September 2021 Summaries

New Relic is partnering with Aporia to provide a comprehensive monitoring solution for machine learning (ML) models in production. This integration enables data science teams to monitor their ML models' effectiveness, detect issues early, and make informed decisions to improve business outcomes. With New Relic's partnership with Aporia, users can seamlessly collaborate between data science and DevOps teams, customize dashboards, and receive live alerts for model performance degradation, data drift, unexpected bias, and other issues. This solution aims to provide full observability into ML-powered applications, enabling predictable ML performance and improved business outcomes.

New Relic has teamed up with Aporia to enhance machine-learning model monitoring by integrating Aporia’s alerts into New Relic One, enabling comprehensive model management within MLOps infrastructure. This partnership allows seamless collaboration between data science and DevOps teams, facilitating the development, testing, and monitoring of sophisticated ML models. Users can customize model performance metrics using Aporia's Inferences feature, while New Relic Alerts and Applied Intelligence provide a centralized notification system for operational monitoring and problem resolution. Aporia offers fast and secure monitoring for ML models in production, enabling the detection of issues like data drift and performance degradation. By connecting Aporia’s alerts to New Relic’s Incident Intelligence engine, teams gain full observability and customizable dashboards, promoting clear ML model visibility and predictable performance. For further setup and integration guidance, users can refer to the Incident Intelligence documentation.

Gatsby is a popular React-based framework used for building fast, secure, and powerful websites with a data layer that simplifies integrating different content, APIs, and services into one web experience. It builds static files to be deployed to a content delivery network (CDN), improving page load times and reducing operational complexity. A new Gatsby quickstart on New Relic Instant Observability provides granular build data directly in New Relic, allowing developers to debug and optimize their build times. The quickstart helps track the impact of each plugin, analyze performance issues, and identify areas for improvement. It also enables the evaluation of cloud providers, page creation time, image audit, and optimization opportunities for image processing. By using this quickstart, developers can gain insights into their Gatsby builds, reduce build times, and improve the overall development experience.

Gatsby, a React-based framework, is popular among developers using the Jamstack architecture for building fast and scalable websites by compiling sites into static files for deployment on content delivery networks (CDNs). New Relic has introduced a Gatsby quickstart feature in its Instant Observability platform, enabling users to monitor and optimize Gatsby build times by examining internal APIs for insights into the performance of APIs, plugins, and code changes. The blog details best practices for evaluating and improving the performance of large Gatsby sites, such as New Relic's documentation site, which utilizes a significant number of plugins and experiences frequent content updates. By leveraging the Gatsby quickstart integration, users can analyze the impact of different plugins, optimize build processes, and evaluate the efficiency of cloud providers. Additionally, the article highlights the importance of optimizing GraphQL queries and image processing to enhance build times, offering practical examples and strategies to improve efficiency and reduce operational complexity.

AWS GuardDuty is a threat detection service that protects AWS accounts, workloads, and data. It can be combined with AWS CloudTrail and New Relic One to monitor cloud services for threats. The integration allows users to correlate security events with other telemetry data, visualize them in dashboards, alert teams for faster resolution, retain data beyond AWS quotas, and receive alerts via EventBridge and a Kinesis Firehose delivery stream. It also provides use cases such as mitigating SSH brute force attacks and detecting publicly accessible S3 buckets, enabling users to speed up remediation and gain visibility into security issues in their AWS environment.

Cloud computing offers significant advantages such as flexibility and scalability, but it also presents potential security challenges, especially on platforms like Amazon Web Services (AWS). Amazon GuardDuty, a threat detection service, can mitigate these issues by monitoring AWS accounts, workloads, and data for threats. This process involves integrating GuardDuty with AWS CloudTrail and New Relic One, enhancing observability and allowing users to correlate AWS security events with telemetry data, visualize them in dashboards, and alert teams for rapid resolution. To implement this integration, you configure Amazon EventBridge and Amazon Kinesis Firehose to stream GuardDuty findings to New Relic Logs, and set up an API polling integration to capture CloudTrail events. Additionally, dashboard visualizations and NRQL alerts facilitate monitoring of specific threats such as SSH brute force attacks and public access to S3 buckets, improving the speed and efficacy of security event remediation.

