However, as systems grow more complex and the amount of data grows exponentially, so do the challenges in troubleshooting and maintaining them. Generative AI promises to improve the SRE experience and tame complexity. In particular, AI assistants based on retrieval augmented generation (RAG) are accelerating root cause analysis (RCA) and improving customer experiences.

The observability challenge

Observability provides complete visibility into system and application behavior, performance, and health using multiple signals such as logs, metrics, traces, and profiling. Yet, the reality often needs to catch up. DevOps teams and SREs frequently find themselves drowning in a sea of logs, metrics, traces, and profiling data, struggling to extract meaningful insights quickly enough to prevent or resolve issues. The first step is to leverage OpenTelemetry and its open standards to generate observability data in consistent and understandable formats. This is where the intersection of OpenTelemetry, GenAI, and observability becomes not just valuable, but essential.

RAG-based AI assistants: A paradigm shift 

RAG represents a significant leap forward in AI technology. While LLMs can provide valuable insights and recommendations leveraging public domain expertise from OpenTelemetry knowledge bases in the public domain, the resulting guidance can be generic and of limited use. By combining the power of large language models (LLMs) with the ability to retrieve and leverage specific, relevant internal information (such as GitHub issues, runbooks, customer issues, and more), RAG-based AI Assistants offer a level of contextual understanding and problem-solving capability that was previously unattainable. Additionally, the RAG-based AI Assistant can retrieve and analyze real-time telemetry from OTel and correlate logs, metrics, traces, and profiling data with recommendations and best practices from internal operational processes and the LLM’s knowledge base.

In analyzing incidents with OpenTelemetry, AI assistants that can help SREs:

1. Understand complex systems: AI assistants can comprehend the intricacies of distributed systems, microservices architectures, and the OpenTelemetry ecosystem, providing insights that take into account the full complexity of modern tech stacks.
2. Offer contextual troubleshooting: By analyzing patterns across logs, metrics, and traces, and correlating them with known issues and best practices, RAG-based AI assistants can offer troubleshooting advice that is highly relevant to the specific context of each unique environment.
3. Predict and prevent issues: Leveraging vast amounts of historical data and patterns, these AI assistants can help teams move from reactive to proactive observability, identifying potential issues before they escalate into critical problems.
4. Accelerate knowledge dissemination: In rapidly evolving fields like observability, keeping up with best practices and new techniques is challenging. RAG-based AI assistants can serve as always-up-to-date knowledge repositories, democratizing access to the latest insights and strategies.
5. Enhance collaboration: By providing a common knowledge base and interpretation layer, these AI assistants can improve collaboration between development, operations, and SRE teams, fostering a shared understanding of system behavior and performance.

Operational efficiency

For organizations looking to stay competitive, embracing RAG-based AI assistants for observability is not just an operational decision—it’s a strategic imperative. It helps overall operational efficiency through:

1. Reduced mean time to resolution (MTTR): By quickly identifying root causes and suggesting targeted solutions, these AI assistants can dramatically reduce the time it takes to resolve issues, minimize downtime, and improve overall system reliability.
2. Optimized resource allocation: Instead of having highly skilled engineers spend hours sifting through logs and metrics, RAG-based AI assistants can handle the initial analysis, allowing human experts to focus on more complex, high-value tasks.
3. Enhanced decision-making: With AI assistants providing data-driven insights and recommendations, teams can make more informed decisions about system architecture, capacity planning, and performance optimization.
4. Continuous learning and improvement: As these AI Assistants accumulate more data and feedback, their ability to provide accurate and relevant insights will continually improve, creating a virtuous cycle of enhanced observability and system performance.
5. Competitive advantage: Organizations that successfully leverage RAG AI Assistants in their observability practices will be able to innovate faster, maintain more reliable systems, and ultimately deliver better experiences to their customers.

Embracing the AI-augmented future in observability

The combination of RAG-based AI assistants and open source observability frameworks like OpenTelemetry represents a transformative opportunity for organizations of all sizes. Elastic, which is OpenTelemetry native, and offers a RAG-based AI assistant, is a perfect example of this combination. By embracing this technology, teams can transcend the limitations of traditionally siloed monitoring and troubleshooting approaches, moving towards a future of proactive, intelligent, and highly efficient system management.

