IBM Journey to AI blog

SEPTEMBER 5, 2023

Developers require hands-on interaction with the tools they use—a deep relationship that makes the technology their own, even as they work in the cloud. Open-source software. DevOps, open source and the mainframe Open-source software and DevOps share a common philosophy and technical underpinnings.

DevOps 334

DevOps Automation Software Development AI 334

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. Versioning.

DevOps 140

DevOps Software Engineer Data Scientist ML 140

IBM Journey to AI blog

NOVEMBER 13, 2023

Overview of Kubernetes Containers —lightweight units of software that package code and all its dependencies to run in any environment—form the foundation of Kubernetes and are mission-critical for modern microservices, cloud-native software and DevOps workflows.

DevOps 336

DevOps Software Development Automation Large Language Models 336

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

Analytics Vidhya

FEBRUARY 2, 2022

Table of contents Overview Traditional Software development Life Cycle Waterfall Model Agile Model DevOps Challenges in ML models Understanding MLOps Data Engineering Machine Learning DevOps Endnotes Overview: MLOps According to research by deeplearning.ai, only 2% of the companies using Machine Learning, Deep learning have […].

DevOps 247

DevOps Software Development Machine Learning Deep Learning 247

IBM Journey to AI blog

AUGUST 12, 2024

MLOps is a set of practices that combines machine learning (ML) with traditional data engineering and DevOps to create an assembly line for building and running reliable, scalable, efficient ML models. AIOPs enables ITOPs personnel to implement predictive alert handling, strengthen data security and support DevOps processes.

Big Data 278

Big Data DevOps Automation Machine Learning 278

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.

Software Development 293

Software Development DevOps Big Data AI 293

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

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

Marktechpost

NOVEMBER 23, 2024

Gemini for DevOps Engineers This course teaches engineers to use Gemini to manage infrastructure. You will learn to prompt Gemini for application logs, create a GKE cluster, and explore build environments, with hands-on labs enhancing the DevOps workflow.

DevOps 110

DevOps Data Analysis Large Language Models Generative AI 110

IBM Journey to AI blog

FEBRUARY 29, 2024

DevOps: The combination of microservices as an architecture and containers as a platform is a strong pairing and a common foundation for many teams that embrace DevOps and development environments as the way they choose to handle software development.

DevOps 295

DevOps Software Development Artificial Intelligence Artificial Intelligence 295

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

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. As the software development landscape evolves, we are leveraging GenAI to automate those repetitive tasks that can bog teams down.

DevOps 147

DevOps Automation Responsible AI Software Development 147

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.

Software Development 133

Software Development DevOps Big Data AI 133

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

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

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.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

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 188

Data Science DevOps Machine Learning Data Scientist 188

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

Becoming Human

MAY 11, 2023

MLOps projects are projects that focus on implementing machine learning operations best practices into a company’s existing software development and deployment process. DevOps and DataOps: DevOps and DataOps are related approaches that emphasize collaboration between software developers and IT operations teams.

Machine Learning 78

Machine Learning DevOps Software Development Automation 78

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. The result? So what are you waiting for?

Software Engineer 40

Software Engineer Software Development DevOps ETL 40

Marktechpost

JUNE 25, 2024

Gemini for DevOps Engineers This course teaches engineers to use Gemini to manage infrastructure. You will learn to prompt Gemini for application logs, create a GKE cluster, and explore build environments, with hands-on labs enhancing the DevOps workflow.

DevOps 111

DevOps Data Analysis Large Language Models Generative AI 111

IBM Journey to AI blog

FEBRUARY 6, 2024

Cloud-based applications and services Cloud-based applications and services support myriad business use cases—from backup and disaster recovery to big data analytics to software development. Serverless computing allows software developers to devote more attention to the code and business logic specific to their applications.

DevOps 306

DevOps Automation Software Development Big Data 306

Unite.AI

SEPTEMBER 30, 2024

The result will be greater innovation and new benchmarks for speed and quality in software development. AI-powered QA is also becoming central to DevOps. Processes will become more efficient, and collaboration between development and QA teams will improve.

Automation 312

Automation AI AI DevOps 312

Unite.AI

NOVEMBER 17, 2023

AI recommends safer libraries, DevOps methods, and a lot more. Enhanced Static Application Security Testing (SAST) SAST examines source code to find security vulnerabilities without executing the software. As AI evolves in software development, so do the methods of cyber attackers.

Artificial Intelligence 299

Artificial Intelligence Artificial Intelligence DevOps Automation 299

Towards AI

FEBRUARY 10, 2024

Definition of a full-stack data scientist The sibling relationship between data science and software development has led to the borrowing of many concepts from the software development domain into data science practice. Read between the lines Let’s have a look at how a software development team is structured.

Data Scientist 101

Data Scientist Software Development Data Science Machine Learning 101

Unite.AI

DECEMBER 7, 2023

Clean Code promotes security, maintainability, and reliability, and can enable developers to anticipate and handle unexpected states more effectively and get the software back up and running quicker. Incorporating AI into the software development life cycle has its benefits, such as enabling developers to work more efficiently.

Software Development 243

Software Development DevOps Explainability AI 243

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. class Singleton: _instance = None def __new__(cls): if cls.

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

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

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.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

Towards AI

AUGUST 16, 2023

Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). The ideal MLOps engineer would have some experience with several MLOps and/or DevOps platforms. Figure 1: Venn diagram showing the relationship among the MLOps-related fields [Wikipedia].

DevOps 86

DevOps Machine Learning Software Development ML 86

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

Flipboard

APRIL 21, 2023

A report by GitLab finds that AI and ML in software development workflows show promise, but challenges like toolchain complexity and security concerns persist. The post DevSecOps: AI is reshaping developer roles, but it’s not all smooth sailing appeared first on TechRepublic.

Software Development 171

Software Development ML AI AI 171

Pickl AI

JANUARY 30, 2023

A Software Developer Uses Python: Backend Development : Python finds applications in developing server-side applications and APIs. The developer will use frameworks such as Django and Flask for this. If you have completed the Python certification, a number of job opportunities open up for you.

Python 97

Python DevOps Machine Learning Data Science 97

Unite.AI

SEPTEMBER 4, 2024

Software Development and Code Management The native GitHub integration makes Claude a powerful ally for engineering teams. Potential Use Cases for Claude Enterprise Customers To better understand the potential impact of the Claude Enterprise Plan, let's examine some real-world applications and feedback from early adopters.

DevOps 147

DevOps Large Language Models Automation Software Development 147

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. Kate and Tom’s story highlights the importance of DevOps processes and collaboration in software development.

DevOps 100

DevOps Automation Software Development Generative AI 100

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. You asked, and we delivered!

Automation 296

Automation DevOps Software Development Artificial Intelligence 296