New Relic has obtained certifications from independent, third-party auditing organizations, such as SOC2, ISO27001, and HITRUST to ensure compliance with data protection regulations. The company's Data Privacy Framework (DPF) certification under the EU-U.S. Data Privacy Framework (including Swiss- U.S. Privacy Framework and UK Extension to the DPF) has been formally approved by the United States Department of Commerce. This framework introduces new binding safeguards to address concerns raised by the CJEU, including limiting access to personal data by US intelligence services to what is necessary and proportionate. New Relic processes limited personal data, primarily telemetry data relevant to assessing technical performance, and employs strong technical and administrative security measures to protect customer data. The company's pre-signed Data Processing Agreement (DPA) explains how New Relic would process law enforcement requests pertaining to personal data in the event of a national security agency contact. With its robust compliance programs and certifications, New Relic supports customers in meeting their data controller obligations efficiently and effectively.

This rising junior at Oregon State University is a recipient of a New Relic scholarship to attend this year's virtual Grace Hopper Celebration conference, an event that supports university students who identify as women and are looking for career opportunities in the tech industry. She is majoring in computer science with a concentration in artificial intelligence, and her experience with organizations like Girls Who Code and the OSU ACM-W chapter has helped her grow her professional network and build confidence in her skills. With the support of these organizations and the scholarship, she hopes to pursue a career in research or industry after graduation, possibly with an internship or graduate school application. She advises other women interested in pursuing tech careers to be authentic and not apologize for their struggles, as imposter syndrome is common among those in this field.

As of October 12, 2023, New Relic's certification under the EU-U.S. Data Privacy Framework has been approved, simplifying data transfers from the EU, UK, and Switzerland to the U.S. by eliminating the need for additional mechanisms like Standard Contractual Clauses (SCCs). This certification reflects an adaptation to the Schrems II decision, which had invalidated the Privacy Shield as a compliant data transfer mechanism. The updated Data Privacy Framework introduces binding safeguards, such as limiting U.S. intelligence access and establishing a Data Protection Review Court, thereby addressing previous concerns. New Relic ensures data security and compliance with GDPR through robust technical and organizational measures, independent certifications, and customizable settings that allow customers to control personal data transmission. While New Relic predominantly processes telemetry data relevant to system performance rather than personal data, it provides options for customers to manage and reduce the risk of transmitting sensitive information.

Andrea Tongsak, a junior majoring in computer science with a concentration in artificial intelligence at Oregon State University, shares her experiences and aspirations after receiving a New Relic scholarship to attend the virtual Grace Hopper Celebration conference. Tongsak, who is actively involved in OSU's Association of Computing Machinery Women's Chapter (ACM-W), reflects on how the scholarship has reinforced her decision to pursue computer science after initially feeling out of place in the field. She hopes to publish a research paper and attend a research conference before graduating, and she plans to apply for graduate school with a focus on either research or industry work. Tongsak also highlights her involvement in organizations like Girls Who Code and advises other women interested in tech careers to be authentic and open about their struggles.

This week, New Relic hosted two free events for Black, Indigenous, and People of Color (BIPOC) in the engineering and tech community, BIPOC In Tech: Writing Your Own Script and Allyship in Action. The events aimed to build a more inclusive community by providing opportunities for underrepresented engineers, developers, and professionals to share their experiences and learn from industry experts. After attending these events, attendees reported feeling inspired and empowered to write their own scripts, with key takeaways including sharpening internal fortitude, forging one's own lane, drawing strength from the past, present, and future, and surrounding oneself with co-conspirators. These events are part of New Relic's diversity, equity, and inclusion (DE&I) journey, which aims to create a more inclusive community for all professionals in the tech industry.

