Tagged With: ai Archives | Page 2 of 2

Tagged With: ai

November 30, 2023

DevOps Best Practices for Faster and More Reliable Software Delivery

Noutați

Imagine a scenario where teams creating the software and delivery aren’t just passing information but sitting together, brainstorming and solving problems in real time. That’s the core of DevOps. It’s not a one-click software solution, but teams working together to provide a reliable solution for seamless and faster software delivery. Let’s take an example of an app or software update; users would expect it to work seamlessly. The secret here for that seamless experience is often a well-structured DevOps strategy. DevOps isn’t just about speeding things up, it’s about balancing the need for speed with the need for stability. According to research, 99% of organizations witnessed a positive impact after implementing DevOps in their business delivery processes. They’re deploying updates far more frequently, their failure recovery is lightning-fast, and they see fewer issues when they launch new features. Using DevOps for Efficient Software Delivery DevOps is crucial for organizations looking to resolve the complexities of modern software delivery. It bridges the gap between ‘code complete’ and ‘code in production,’ ensuring that software isn’t just created but delivered swiftly and effectively to the end-user. This approach not only accelerates time-to-market but also enhances product quality and customer satisfaction. By adopting continuous integration and continuous delivery (CI/CD), automation, and constant feedback, DevOps empowers teams to respond to market changes with agility and confidence. It’s about balancing processes, people and technology that work together to unlock higher efficiency, innovation and success. Implementing Continuous Integration and Continuous Deployment (CI/CD) Continuous integration and continuous deployment (CI/CD) are core practices in the DevOps approach, designed to streamline and automate the steps in getting software from development to deployment. CI/CD establishes a framework for development teams that supports frequent code changes while maintaining system stability and high-quality output. This method depends on automation to detect problems early, reduce manual errors and speed up the delivery process, ensuring that new features, updates and fixes are available to users quickly and reliably. Teams should follow several best practices: • Commit to Version Control Rigorously: Every piece of code, from application to configuration scripts, should be version-controlled. It ensures that any changes can be tracked, rolled back or branched out at any point, providing a solid foundation for collaborative development and deployment.• Automate the Build for Consistency: Automation is the key to CI/CD. By automating the build process, one can ensure that the software can be reliably built at any time. This automation includes compiling code, running database migrations, and executing any necessary scripts to move from source code to a working program.• Incorporate Comprehensive Automated Testing: A robust suite of automated tests, including unit, integration, acceptance, and regression tests, should be run against every build to catch bugs early. Automated tests act as a safety net that helps maintain code quality throughout the rapid pace of DevOps cycles.• Replicate Production in Staging: A staging environment replicates your production environment and is crucial for pre-deployment testing. It should mimic production as closely as possible to surface any environment-specific issues that could otherwise cause unexpected behavior after release. • Ensure Quick and Safe Rollbacks: The ability to roll back to a previous state quickly is essential. This safety measure minimizes downtime by swiftly reversing failed deployments or critical issues without going through a prolonged troubleshooting process during peak hours.• Monitor Relentlessly […]