As leaders in the tech industry, it’s imperative that we not only acknowledge this shift but actively prepare our organizations to leverage it. This means investing in the right tools and platforms, upskilling our teams, and fostering a culture that embraces AI as a collaborator in our quest to achieve the promise of observability.

The future of observability is here, and it’s powered by artificial intelligence. Those who recognize and act on this reality today will be best positioned to thrive in the complex digital ecosystems of tomorrow.

To learn more about Kubernetes and the cloud native ecosystem, join us at KubeCon + CloudNativeCon North America, in Salt Lake City, Utah, on November 12-15, 2024.

The post Accelerate root cause analysis with OpenTelemetry and AI assistants appeared first on SD Times.

RAG is a practice in which local data is added to an LLM, such as private company data or data that is more up-to-date than the LLMs training set. This allows it to give more accurate responses and reduces the occurrence of hallucinations.  

Playground offers a low-code interface for adding data to an LLM for RAG implementations. They can use any data stored in an Elasticsearch index for this. 

It also allows developers to A/B test LLMs from different model providers to see what suits their needs best. 

The platform can utilize transformer models in Elasticsearch and also makes use of the Elasticsearch Open Inference API that integrates with inference providers, such as Cohere and Azure AI Studio. 

“While prototyping conversational search, the ability to experiment with and rapidly iterate on key components of a RAG workflow is essential to get accurate and hallucination-free responses from LLMs,” said Matt Riley, global vice president and general manager of Search at Elastic. “Developers use the Elastic Search AI platform, which includes the Elasticsearch vector database, for comprehensive hybrid search capabilities and to tap into innovation from a growing list of LLM providers. Now, the playground experience brings these capabilities together via an intuitive user interface, removing the complexity from building and iterating on generative AI experiences, ultimately accelerating time to market for our customers.”

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This follows OpenTelemetry’s announcement in March that it would be supporting profiling and was working towards having a stable spec and implementation sometime this year.

Elastic’s agent profiles every line of code running on a company’s machines, including application code, kernels, and third-party libraries. It is always running in the background and can collect data about an application over time. 

It measures code efficiency across three categories: CPU utilization, CO2, and cloud cost. According to Elastic, this helps companies identify areas where waste can be reduced or eliminated so that they can optimize their systems. 

Universal Profiling currently supports a number of runtimes and languages, including C/C++, Rust, Zig, Go, Java, Python, Ruby, PHP, Node.js, V8, Perl, and .NET. 

“This contribution not only boosts the standardization of continuous profiling for observability but also accelerates the practical adoption of profiling as the fourth key signal in OTel. Customers get a vendor-agnostic way of collecting profiling data and enabling correlation with existing signals, like tracing, metrics, and logs, opening new potential for observability insights and a more efficient troubleshooting experience,” Elastic wrote in a blog post. 

OpenTelemetry echoed those sentiments, saying: “This marks a significant milestone in establishing profiling as a core telemetry signal in OpenTelemetry. Elastic’s eBPF based profiling agent observes code across different programming languages and runtimes, third-party libraries, kernel operations, and system resources with low CPU and memory overhead in production. Both, SREs and developers can now benefit from these capabilities: quickly identifying performance bottlenecks, maximizing resource utilization, reducing carbon footprint, and optimizing cloud spend.”

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• Why OpenTelemetry is driving a new wave of innovation on top of observability data

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Chris Meserole appointed first director of the Frontier Model Forum

Earlier this year, Anthropic, Google, Microsoft, and OpenAI formed the Frontier Model Forum, an organization dedicated to safely and responsibly developing frontier AI models. In October the organization announced its first director will be Chris Meserole. 

He was previously director of the AI and Emerging Technology Initiative at the Brookings Institution, a research institute for public policy. His past research has been featured in publications such as the New Yorker, The New York Times, Foreign Affairs, Foreign Policy, and Wired. 

BlackBerry CEO John Chen to retire

Chen’s retirement is effective on November 4, 2023, and Richard (Dick) Lynch will serve as the interim CEO while a replacement is found, and will also be Chen’s successor as chair of the board. Lynch has been on the board of the company since 2013 and Chen has been CEO since then too. 