New Relic has announced the general availability (GA) of its native support for OpenTelemetry Protocol (OTLP) and OpenTelemetry, providing a cost-effective, high-performance observability platform with features like long-term storage, powerful querying, easy-to-use dashboards, analytics, and alerting. This support combines with Amazon Web Services' Distro for OpenTelemetry, offering a powerful observability solution for AWS workloads and infrastructure. New Relic has been contributing to the Cloud Native Computing Foundation's OpenTelemetry open-source project, which provides instrumentation standards for instrumenting, generating, collecting, and exporting metric, log, and trace data. With GA of native support for OTLP, users can easily ingest their OpenTelemetry data and gain complete visibility into their entire stack in one platform.

New Relic recently hosted two events, "BIPOC In Tech: Writing Your Own Script" and "Allyship in Action," aimed at fostering inclusivity and support for Black, Indigenous, and People of Color (BIPOC) in the tech industry. The first event, led by anti-racist economist Kim Crayton, featured open discussions with notable figures like Arlan Hamilton and Gabby Rivera, highlighting the systemic challenges faced by BIPOC professionals and offering strategies for overcoming these barriers. The event encouraged BIPOC attendees to empower themselves by writing their own narratives and emphasized the importance of psychological safety in workplaces. The second event, led by Simma Lieberman and Fresh "Lev" White, focused on enhancing allyship by encouraging allies to take proactive actions to support BIPOC colleagues. Both events underscored the significance of genuine allyship and leveraging privilege for positive change, with the hope of inspiring attendees to foster a more inclusive and supportive tech community.

New Relic has announced the general availability of its native support for the OpenTelemetry Protocol (OTLP), offering full support for Trace data and early access for Metrics and Logs. This development allows users to leverage New Relic's high-performance observability platform with features like long-term storage, powerful querying, and intelligent analytics. By integrating with Amazon Web Services' Distro for OpenTelemetry, users can enhance the observability of AWS workloads. OpenTelemetry serves as the standard for instrumenting, generating, collecting, and exporting metric, log, and trace data, enabling users to better understand and troubleshoot application performance. The platform supports stable specifications for Trace data and evolving ones for Metric and Log data, with version 1.0 SDKs available for several programming languages. New Relic's native support simplifies the process of ingesting OpenTelemetry data, providing comprehensive visibility into the entire technology stack. Users can easily configure the OTLP exporter to send telemetry data to New Relic, either by using an OpenTelemetry SDK or through AWS instructions with Docker Compose.

The increasing reliance on digital experiences has led to a growing demand for observability solutions among customers, employees, partners, and suppliers. A survey of over 1,300 software engineers, developers, IT leaders, and executives revealed that modern observability is crucial for businesses, with 90% of respondents believing it's important and strategic to their business and role. Observability empowers software engineers and developers with a data-driven approach across the entire software lifecycle, enabling better digital experiences, faster deployment, and greater cost effectiveness. However, despite its benefits, only 50% of organizations are currently implementing observability, and those who are leveraging it often struggle with tool toggling between multiple tools, hindering success. The survey provides insights into modern observability, best practices, and emerging trends, offering a path forward for organizations to chart their own path to modern observability and reap its benefits.

Monitoring website performance is crucial for developers, especially those using the Jamstack architecture. The average website loading time of 3 seconds or more can lead to user abandonment. Amazon's findings show that every 100ms of latency costs 1% in sales. To measure performance, developers need tools such as Netlify, Eleventy.js, and the browser monitoring agent. The New Relic One browser monitoring agent measures webpage build times and reports back to the dashboard in real-time, providing information on code causing delays. By setting up a web application with these tools, developers can monitor their website's performance and detect issues before they arise. This includes measuring organic user interactions, generating simulated traffic using synthetic monitoring, and detecting client-side JavaScript errors. With this data, developers can fix problems right away, leading to improved website conversion rates and happier customers.

