blog

NOVEMBER 13, 2023

Developed internally at Google and released to the public in 2014, Kubernetes has enabled organizations to move away from traditional IT infrastructure and toward the automation of operational tasks tied to the deployment, scaling and managing of containerized applications (or microservices ).

DevOps 336

DevOps Software Development Automation Large Language Models 336

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%). Investment in AI capabilities is substantial, with 93% of companies spending at least £100,000 in 2024, and 44% allocating £500,000 or more.

DevOps 296

DevOps Software Development Big Data AI 296

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)?

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DevOps Artificial Intelligence Artificial Intelligence Automation 52

IBM Journey to AI blog

MARCH 22, 2024

Quality Assurance (QA) is a critical component of the software development lifecycle, aiming to ensure that software products meet specified quality standards before release. QA encompasses a systematic and strategic approach to identifying, preventing and resolving issues throughout the development process.

Automation 263

Automation DevOps IDP Software Development 263

Unite.AI

NOVEMBER 21, 2024

Archana Joshi brings over 24 years of experience in the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. They support us by providing valuable insights, automating tasks and keeping us aligned with our strategic goals.

DevOps 147

DevOps Automation Responsible AI Software Development 147

O'Reilly Media

OCTOBER 19, 2021

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments.

DevOps 140

DevOps Software Engineer Data Scientist ML 140

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 222

DevOps Automation Software Development 222

Unite.AI

SEPTEMBER 30, 2024

AI quality assurance (QA) uses artificial intelligence to streamline and automate different parts of the software testing process. AI also automates test data generation, creating a wide range of test data that reduces the need for manual input. Automated QA surpasses manual testing by offering up to 90% accuracy.

Automation 312

Automation AI AI DevOps 312

IBM Journey to AI blog

MARCH 29, 2024

Monitoring and optimizing application performance is important for software developers and enterprises at large. OpenTelemetry and Prometheus enable the collection and transformation of metrics, which allows DevOps and IT teams to generate and act on performance insights. What is OpenTelemetry?

DevOps 263

DevOps Software Development Automation Metadata 263

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%). Investment in AI capabilities is substantial, with 93% of companies spending at least £100,000 in 2024, and 44% allocating £500,000 or more.

DevOps 132

DevOps Software Development Big Data AI 132

Unite.AI

JUNE 19, 2024

MLOps, or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. ML Operations : Deploy and maintain ML models using established DevOps practices.

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Software Engineer DevOps Machine Learning ML 182

IBM Journey to AI blog

FEBRUARY 29, 2024

This is managed by the container runtime—a software solution interacting with the OS to make the necessary room to run container images. Further, Kubernetes is an open-source system and encourages the avid participation of contributors (who oversee the project now), with each software provider putting its own spin on Kubernetes.

DevOps 295

DevOps Software Development Artificial Intelligence Artificial Intelligence 295

The MLOps Blog

JANUARY 23, 2023

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Lived through the DevOps revolution. Came to ML from software. Founded two successful software services companies. If you’d like a TLDR, here it is: MLOps is an extension of DevOps.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

IBM Journey to AI blog

AUGUST 11, 2023

MLOps, which stands for machine learning operations, uses automation, continuous integration and continuous delivery/deployment (CI/CD) , and machine learning models to streamline the deployment, monitoring and maintenance of the overall machine learning system. How to use ML to automate the refining process into a cyclical ML process.

Data Science 188

Data Science DevOps Machine Learning Data Scientist 188

IBM Journey to AI blog

FEBRUARY 6, 2024

This unified view gives administrators and development teams centralized control over their infrastructure and apps, making it possible to optimize cost, security, availability and resource utilization. Automation Automation tools are a significant feature of cloud-based infrastructure. What is a public cloud?

DevOps 306

DevOps Automation Software Development Big Data 306

Unite.AI

JUNE 7, 2023

Can you discuss why enterprises should integrate application security early into the software development life cycle? DevSecOps, also known as “secure devops”, is the mindset that security is integrated throughout the entire SDLC, from requirements to architecture and design, coding, testing, release and deployment.

Software Development 260

Software Development Automation DevOps Machine Learning 260

Marktechpost

AUGUST 30, 2024

On top of dealing with complicated documentation and software development kits (SDKs), existing cloud-native database services necessitate even more DevOps to orchestrate individual database instances: schema migrations, connection pooling, versioning across cases, etc.

DevOps 64

DevOps Software Development Automation Artificial Intelligence 64

Unite.AI

NOVEMBER 17, 2023

During the coding and testing phases, AI algorithms can detect vulnerabilities that human developers might miss. Below, I am listing several ways in which AI can assist developers in creating secure apps. Automated Code Review and Analysis AI can review and analyze code for potential vulnerabilities.

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Artificial Intelligence Artificial Intelligence DevOps Automation 299

IBM Journey to AI blog

DECEMBER 19, 2023

This post expands on the topic and provides a maturity model and building blocks that help enterprises accelerate their software supply chain lifecycle in the ever-evolving landscape of enterprise cloud-native software development. We recommend a 4-stage roadmap for implementation, as shown in the figure below.

Automation 235

Automation DevOps Software Development 235

AWS Machine Learning Blog

MAY 1, 2024

This post demonstrates how to build a chatbot using Amazon Bedrock including Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock , within an automated solution. Solution overview In this post, we use publicly available data, encompassing both unstructured and structured formats, to showcase our entirely automated chatbot system.