November 12, 2023

NetApp Extends Microsoft Alliance to Include CloudOps Tools

Noutați

NetApp this week extended its alliance with Microsoft to now include its CloudOps portfolio of tools for optimizing cloud computing environments. Previously, the alliance between the two companies focused on data management but is now expanding to include tools to deploy workloads, improve performance and reduce costs using machine learning algorithms across both instances of virtual machines and the Azure Kubernetes Service (AKS). Kevin McGrath, vice president of Spot by NetApp, said in more challenging economic times, there’s a lot more focus on programmatically reining cloud costs using FinOps best practices within the context of a DevOps workflow. Organizations are also starting to create platform engineering teams to more efficiently manage DevOps workflows at scale across hybrid cloud computing environments, he added. For years, developers have been provisioning cloud infrastructure resources with little to no oversight. Unfortunately, developers are also prone to over-provision infrastructure resources to ensure maximum application availability. Many of those infrastructure resources never wind up being consumed by the application, so the cost of cloud computing winds up becoming inflated. IT leaders are also being increasingly required to make sure cloud costs are more predictable. Sudden spikes in consumption resulting in higher monthly bills are an unwelcome surprise to finance teams that are now required to manage costs more closely. Ongoing advances in artificial intelligence (AI) should make it easier to predict costs across highly dynamic cloud computing environments. Navigating all the pricing options that cloud service providers make available is challenging. IT teams need to clearly understand the attributes of each workload to ensure optimal usage of cloud infrastructure resources. Less clear is the degree to which IT teams are pitting cloud service providers against one another. Pricing across the cloud services that most organizations use today is fairly consistent. Most organizations that deploy workloads in the cloud tend to run the bulk of them on the same service because they lack the internal expertise needed to manage multiple clouds equally well. There may be some workloads running on additional clouds, but enterprise licensing agreements reward customers for running more workloads on a cloud. The only way to really optimize cloud spending is to shift workloads to less expensive tiers of service that might only be available for a relatively limited amount of time. One way or another, the management of cloud computing is finally starting to mature. As the percentage of workloads that organizations have running in the cloud steadily increases, IT teams are becoming more adept at both maximizing application performance and the associated return on investment (ROI). Each IT organization will need to decide for itself how best to manage cloud computing environments as it continues to build and deploy cloud-native applications alongside legacy monolithic applications running on virtual machines, but NetApp is betting the need for tools such as CloudOps will increase as cloud computing environment become more complex. The challenge, as always, is finding and retaining the talent needed to manage cloud computing environments when every other organization is looking for that same expertise.

November 9, 2023

How to Solve the GPU Shortage Problem With Automation

Noutați

GPU instances have never been as precious and sought-after as they have since generative AI captured the industry’s attention. Whether it’s due to broken supply chains or the sudden demand spike, one thing is clear: Getting a GPU-powered virtual machine is harder than ever, even if a team is fishing in the relatively large pond of the top three cloud providers. One analysis confirmed “a huge supply shortage of NVIDIA GPUs and networking equipment from Broadcom and NVIDIA due to a massive spike in demand.” Even the company behind the rise of generative AI–OpenAI–suffers from a lack of GPUs. And companies have started adopting rather unusual tactics to get their hands on these machines (like repurposing old video gaming chips). What can teams do when facing a quota issue and the cloud provider runs out of GPU-based instances? And once they somehow score the right instance, how can you make sure no GPUs go to waste? Automation is the answer. Teams can use it to accomplish two goals: Find the best GPU instances for their needs and maximize their utilization to get more bang for their buck. Automation Makes Finding GPU Instances Easier The three major cloud providers offer many types and sizes of GPU-powered instances. And they’re constantly rolling out new ones–an excellent example of that is AWS P5, launched in July 2023. To give a complete picture, here’s an overview of instance families with GPUs from AWS, Google Cloud and Microsoft Azure: AWS P3 P4d G3 G4 (this group includes G4dn and G4ad instances) G5 Note: AWS offers Inferentia machines optimized for deep learning inference apps and Trainium for deep learning training of 100B+ parameter models. Google Cloud Microsoft Azure NCv3-series NC T4_v3-series ND A100 v4-series NDm A100 v4-series When picking instances manually, teams may easily miss out on opportunities to snatch up golden GPUs from the market. Cloud automation solutions help them find a much larger supply of GPU instances with the right performance and cost parameters. Considering GPU Spot Instances Spot instances offer significant discounts–even 90% off on-demand rates–but they come at a price. The potential interruptions make them a risky choice for important jobs. However, running some jobs on GPU spot instances is a good idea as they accelerate the training process, leading to savings. ML training usually takes a very long time–from hours to even weeks. If interruptions occur, the deep learning job must start over, resulting in significant data loss and higher costs. Automation can prevent that, allowing teams to get attractively-priced GPUs still available on the market to cut training and inference expenses while reducing the risk of interruptions. In machine learning, checkpointing is an important practice that allows for the saving of model states at different intervals during training. This practice is especially beneficial in lengthy and resource-intensive training procedures, enabling the resumption of training from a checkpoint in case of interruptions rather than starting anew. Furthermore, checkpointing facilitates the evaluation of models at different stages of training, which can be enlightening for understanding the training dynamics. Zoom in on Checkpointing PyTorch, a popular ML framework, provides native functionalities for checkpointing models and optimizers during training. Additionally, higher-level libraries such as PyTorch Lightning abstract away much of the boilerplate code associated with training, evaluation, and checkpointing in PyTorch. Let’s take a […]