Amid the growing reliance on digital experiences, the importance of observability has become increasingly apparent, as highlighted in a survey by New Relic involving over 1,300 software engineers, developers, IT leaders, and executives. The survey results, detailed in the 2021 Observability Forecast, reveal that 90% of respondents view observability as crucial to their business, with a significant number expecting to increase their observability budgets. Despite its benefits, such as enhancing digital transformation, improving customer experiences, and boosting operational efficiency, only half of the surveyed organizations are actively implementing observability. This gap indicates a substantial opportunity for organizations to adopt a unified data-driven approach that consolidates telemetry data into a single platform, thereby overcoming the inefficiencies of using multiple tools. New Relic advocates for this modern observability model, which empowers engineers to focus on impactful coding, supporting business success and growth.

Monitoring site performance is crucial for developers using any architecture, including Jamstack, which is favored for its security, performance, and scalability features. A website's load time is critical, as it can significantly affect user retention and sales, exemplified by Amazon's finding that 100ms of latency costs 1% in sales. The blog post offers a detailed guide on how to monitor and improve Jamstack application performance using tools like Netlify for deployment automation and New Relic for browser monitoring. The tutorial includes steps to deploy an application on Netlify, install a browser monitoring agent to measure load times, and detect client-side JavaScript errors, ultimately aiming to enhance the user experience by identifying and resolving issues before they impact users. By leveraging such tools, developers can improve site performance, thereby increasing customer satisfaction and conversion rates, with additional resources available for optimizing build times and monitoring frontend performance.

New Relic One Full Stack Observability provides an integrated error-tracking solution called Errors Inbox, which groups similar errors together for easier analysis and triaging. This allows developers to quickly resolve errors without switching between multiple apps or losing context. The solution can be integrated with Slack, enabling teams to communicate and collaborate using the tools they already use. With Errors Inbox, developers can triage errors directly in the APM UI, view stack traces and logs in context, and automatically find workloads that include specific services or applications. By integrating Errors Inbox with Slack, teams can receive notifications and take action on errors without leaving their workflow.

Error-tracking software, such as New Relic One's Errors Inbox, assists developers in resolving code errors more efficiently by grouping similar errors for easier analysis and triaging. Despite the challenge of standalone error-tracking tools that require switching between apps, Errors Inbox integrates with existing tools like Slack, allowing teams to collaborate and address errors without losing context. Errors Inbox, part of New Relic One's Full Stack Observability, streamlines the process by displaying errors on a single screen, enabling users to review and assign errors directly from the interface. The integration with Slack allows notifications and error details to be channeled appropriately, enhancing team collaboration on error resolution. This tool supports best practices like setting up specific error inboxes for different teams and environments, ensuring effective monitoring and quick action during deployments.

New Relic has released two innovative features: Network Performance Monitoring and a new Slack integration for Errors Inbox. Network Performance Monitoring allows users to quickly eliminate network blame by correlating and analyzing telemetry data, providing context for faster resolution. This feature enables users to analyze and understand their entire stack's performance, gather data in a single platform, and identify network anomalies. The second release, New Relic Errors Inbox with Slack integration, provides proactive alerts and collaboration tools for error detection and triage, allowing users to take action on errors even faster.

Recent innovations from New Relic include Network Performance Monitoring and a Slack integration for Errors Inbox, both designed to enhance the efficiency of incident response and system performance analysis. Network Performance Monitoring, now part of New Relic One, allows for a comprehensive correlation of telemetry data across applications, infrastructure, and network layers, helping to quickly identify whether network issues are contributing to system problems. It supports the analysis of SNMP and network flow data, providing a holistic view to eliminate blind spots and facilitate faster resolution. Meanwhile, the Slack integration with Errors Inbox consolidates error data to streamline detection and triage, enabling collaborative debugging by sending error details directly to relevant channels. Users can break down errors by attributes like username or device for root cause analysis, and these tools are readily available to New Relic Full-Stack Observability customers, with easy setup instructions provided.