Chatbots 127

Chatbots Automation Machine Learning DevOps 127

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 projects that focus on implementing machine learning operations best practices into a company’s existing software development and deployment process.

Machine Learning 78

Machine Learning DevOps Software Development Automation 78

IBM Journey to AI blog

JANUARY 26, 2024

At Instana, addressing our customers’ needs and creating a simple tool that is easy to use is fundamental to helping our DevOps and SRE teams reduce burnout rates, allowing them to excel in what they do best. We announced our action framework, where you can create new actions or reuse your existing automation (e.g.,

Automation 296

Automation DevOps Software Development Artificial Intelligence 296

ODSC - Open Data Science

OCTOBER 9, 2024

Let’s go and explore together how AI can revolutionize key areas of software development, from coding to testing, deployment, and security. These tools use machine learning models trained on vast amounts of code to assist developers in writing cleaner, more efficient code. How you might ask? The result?

Software Engineer 40

Software Engineer Software Development DevOps ETL 40

Flipboard

DECEMBER 26, 2023

Software development is currently undergoing a profound transformation, marked by a quiet yet remarkable surge in advanced automation. This impending …

Software Engineer 172

Software Engineer Software Development Automation DevOps 172

Pickl AI

JANUARY 30, 2023

Data Automation: Automate data processing pipelines and workflows using Python scripting and libraries such as PyAutoGUI and Task Scheduler. A Software Developer Uses Python: Backend Development : Python finds applications in developing server-side applications and APIs. How to Make a Career in Python?

Python 97

Python DevOps Machine Learning Data Science 97

Heartbeat

FEBRUARY 1, 2023

MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. We also have software engineers who are in charge of creating and maintaining the facilities and tools required to deploy and operate machine learning models. Learn more lessons from the field with Comet experts.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

IBM Journey to AI blog

APRIL 4, 2024

cloud servers, data storage , networking capabilities, automation , software, data analytic tools)—with the security and control of on-premises IT infrastructure. SaaS, or Software-as-a-Service , is on-demand access to ready-to-use software apps (e.g., Adobe Creative Suite, Slack).

DevOps 305

DevOps Automation Software Development Generative AI 305

IBM Journey to AI blog

AUGUST 6, 2024

Serverless, or serverless computing, is an approach to software development that empowers developers to build and run application code without having to worry about maintenance tasks like installing software updates, security, monitoring and more. How does serverless work?

DevOps 113

DevOps Big Data Chatbots Artificial Intelligence 113

Unite.AI

SEPTEMBER 4, 2024

System for Cross-Domain Identity Management (SCIM): This automation tool streamlines user provisioning and access control management across different domains and systems. Software Development and Code Management The native GitHub integration makes Claude a powerful ally for engineering teams.

DevOps 147

DevOps Large Language Models Automation Software Development 147

IBM Journey to AI blog

JANUARY 26, 2024

At Instana, addressing our customers’ needs and creating a simple tool that is easy to use is fundamental to helping our DevOps and SRE teams reduce burnout rates, allowing them to excel in what they do best. We announced our action framework, where you can create new actions or reuse your existing automation (e.g.,

Automation 169

Automation DevOps Software Development Artificial Intelligence 169

IBM Journey to AI blog

JULY 16, 2024

To maintain consistency and expedited deployments, Kate’s code repository is configured to trigger Azure DevOps pipeline builds, which has automation capability to perform all deployment operations. Adopting cloud-native development Fast forward a few years. Curious about this possibility, she decided to give it a try.

DevOps 100

DevOps Automation Software Development Generative AI 100

Mlearning.ai

DECEMBER 21, 2023

Design patterns in software engineering are typical solutions to common problems in software design. They represent best practices, evolved over time, and are a toolkit for software developers to solve common problems efficiently. The team sets up an automated pipeline for continuous integration and deployment.

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

Flipboard

NOVEMBER 24, 2023

This offering enables BMW ML engineers to perform code-centric data analytics and ML, increases developer productivity by providing self-service capability and infrastructure automation, and tightly integrates with BMW’s centralized IT tooling landscape. This results in faster experimentation and shorter idea validation cycles.

ML 153

ML DevOps Data Scientist ML Engineer 153

IBM Journey to AI blog

NOVEMBER 2, 2023

When it comes to modern IT infrastructure, the role of Kubernetes —the open-source container orchestration platform that automates the deployment, management and scaling of containerized software applications (apps) and services—can’t be underestimated.

DevOps 324

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. Automating data labeling Data labeling is an inherently labor-intensive task that involves humans (labelers) to label the data. Let’s look at how to automate model building next!

DevOps 120

DevOps Automation Machine Learning Metadata 120

ODSC - Open Data Science

APRIL 12, 2023

Red Hat Since its start with the Red Hat® Enterprise Linux®, Red Hat has expanded its products to include agile integration, management and automation solutions, middleware, cloud-native application development, and hybrid cloud infrastructure. 25 years in, they remain committed to operating transparently, responsibly, and open source.

Data Science 97

Data Science DevOps Deep Learning Software Development 97

AWS Machine Learning Blog

OCTOBER 2, 2023

An MLOps pipeline allows to automate the full ML lifecycle from data labeling to model training and deployment. Implementing an MLOps pipeline at the edge introduces additional complexities that make the automation, integration, and maintenance processes more challenging due to the increased operational overhead involved.

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DevOps Automation ML Computer Vision 119