“Today I announced, with a very heavy heart, that I am retiring from BlackBerry, on November 4, 2023, now that the Company’s future has been stabilized,” Chen wrote in a memo to employees. “You may have heard the story before of how Prem Watsa forced me out of retirement in 2013 with a plea to save BlackBerry, an iconic company beloved to many people including me. How could I say no? I joined BlackBerry with three key priorities. First, to ensure BlackBerry’s survival by repairing its financial health as the Company was just days away from potential bankruptcy at the time. Second, to establish a new strategy and line up the Company’s capabilities to deliver on that strategy. Third, to set the Company up for long-term growth. Now that each of these priorities has been achieved, the time seems right for me to leave.

Hanneke Faber joins Logitech as new CEO

Faber is coming to Logitech after gaining 30 years of experience across a variety of industries. Previously, she led the nutrition business at Unilever for six years, and prior to that she held leadership roles at Ahold Delhaize and Procter & Gamble. 

She is also on the board of directors at Tapestry Inc, which is the parent company of Coach, Kate Spade New York, and Stuart Weitzman, and was recognized in 2021 by Fortune magazine as an influential international woman in business. 

She will initially join Logitech at the company’s headquarters in Switzerland, beginning December 1st, before moving to Silicon Valley sometime next year. 

“Hanneke will continue to drive the innovation Logitech is known for, and advance the company’s unique culture, to unlock Logitech’s full potential for long-term growth and increased value for all our stakeholders,” said Wendy Becker, chairperson of the board for Logitech. “Her passion and strategic vision will lead Logitech into our next chapter.”

Abhishek Singh joins Elastic as general manager for Observability

In this new role, he will oversee Elastic Observability and be responsible for bringing forward delivery of new advancements like the Elastic AI Assistant for Observability.

Previously he was vice president of product at Datadog, and before that he held leadership roles at AWS, BlackRock, and Webscan. While at AWS, Singh was the general manager of AWS X-Ray, which is also an observability solution. 

“I’ve been impressed by Elastic’s innovative use of generative AI which allows an organization to use their proprietary data for context awareness that leads to more accurate solutions,” said Singh. “I look forward to working with Elastic to help enterprises break down silos by bringing together infrastructure, application, user, and business telemetry for end-to-end observability on a single AI-powered platform.”

Microsoft appoints Takeshi Numoto as executive vice president and chief marketing officer

Numoto will step into the role to replace Chris Capossela, who is leaving the company after 32 years. Numoto has worked at Microsoft for over two decades, most recently in the Commercial Chief Marketing Officer role. 

Yusuf Mehdi will be stepping into Numoto’s old role, and will continue to lead the Search, Ad, & News, and Devices & Creativity Customer Solution Areas. 

“Takeshi has been at the heart of our Cloud transformation, he’s a fantastic systems thinker who works end-to-end across all functions from engineering to finance to operations to sales, and he’s built a great team of marketing leaders,” said Satya Nadella, CEO of Microsoft. “I’m thrilled for him to step into the CMO role for Microsoft and drive our vision forward.

The post People on the Move in Tech in October appeared first on SD Times.

ESRE offers organizations assistance with creating secure deployments so they can access the full value of their proprietary structured and unstructured data while also working to improve infrastructure.

With this, users can build custom generative AI applications without worrying about the size and overall cost of running large language models. 

“Generative AI is a revolutionary moment in technology and the companies that get it right, fast, are tomorrow’s leaders,” said Ash Kulkarni, CEO of Elastic. “The Elasticsearch Relevance Engine is available today, and we’ve already done the hard work of making it easier for companies to do generative AI right.”

Additionally, the ability to bring your own transformer model and integrate third-party transformer models provides users with the ability to create secure deployments and utilize the innovations of generative AI on their own business data. 

Key features of ESRE include:

• Relevance ranking capabilities such as BM25F for hybrid search
• A vector database for storing and querying embeddings in high-dimensions 
• A proprietary transformer model that offers out-of-the-box semantic search
• Bring your own transformer models 
• An integration with third-party transformer models like OpenAI GPT through APIs

The post Elasticsearch announces new relevance engine designed to bring AI innovation to enterprise data appeared first on SD Times.

This comes as a long awaited conclusion to the license battle between the two companies due to the change to the license of ElasticSearch and Kibana back in 2021. The open-source license was changed to SSPL as a result of Elastic believing customers were being misled by Amazon offering Amazon Elasticsearch Service, and that Amazon was abusing the intent of the open-source license and profiting off the project. 

Amazon has since renamed Amazon Elasticsearch Service, to Amazon OpenSearch Service. 