Successful companies use data to empower their software engineers and measure their impact, providing insights on optimizing application performance, building new features, and preventing service outages. Data-driven engineering is the practice of using telemetry collected from engineering tools and platforms to optimize software development teams' work, leading to direct benefits for teams, customers, and bottom lines. To implement data-driven engineering, companies should collect relevant data, measure its impact, and apply it to inform business decisions, ensuring that resources are allocated effectively and teams are working on projects with the desired effect on end-users. A data-driven approach can help organizations grow at a rate of over 30% annually, but requires a well-defined process and an engineer-centric pricing model for observability platforms. By empowering their teams with data-driven engineering, companies can see how their work positively affects critical business drivers and their company's bottom line.

Data-driven engineering involves using telemetry data such as metrics, events, logs, and traces to empower software engineers to optimize application performance, build new features, and prevent potential issues, ultimately benefiting the company’s bottom line. This approach emphasizes making business decisions based on concrete insights rather than incomplete information or intuition, and ensures that all engineers can see and understand data to optimize performance. Implementing data-driven engineering requires answering critical questions about project selection, resource allocation, and customer impact, and involves defining specific key performance indicators (KPIs) for engineering teams. Successful implementation requires collecting and integrating data from various sources, fostering a culture of data-driven decisions across the organization, and selecting an observability platform with a consumption-based pricing model to avoid compromising on data coverage. This empowers engineering teams to enhance their contribution to business goals, ensuring applications and websites are built on robust software foundations.

Structured logging in Python applications can transform logs from a cryptic data dump to a rich, searchable, and actionable source of insight, enabling faster and more accurate automated processing and analysis. By structuring logs consistently, developers can extract specific pieces of information without needing to parse arbitrary text strings, making troubleshooting easier and more efficient. The `structlog` library provides a robust and flexible way to implement structured logging in Python applications, with built-in processors that add timestamps, format log entries, filter logs, or redirect logs to different targets. By combining structured logging with a powerful query language like NRQL, developers can analyze their application's behavior and usage patterns more effectively, leading to more informed decision-making and efficient problem-solving. With the right implementation of structured logging, teams can maximize their ROI by gaining deeper insights into user behavior, identifying errors more easily, analyzing performance metrics, and optimizing log management.

New Relic is hosting BIPOC In Tech, a free event for Black, Indigenous, and People of Color (BIPOC) in the engineering and tech community to build community, share insights, and advance together. The event aims to create a stage to spotlight powerful voices in the BIPOC community, support engagement, and uplift each other. It includes sessions such as "Writing Your Own Script" and "Allyship in Action," focusing on advancing psychological safety, allyship, and co-conspirators for change among BIPOC professionals and allies. The event is reserved for BIPOC engineers, developers, and technical practitioners, and takes place virtually on September 15, 2021.

New Relic has adopted a usage-based pricing model for its unified platform, providing customers with more value for their money. This approach allows users to pay only for what they use, eliminating the need for shelfware and overage penalties. The company's pricing is comparable to that of AWS CloudWatch and Datadog, but offers more value for the cost. New Relic's usage-based pricing model has resonated with customers such as Chegg and ZenHub, who have seen significant benefits in terms of cost savings and increased data visibility. The company's innovative approach to pricing has also led to the development of new features and capabilities at no additional cost to users. With its usage-based pricing model, New Relic is changing the economics of observability and empowering companies to leverage all available telemetry at a dramatically lower cost than before.

New Relic is hosting "BIPOC In Tech," a free virtual event aimed at supporting and connecting Black, Indigenous, and People of Color (BIPOC) in the engineering and tech community. Scheduled for September 15, 2021, the event seeks to foster an inclusive environment where BIPOC engineers, developers, and technical practitioners, as well as students aspiring to these careers, can share insights and advance together. The event will spotlight diverse experiences within the BIPOC community, including gender diversity and other intersectional identities, with sessions led by Kim Crayton and featuring speakers like Arlan Hamilton and Gabby Rivera. Additionally, a session titled "Allyship in Action" will be held for allies wishing to deepen their support for the BIPOC community. The event emphasizes collaboration to ensure access, inclusion, and support for BIPOC professionals in the tech industry.