October 28, 2023

Digital.ai Update Extends Scope and Reach of DevSecOps Platform

Noutați

Digital.ai this week made generally available a Denali update to its DevSecOps platform that promises to make it simpler to integrate custom artificial intelligence (AI) models with the AI models developed by the company. At the same time, the company is adding self-guided workflows and templates to generate tests and implement DevSecOps best practices along with integrations with Terraform by Hashicorp, Azure Bicep, Azure Key Vault and AWS Secrets Manager. Finally, Digital.ai is adding an ARM Protection feature to better secure iOS applications without requiring embedded bitcode or integrations into the build system. DevOps teams can, via a single command, protect compiled applications locally with support for obfuscation, run-time active protections and application monitoring without uploading them to a third-party service. Greg Ellis, general manager for application security for Digital.ai, said the overall goal is to make it simpler for software engineering teams to invoke capabilities that have been embedded within the company’s DevSecOps platform. In the case of AI models, that also means instead of requiring DevOps teams to only use AI models developed by Digital.ai, the company is moving to make it simpler for DevOps teams that adopt its platform to incorporate custom AI models as they see fit as part of an ongoing effort to democratize intelligence at scale, he noted. In general, it’s already apparent organizations will be employing heterogeneous approaches to incorporating AI models into DevOps workflows, said Ellis. The challenge now is moving beyond experimenting with AI to embedding them within DevOps workflows, he added. It’s already clear developers are using generative AI to develop code at increasingly faster rates. The challenge now is to manage that accelerated pace of development when many organizations are already struggling to manage existing DevOps workflows at scale. Hopefully, AI technologies will also one day help software engineers find ways to manage that volume of code moving across their DevOps pipelines. In the meantime, organizations will also need to better define where the machine learning operations (MLOps) workflows that data scientists use to build AI models end and where DevOps workflows that will be used to embed AI models into applications begin. As is often the case when it comes to emerging technologies, cultural issues are just as challenging as the implementation hurdles that need to be overcome. At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated. Less clear is to what degree AI may drive organizations that have already embraced DevOps to adopt an alternative platform, but savvy DevOps teams are, at the very least, starting to map out which processes are about to be automated so they can have more time to focus on issues that add more value to the business.

October 22, 2023

Survey Surfaces Benefits of Applying AI to FinOps

Noutați, Software

A survey of 200 enterprise IT decision-makers published this week found organizations that have infused artificial intelligence (AI) into financial operations (FinOps) workflows to reduce IT costs are 53% more likely to report cost savings of more than 20%. Conducted by the market research firm Foundry on behalf of Tangoe, a provider of tools for managing IT and telecommunications expenses, the survey found organizations that embraced FinOps without any AI capabilities averaged less than 10% in cost savings. The top three drivers for adopting FinOps/cloud cost management programs are the need to increase cloud resource production and performance (70%), reduce budgets (60%) and rising costs (58%), and simpler overall program management (50%), the survey found. Major benefits included productivity savings (46%), cost savings (43%) and reduced security risks (43%). Nearly two-thirds of respondents cited service utilization and right-sizing of services as another reason to embrace FinOps. FinOps describes a methodology for embedding programmatic controls within DevOps workflows to reduce costs. In the face of increased economic headwinds, IT leaders are looking to reduce cloud computing costs, but it’s turning out to be more challenging than many of them anticipated. Cloud infrastructure is typically provisioned by developers using infrastructure-as-code (IaC) tools with little to no supervision. The reason for this is developers have long argued that waiting for an IT team to provision cloud infrastructure took too long. Developers would be more productive if they just provisioned cloud infrastructure themselves. However, after ten years of cloud computing, it’s become apparent there are a lot of wasted cloud infrastructure resources. Developers who don’t pay the monthly bills for cloud services tend to view available infrastructure resources as essentially infinite. It’s usually not until someone from the finance department starts raising cost concerns that developers even become aware there might be an issue. The challenge is that adopting FinOps best practices is not quite as easy as it might seem. In fact, more than half (54%) of survey respondents cited challenges in building the right process and human support systems for FinOps into workflows that have been in place for years. Chris Ortbals, chief product officer for Tangoe, said the simplest path to FinOps is to rely on a software-as-a-service (SaaS) platform designed from the ground up to leverage AI to help IT teams manage cloud computing and telecommunications expenses both before and after applications are deployed. Each DevOps team will ultimately need to determine how much they will implement metrics to foster more efficient consumption of cloud computing resources. The more aware of those costs DevOps teams are, the more likely that better decisions about what types of workloads should be run where and, just as importantly in the age of the cloud, at what time, given all the pricing options provided. Developers, of course, tend to jealously guard their prerogatives. Convincing them to give up their ability to provision cloud infrastructure on demand is going to be a challenge, at least until someone makes it plain how much all those cloud instances wind up costing the organization each and every month.

