New Relic is extending its observability experience to provide a new offering for artificial intelligence (AI) and machine learning (ML) teams to break down visibility silos.
This brand new offering provides AI/ML and DevOps teams one place to monitor and visualize critical signals like recall, precision, and model accuracy alongside their apps and infrastructure.
AI/ML engineers and data scientists can now send model performance telemetry data into New Relic One and—with integrations to leading machine learning operations (MLOps) platforms—proactively monitor ML model issues in production. You can empower your data teams with full visibility, with custom dashboards and visualizations that can show you the performance of your ML investments in action.
AI and ML models are based on both code and the underlying data. Because the real world is constantly changing, models developed on static data can become irrelevant or “drift” over time, becoming less accurate. Monitoring the performance of an ML model in production is essential to continue to deliver relevant customer experiences.
By using New Relic One for your ML model performance monitoring, your development and data science teams can:
- Bring your own ML data or integrate with data science platforms and monitor ML models and interdependencies with the rest of the application components, including infrastructure, to solve problems faster.
- Create custom dashboards to gain trust and insights for more accurate ML models.
- Apply predictive alerts to ML models from New Relic Alerts and Applied Intelligence to detect unusual changes and unknowns early before they impact customers.
- Review ML model telemetry data for critical signals to maintain high-performing models.
- Collaborate in a production environment and contextualize alerts, notifications, and incidents before they have an impact on the business.
- Access data that allows you to make data-driven decisions, such as boosting innovation, planning decisions, increasing reliability, and enhancing customer experience.
The Latest
Digital businesses don't invest in monitoring for monitoring's sake. They do it to make the business run better. Every dollar spent on observability — every hour your team spends using monitoring tools or responding to what they reveal — should tie back directly to business outcomes: conversions, revenues, brand equity. If they don't? You might be missing the forest for the trees ...
Every day, companies are missing customer experience (CX) "red flags" because they don't have the tools to observe CX processes or metrics. Even basic errors or defects in automated customer interactions are left undetected for days, weeks or months, leading to widespread customer dissatisfaction. In fact, poor CX and digital technology investments are costing enterprises billions of dollars in lost potential revenue ...
Organizations are moving to microservices and cloud native architectures at an increasing pace. The primary incentive for these transformation projects is typically to increase the agility and velocity of software release and product innovation. These dynamic systems, however, are far more complex to manage and monitor, and they generate far higher data volumes ...
Global IT teams adapted to remote work in 2021, resolving employee tickets 23% faster than the year before as overall resolution time for IT tickets went down by 7 hours, according to the Freshservice Service Management Benchmark Report from Freshworks ...
Once upon a time data lived in the data center. Now data lives everywhere. All this signals the need for a new approach to data management, a next-gen solution ...
Findings from the 2022 State of Edge Messaging Report from Ably and Coleman Parkes Research show that most organizations (65%) that have built edge messaging capabilities in house have experienced an outage or significant downtime in the last 12-18 months. Most of the current in-house real-time messaging services aren't cutting it ...
Networks need to be up and running for businesses to continue operating and sustaining customer-facing services. Streamlining and automating network administration tasks enable routine business processes to continue without disruption, eliminating any network downtime caused by human error or other system flaws ...
Enterprises have had access to various Project and Portfolio Management (PPM) tools for quite a few years, to guide in their project selection and execution lifecycle. Yet, in spite of the digital evolution of management software, many organizations still fail to construct an effective PPM plan or utilize cutting-edge management tools ...
It has become increasingly difficult for DevOps and SRE teams to minimize the impact of issues and ensure high-quality end-user experiences. In this blog, I'm going to propose a new approach to support real-time use cases — edge observability — that enables you to detect issues as they occur and resolve them in minutes ...