AWS Lambda provides a managed serverless computing service that allows developers to run applications without managing servers. However, AWS itself doesn't handle code quality and configuration issues, so monitoring tools like New Relic are necessary for troubleshooting problems with Lambda-powered applications. New Relic offers serverless monitoring of Lambda code, providing visibility into system performance, errors, and code-level instrumentation. It also provides synthetic monitoring, which simulates user flows in production to test the performance of AWS Lambda functions. With New Relic's serverless and synthetics offerings, developers can monitor their AWS Lambdas from both inside and outside the application, setting up alerts for issues and getting detailed insights into system performance.

The log monitoring landscape for cloud-native architectures is complex due to the diversity of microservices, container technologies, and open source components, making it necessary to rethink traditional strategies for aggregating, analyzing, and storing application logs. The main challenges with log monitoring in cloud-native environments include scale, ephemeral storage, log variety, and vendor lock-in, which can limit a team's ability to respond to issues effectively or become locked into a specific vendor's proprietary solution. To overcome these challenges, implementing a centralized log management solution using open standards, embracing the latest tracing and logging technologies, adopting open standards for application logs, and excluding unnecessary data from logs are key best practices that help define an effective strategy for cloud-native applications.

Prometheus is an open-source monitoring solution that collects metrics data and stores it in a time series database, providing efficient, scalable, and flexible monitoring practices for organizations. It uses PromQL, a powerful query language, to slice and dice collected time-series data, allowing flexibility in monitoring diverse systems and services. Prometheus follows a pull-based model for data collection and retrieval, storing collected metrics in a time-series database, and utilizing discovery mechanisms to ensure that new instances are automatically detected and monitored without manual intervention. The platform offers features such as multi-dimensional data, PromQL, alerting rules, data visualization through integrations, scalability and federation, making it a popular choice for monitoring modern cloud-native architectures. Prometheus is suitable for monitoring almost any part of an application, including microservices, frontend, backend, servers, hardware, and infrastructure, providing users with the tools needed to gain deep insights into their systems' behavior and performance.

Managing Kubernetes clusters can become overwhelming, but finding the right monitoring tool can help manage clusters and optimize performance. To monitor a large number of short-lived containers, Kubernetes has built-in tools and APIs that provide visibility into application performance. Four key components to monitor are infrastructure (worker nodes), containers, applications, and the Kubernetes cluster (control plane). Resource contention, inefficient networking, slow storage access, improper pod design, ineffective Horizontal Pod Autoscaling (HPA), cluster overhead, and best practices such as defining resource limits, using optimized container images, and deploying clusters closer to users can impact performance. Gathering data on CPU and memory usage for every pod in the Kubernetes cluster is essential for setting good resource limits. Optimizing container images by having a single purpose, being lightweight, providing endpoints for readiness and health checks, and employing multi-step builds can improve deployment efficiency. Deploying clusters near users reduces latency and enhances the user experience. Implementing comprehensive monitoring and logging solutions, using HPA to automatically adjust pod replicas, and addressing common performance issues such as resource contention, inefficient networking, slow storage access, improper pod design, ineffective HPA, and cluster overhead are crucial for maintaining a reliable and efficient Kubernetes system.

Application performance monitoring (APM) allows you to track key metrics and events in your software, giving insights into page load speed, performance bottlenecks, service outages, and errors. A modern APM solution enables proactive issue resolution by minimizing the analysis loop, lowering security risks, and benefiting both end users and your bottom line. To implement an effective APM practice, start small with a single service to minimize risk, then gradually expand coverage as needed. Instrumentation is key, using guided installations or custom instrumentation to collect telemetry data. Set up dashboards to monitor application performance, customize metrics, and create alerts for critical thresholds. Foster collaboration between teams by setting up shared access roles and implementing best practices such as standardizing naming conventions, tagging data, combining APM with CI/CD, documenting workflows, and reducing context switching between tools. By following these steps, you can optimize your APM practice to drive wide adoption, improve system health, and reduce mean time to detect (MTTD) and mean time to resolution (MTTR).