September 22, 2023

Will ChatGPT Replace Human Software Developers? Probably Not

Noutați, Software

Since the release of ChatGPT, there has been a great deal of hype around generative AI and how companies can leverage it to cut costs and democratize software and application development. Naturally, with discussions of cost-cutting and democratization come whispers about what will happen to software developers. This is a real and valid concern, but software developers’ skills, expertise and creativity are still very much needed. While generative AI and AI code generation tools like ChatGPT have shown some promise and potential benefits, they are still in their infancy—like many other innovative technological advancements. We also don’t know what scenarios they may present down the road or their true abilities when the technology matures. For instance, how will it integrate with other technologies? We don’t know what will happen when a ChatGPT-generated line of code breaks or needs to be changed. We don’t know if it can provide a novel solution to a unique problem or what security threats it will present. Given these unknowns, technology executives should think twice about replacing experienced and creative technology talent, such as software developers, with AI code generators. Will ChatGPT create novel code to solve a unique problem never encountered before? Probably not. A Tale as Old as Time (Or at Least a Decade) The technology industry has been searching for and developing new ways to make certain software development tasks much easier and more streamlined for years. One example of this is low-code/no-code. The notion of simplifying application development and replacing software developers with laypeople (citizen developers) has been around for more than a decade now, as low-code and no-code solutions have grown more popular. These solutions have promised that companies don’t need technical talent to get their software and app projects off the ground. However, if you look at the impact of these solutions today, their use can result in large amounts of technical debt and almost always require the skill of experienced software developers. The reason? Building complex software and applications is extremely difficult; it’s an art. Low-code and no-code solutions have their rightful place and can make things easier if a company is looking to launch a simple app or static web page. These solutions can increase the pace of development and time-to-market and enable everyday people without any development skills to facilitate them. However, they are not actually a complete solution and often overlook aspects of development that a human software developer would typically address. Without a skilled expert involved, low-code/no-code platforms often can’t solve a unique problem a company has. So, how does this relate to AI code generators like ChatGPT? Here’s how. A Similar Situation—With One Key Difference When thinking about their place in the development process, AI code generators are not that different from low-code or no-code solutions. The thinking is that they will also enable non-technical individuals to create software and applications with ease. Yet, there is one key difference—they promise expertise, too. But is the expertise coming from the AI code generator or the person piloting it? The answer is simple; it is not from the code generator. There have been examples of companies and individuals that have tried using ChatGPT to build code, and they have appeared to be successful. However, without the input of the individuals using it, it never would […]