With this resolution, the companies hope to remove any confusion in the marketplace and provide clarity to their communities and customers.

This resolution means that there is now only one Elasticsearch and it comes from Elastic. When using Elasticsearch, regardless of it being the Elastic Cloud service in AWS, Azure, or Google Cloud, it is coming directly from the people who created the product.

According to Elastic, in the future it will be looking to collaborate more with Amazon in order to bring added ease of use to their shared customer base who use Elastic on AWS. Elastic has already seen benefits from its partnership around recent areas of investment geared towards streamlining data ingestion and making onboarding easier to Elastic Cloud on AWS.

Additionally, Elastic has announced over 20 new integrations in order to streamline data ingestion into Elastic. These include AWS FireLens, Amazon S3 Storage Lens, the Elastic Serverless Forwarder in the AWS Serverless Application Repository, Elastic and AWS Web Application Firewall, and the Elastic and AWS Network Firewall.  

Elastic Cloud has also been certified as a part of the AWS ISV Workload Migration Program. This works to support the migration of customers to Elastic Cloud on AWS and allows Elastic to simplify the migration process with onboarding guidance and migration resources.

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With this, several reliability and quality improvement have been made, including:

• CLR: Addresses rare crashes and hangs that can appear in cases where GC occurs while another thread is in the middle of certain special paths used to invoke shared-generic code from non-shared-generic contexts.
• WPF: Addresses a hang when scrolling a list control where certain conditions are met, addresses an exception “Height must be non-negative” that can occur with the addition of items or groups to the collection displayed by an items control, and addresses an issue where a shared ContextMenu no longer displays after it fails to display once because its owner was removed from the visual tree.

For more information, visit here. 

Support ending for older versions of Visual Studio

In order to keep users secure, Microsoft has announced that several older versions of Visual Studio will no longer be supported in the near future. These include Visual Studio 2012 with support ending January 9, 2023, Visual Studio 2017 with mainstream support ending April 12, 2022, Visual Studio 2019 version 16.7 with support ending April 12, 2022, and Visual studio 2019 Preview Channel with support ending after April 2022.

With this, Microsoft recommends that users of these versions upgrade to Visual Studio 2022, especially those currently using the Community Edition. With Visual Studio 2022, users gain access to three channels: Preview Channel, Current Channel, and Long-Term Servicing Channels.

To learn more, visit here.

Elastic 8.0 released

With this release come several enhancements being made to Elasticsearch’s vector search capabilities, native support for modern natural language processing models, increasingly specified data onboarding, and a streamlined security experience. 

Additionally, users are able to perform named entity recognition, sentiment analysis, text classification, and more straight from Elasticsearch, without any additional coding. 

Elastic 8.0 is generally available now on Elastic Cloud, the hosted Elasticsearch offering that includes all of the new features in this latest release. See here to get started. 

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Among the upgrades featured is native Google Cloud data source integration with Google Cloud Dataflow. This provides users with faster data ingestion in Elastic Cloud as well as a simplified data architecture. This integration allows users to easily and securely ingest Pub/Sub, Big Query, and Cloud Storage data into their Elastic Cloud deployments. 

In addition, there have also been updates to Elasticsearch and Kibana that include: enhancements to runtime fields which gives users a new way to explore their data with the flexibility of schema on read and schema on write. 

InstallAware X14 for Windows 11

InstallAware released InstallAware X14, its first major version upgrade in almost a year. Some features included in the update are: the ability to programmatically pin apps to the new Windows 11 Start Menu/Taskbar, fully updated definitions for Windows 11 and Server 2022, and the ability to deploy .NET 6 in a single click.

In addition, users can also programmatically emit setup scripts from 64-bit binaries with the new 64-bit Scripting Interface as well as gain access to Full Stack ARM64 support including native setup captures and a 100% runtime free setup engine.

For more information on the upgrade, visit InstallAware’s website.

Testim Pro free for startups under 20 employees 

Testim is now offering Testim Pro features free to startups with fewer than 20 employees. With this, smaller startups gain access to Professional plan features such as: accessibility testing, TestOps, network mocking, API testing, and more.

Testim has also recently added TestOps features like pull requests, test status, owners, folders, and insights in order to help teams grow their test automation projects more efficiently and now small startups can gain access to those tools. 