**Log management is a critical process for handling log data, including generating, aggregating, storing, analyzing, archiving, and disposing of logs. It's essential for modern businesses to ensure security, compliance, performance optimization, incident detection and response, root cause analysis, and data-driven decision-making. Log management involves the entire logging lifecycle, from creation to archival or deletion, and requires a comprehensive solution with features such as flexible instrumentation, compatible log forwarding, powerful querying, secure data storage, and indexing for efficient querying and analysis. Successful log management enables businesses to reduce context switching, find and fix problems faster, instantly search logs for needed data, visualize all data in a single place, and optimize resource efficiency. It's crucial to understand the basics of log management to make informed decisions about an effective strategy.

Infrastructure monitoring is a critical component of IT management that ensures optimal performance and reliability of hardware and software resources supporting an organization's IT environment. It provides real-time insights into the entire stack, enabling proactive problem detection, capacity planning, compliance, and post-deployment feedback. The benefits of implementing a robust infrastructure monitoring system include improved performance and reliability, cost savings, scalability, future-proofing, and enhanced observability. An effective infrastructure monitoring tool should provide comprehensive monitoring capabilities, supports a wide range of technologies, offers real-time monitoring with customizable alert thresholds, collects historical data for trend analysis, and has transparent pricing. New Relic is a popular choice for infrastructure monitoring due to its consumption-based pricing model, seamless collaboration across teams, and ability to break down data silos for rapid remediation.

MLOps is a set of practices that streamlines the lifecycle of machine learning models from development and testing to deployment and monitoring in production environments. It bridges the gap between data science and operations teams, addressing specific ML model management challenges. MLOps provides tools for monitoring and observing model performance, increasing collaboration and enabling continuous processes of development, testing, and operational monitoring. The practice encompasses various components such as data management, model training, deployment, serving, monitoring and logging, CI/CD, infrastructure management, and collaboration tools. MLOps aims to deliver faster development cycles, reliable systems, improved collaboration, accurate models in production, automated workflows, reproducible workflows, and lower operational costs. It differs from DevOps but shares similar goals, such as rapid innovation, scalable systems, and reliable performance. MLOps is essential for companies with machine learning models, unlocking revenue sources, saving time, and cutting costs through efficient workflows. The practice involves best practices like defining project scope, choosing ML tools wisely, automating testing, striving for continuous integration and delivery, applying data version control, implementing robust security measures, and continuous monitoring and logging. New Relic supports MLOps with features such as ML model performance monitoring, observability, and integrations with various MLOps tools, enabling teams to incorporate MLOps best practices into their workflow.

Java garbage collection is an automated process that deletes code no longer needed or used, freeing up memory space and making coding easier for developers. It occurs most frequently in the young generation (eden and survivor spaces) due to many new objects being short-lived, but can also occur in the old generation (tenured space) where long-lived objects are stored. The garbage collection process uses a mark-and-sweep algorithm, which scans different parts of the heap looking for unused objects and removes them to free up memory. While automatic garbage collection offers several benefits such as preventing memory leaks and increasing productivity, it can also lead to performance issues if not optimized properly. Developers can optimize garbage collection by choosing the right collector, monitoring logs, optimizing heap size, tuning parameters, minimizing object creation, and using parallelism and concurrency. Monitoring Java application performance with tools like New Relic can help detect and triage issues related to garbage collection and improve overall performance.