September 22, 2023

Google De-Recruits 100s of Recruiters ¦ ARM Valued at $45½B in IPO

Noutați, Software

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters. This week: Google fires hundreds of recruiters, and ARM gets a sky-high valuation. 1. Layoffs for the recruiters themselves First up this week: Google’s hiring has slowed to such an extent that it has far too many in-house recruiters. Boo hoo? Analysis: Don’t shed a tear at task shedding I get it. Many reading this care little for the typical recruiter. All too often they seem like pointless brokers—adding no value to the process yet receiving a huge bonuses. But this news is the latest indication that DevOps jobs are harder to come by. Louise Matsakis has the scoop: Google lays off hundreds on recruiting team “Hard decision”Google is laying off hundreds of people across its global recruiting team as hiring at the tech giant continues to slow. … Workers who were laid off began learning their roles had been eliminated earlier today, according to posts on social media.…Google began reducing the speed of its hiring last year, after adding tens of thousands of workers in 2020 and 2021. … Google spokesperson Courtenay Mencini said, … “In order to continue our important work to ensure we operate efficiently, we’ve made the hard decision to reduce the size of our recruiting team.” Bring in the RecruitBot 4000. galaxytachyon explains: How likely is it that this is because of AI taking over the jobs? Sift through resumes, contact candidates, schedule some interviews, connect the hiring manager to the candidate, even getting some extra information from the candidate via email or phone calls are all things an LLM can efficiently do. They may actually do it even better than a regular human, since they might “know” more about the role and the technical requirements than an average [recruiter]. AI recruiters—and AI developers, too. Here’s Qbertino: I don’t expect those jobs to return. … After 23 years in IT I’m looking into a … career switch myself. Our industry is fully industrialized, custom coding is by now only for mostly totally broken legacy **** that will be replaced by SOA subscriptions within the next few years and what’s still left to code will be mostly done by AI quite soon I suspect.…Time to move on. It was an awesome ride but we’ve now finally built the bots that will replace us. Nice. This will spell more wealth for everyone in the long run even if we are out of cushy jobs with obscene salaries. When Google catches a cold, do other DevOps shops sneeze? Not in gijames1225’s experience: It’s weird being at a midsize company that has only accelerated hiring for engineers while the big players all go through these layoff cycles. The cynic in me sees them as token displays of fiscal responsibility being made for shareholders and a weird performativity of not wanting to be outdone by other tech giants. Another bit of me wonders about general productivity at these places if they can layoff so many people and nothing really appears to change (from a consumer perspective). All of which makes this Anonymous Coward wonder: I wonder what happens now to those who have threatened to quit or were reluctant to come in to physical offices. Meanwhile, u/saracenraider has questions: Do […]

September 22, 2023

JFrog swampUP: Addressing the Advent of AI

Noutați, Software

At JFrog SwampUp 2023, the buzz with all about AI, especially with JFrog’s announcement of Machine Learning (ML) Model Management capabilities. These conversations around AI and ML reflected these technologies’ growing influence and importance in the DevOps world. How much of the generative AI conversation is just hype, though? And what does that mean for AI and ML as a whole? Alan Shimel, CEO of Techstrong Group, and I sat down with Stephen Chin, VP of DevRel at JFrog, to find out. As far as Chin is concerned, even as more companies create and leverage AI models, these models must be managed like any other software component. Chin said JFrog Artifactory acts as a staging ground to operationalize models using DevSecOps best practices. Algorithms and models will continue to grow in size and complexity, and they will require robust processes around deployment and management – just like any other software artifact. The key, Chin said, is to think of ML as just another development language and leverage tools that standardize and streamline working with it. Compared to traditional enterprise applications, though, DevOps workflows for AI/ML are still relatively immature, but Chin said JFrog’s new model management capabilities aim to provide that missing automation and governance using DevSecOps best practices for governance, security, and licensing compliance. Additionally, Chin noted that AI/ML have become essential for development teams to keep up with the explosive demand for code. At this point, AI has become table stakes. In the AI arms race, the winners are those who understand AI has become a vital development tool to enhance productivity. In terms of job security, the losers are the ones who can’t keep up with the volume of code. According to Chin, you are out of the running if you don’t embrace AI. Looking ahead, AI will not make developers obsolete, though – quite the opposite. Given the quasi-unlimited appetite for new code, Chin emphasized that developers who embrace AI will have more work than ever. One way to think of it is that AI provides a new form of “outsourcing” to boost human productivity, much like previous waves of innovation in computer science. When it comes to security, there are still challenges that need to be addressed; code generated by today’s AI solutions still has significant drawbacks like potential data bias, lack of explainability and simple errors. In the long term, though, Chin believes AI itself will provide the solution to secure an exponentially growing codebase, given its superior scale. Just as AI will make individual developers more productive, it can also supercharge security teams – but it can also empower bad actors. The key will be continuing to democratize the benefits to even the playing field. AI promises to be a transformative technology on the scale of the Bronze Age or Quantum computing, Chin said, but the path forward will require humans and machines working together to ensure it’s used for good. It’s clear that the pace of innovation in AI and ML is rapidly accelerating. As these technologies become further democratized and integrated into developer workflows, they promise to transform how software is built and secured, Chin said. Companies must take advantage of this technology innovation by providing the pipelines and governance for this software revolution, he added. Chin believes the future will […]

Abonează-te la noutăți