The Professional plan also includes advanced features such as: scheduler, TestOps dashboard, email validation, pull requests, and test lifecycle status, all of which are essential to integrating a companies CI/CD pipeline and scaling your quality in your application. To find out if your startup qualifies to get Testim Pro for free, visit Testim’s website. 

Apache weekly update

ASF’s official global conference series, ApacheCon, is wrapped up for 2021 with presentations for ApacheCon Asia available on the ASF Youtube channel and ApacheCon@Home presentations to be posted shortly. 

Apache’s distributed infrastructure runs across three continents and 7M+ weekly checks yield uptime at 100.00%. Performance checks across 50 different service components spread over more than 250 machines in data centers globally. 

This past week, Apache Code Snapshot showed 328 Apache committers changed 7,398,124 lines of code over 2,924 commits. The top 5 contributors listed are: Harikishna Patnala, Gary Gregory, Andy Seaborne, Daniel Gruno, and Mark Thomas.

Apache also announced the release of Apache Druid 0.22.0, Apache Kafka 3.0.0, Apache Ignite 2.11.0, Apache NetBeans 12.5, and Apache Pulsar 2.8.1.

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One such person with that belief is Steve Kearns, the vice president of product management at Elastic, who joined SD Times podcast “What the Dev?” to explain his reasoning. 

According to Kearns, for a long time people have talked about DevOps in the context of bringing APM together with infrastructure and system metrics, but as systems become more complex the need for better observability grows and DevOps becomes a hard requirement for businesses. 

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One thing that makes Kearns feel DevOps is still early days is that everyone has to start somewhere. “You don’t just wake up one day and have fully instrumented applications and a whole architecture,” said Kearns. “And so making sure that you can start in a way that’s appropriate, convenient to you —  we see a lot of people starting with logs. I need to understand the logs my applications are putting out because if there’s a problem, the logs will tell me at least a little bit about it. But then how do I grow from there to the other pillars? And that’s why when I’m thinking about how we’re in these early days, everybody’s at a different stage of their own evolution with one application across their whole organization. And so there’s a lot of different variation of how people get into these different areas.”

Kearns also noted that new technologies, like Kubernetes and containers, address a number of core challenges DevOps teams face, but also make it dramatically harder to pinpoint what caused an issue when issues do occur. 

So ensuring proper visibility into those new tools is crucial in order to be successful at implementing them, which requires constantly leveling up your monitoring capabilities. “Certainly the benefits here are worthwhile, provided you’re ready for the implications of moving in that direction, which is needing better visibility into how they operate,” said Kearns. 

Kearns recommends that companies be always re-evaluating the tools they use for monitoring to ensure that their needs are still being met. 

One trap companies could fall into is that they might have implemented an APM solution seven years ago and still use it, but it might not be the best tool currently for what your DevOps teams want to do. For example, it might not have cost effective support for logs, be able to handle system metrics, or have great integration with Kubernetes. 

“I think it’s really important to be practical about this, but always be trying to say ‘what can I do to better serve my customers to give them a better experience?’ … If you have fast response times people enjoy the product more even if you don’t add another feature. If you can keep that performance fast, keep the reliability high, it makes a difference. And that really ends up being the thing that drives a lot of adoption of this technology.”

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The new version also includes an update regarding the Microsoft Python Language Server’s end of life with Pylance now as the default language support, as well as an update for Python 2.7 IntelliSense support.

The full list of improvements and new features is available here.

Elastic completes acquisition of build.security

The company behind ElasticSearch and the Elastic Stack announced that it has completed the acquisition of build.security, a policy definition and enforcement platform that leverages the open-source standard Open Policy Agent.

“Build.security will enhance our customer’s ability to ensure that their cloud environments are secure in keeping with the policies they have in place, continuously validate their security posture against well-established standards such as the CIS benchmarks, and resolve the complexity associated with building authorization into applications at deployment time,” said Shay Banon, founder and CEO of Elastic.

Micro Focus LoadRunner Cloud 2021.08 

The new release of the cloud-based performance engineering solution includes the ability to customize the capacity for cloud load generators from the user interface, new locations added for cloud load generators, enhanced GIT integration, and more. 

Also, when adding or removing on-premises load generators in a test, users can use the Distribute Evenly button to automatically assign an equal number of Vusers to all on-premises load generators participating in the test.

DOM interactive event, DOM content loaded event duration, and Page load event duration are new client-side experience measurements that were added to the dashboard metrics. 

Additional details are available here.

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