Service levels describe services provided to users within a given period of time in measurable terms, with SLOs (service level objectives) being the goals set for availability expected out of a system, SLIs (service level indicators) being key measurements and metrics to determine the availability of a system, and SLAs (service level agreements) being legal contracts that explain what is agreed upon and what happens if systems don’t meet SLOs. Setting the right SLOs is crucial for improving service reliability and creating an incredible customer experience by understanding user expectations and needs, analyzing historical performance, and defining specific, measurable indicators such as latency, error rate, or uptime. Consistently not meeting SLOs may indicate underlying issues in the service, requiring root cause analysis and improvement efforts. Balancing between setting aggressive SLOs and realistic ones involves understanding user expectations and technical capabilities, involving stakeholders from both business and technical sides. SLIs measure real-time user experience and represent a proportion of successful outputs for a level of service, expressed as a percentage, with examples including availability/uptime, latency, throughput, error rate, saturation, coverage, freshness, capacity, and system boundaries. Service levels come into play to help SRE teams identify critical components of their applications and infrastructure, requiring accurate, customized SLIs and SLOs based on historical system performance to set goals around the performance of a system. Service level management ensures that processes and operational agreements for services provided to customers are appropriate, including monitoring and reporting on service levels, setting and adjusting SLOs, determining SLIs, making sure SLAs are met, and holding customer reviews. Implementing good practices for SLIs, SLOs, and SLAs benefits teams with easy setup, defining reliability across teams, iterating and improving, standardizing reliability, and getting started with New Relic's service level management capabilities.

Amazon Web Services (AWS) has announced the general availability of Amazon Managed Service for Grafana (AMG), a managed service that complements its existing Amazon Managed Service for Prometheus (AMP). Both services aim to simplify infrastructure monitoring and visualization using Prometheus and Grafana without requiring operational management. With these services, users can leverage pre-built dashboards and instrumentation, reducing the need for manual management and scaling of their Prometheus servers and Grafana instances. AWS customers with investments in Prometheus and Grafana can now benefit from a fully-managed, elastic platform that combines data from multiple sources for observability across their entire estate. The introduction of AMG is seen as an exciting development for users seeking to bring additional value to these managed open source solutions.

Amazon Web Services (AWS) has announced the general availability of Amazon Managed Service for Grafana (AMG) and the preview of Amazon Managed Service for Prometheus (AMP), both designed to facilitate infrastructure monitoring and visualization without the operational burdens of management. These services complement New Relic's previous integration of Prometheus and Grafana into their Telemetry Data Platform, allowing users to leverage pre-built Grafana dashboards and built-in metrics instrumentation on a scalable, fully-managed platform. New Relic's platform enables the consolidation of metric data from Prometheus servers with other observability data, enhancing troubleshooting capabilities through powerful AIOps features. AMP and AMG are particularly beneficial for users seeking to offload the management and scaling of Prometheus and Grafana, offering automated scaling and integration with AWS security services for AMP, and the ability to utilize Grafana’s open-source visualization features without the need for server provisioning or software updates for AMG. Both AWS and New Relic customers can benefit from these services, especially when integrating New Relic as a Prometheus data source to achieve a comprehensive observability across their infrastructures.

This tutorial helps developers improve efficiency with Gatsby builds by using OpenTelemetry, an open-source project that provides performance tracing and monitoring capabilities. By leveraging performance tracing built into Gatsby, developers can collect telemetry data and identify inefficiencies in their build process. The tutorial shows how to set up the OpenTelemetry Collector, configure Jaeger, and analyze traces in New Relic One to gain valuable insights into build times and code changes. With this setup, developers can streamline their development time spent waiting for site builds and improve productivity. By using OpenTelemetry, developers can also measure traffic, track errors, and optimize build times for Gatsby applications.

Building large Jamstack applications, particularly with Gatsby, can be complex and time-consuming, as illustrated by New Relic's experience with their substantial docs site. To enhance build efficiency and address lengthy build times, New Relic leverages OpenTelemetry, an open-source project for performance tracing, to monitor and analyze build processes. Gatsby, which integrates React, GraphQL, and webpack, allows pre-built files to be served over a CDN, improving performance, security, and scalability. By using telemetry data and tools like Jaeger for distributed tracing, New Relic can identify bottlenecks in API and plugin performance, enabling more efficient code changes and build processes. This approach not only optimizes build times but also enhances the productivity of the development team, which includes numerous contributors. The article provides a detailed tutorial on setting up an environment to use these tools effectively, emphasizing the importance of streamlining the development process for a better user experience and inviting further exploration through additional tutorials.