AIOps adds machine learning and artificial intelligence to your troubleshooting toolbox so you can detect, diagnose, and resolve incidents faster. This white paper shows you how to build a strong AIOps practice that surfaces the data you need, making it easier and faster to build better software.
Reduce MTTR the right way! Don’t simply measure how quickly your teams can fix an issue. Instead, track progress all the way to resolution. Our nine-step guide shows you how to build an incident response strategy designed to fix problems quickly—and prevent them from happening again.
As software development moves faster and faster, alerting becomes an indispensable practice, and more so for modern DevOps teams. But how do you ensure that alerts are delivered in a timely manner while preventing as many false positives and negatives as possible? (Because we all know: alert fatigue is a real thing.) In this guide, you’ll learn how to create and manage an effective alerting strategy for your technology stack. With alerting design principles and other useful tips.
Kubernetes and containerization offer improved time-to-market, but have complicated infrastructure monitoring and proven difficult to manage at scale. When a problem arises with container-based apps, you need to be able to drill into the ever-changing interdependencies between app, infrastructure, logs, and end-user experience. Learn how to gain that context and ensure system health.
It seems that everyone’s talking about “cloud native” — distributed systems, microservices, containers, serverless computing, and other emerging technologies and architectures. It’s clear that we’ve quickly evolved from the cloud age to the cloud native age. Are you ready and able to succeed in this new era? Download our free eBook to get your fundamental questions answered, so you can optimize your cloud native approach.
Microservices. Kubernetes. Service mesh. Serverless. These “cloud native” technologies are becoming an essential part of digital transformation efforts for modern enterprise IT. It’s clear this trend is following in the steps of Agile and DevOps—and could penetrate 50% of organizations within the next few years. Download a free copy of this 451 Research report and discover the transformative reality of cloud native technologies.
Like any project or task, without the proper tools, data labeling vendors simply can’t do a good job. Learn tips for evaluating vendor toolsets and our approach to tooling in the Outsourcer's Guide to Quality.
Hivemind data scientists tested CloudFactory’s managed workforce against a leading crowdsourcing platform’s anonymous workers. Completing a series of tasks, from basic to complicated, they determined which team delivered the highest-quality structured datasets and costs associated.
When you’re creating high-performing machine learning models, you need quality, labeled data...and lots of it. Getting it can be a challenge. A growing number of innovators are outsourcing data labeling operations so their teams can focus on strategy and innovation. Choosing a data labeling partner is an important decision that can affect your model performance and speed to market. But how do you choose the right data labeling vendor? Find all of the answers here.
Now more than ever, CIOs and COOs must maximize long-term success throughout the life of AI projects. One of the ways of doing that is by reducing risk.
The right workforce gives you the flexibility to respond to changes in the market, products or your business. Find out which workforce is ideal for scaling and accelerating your AI training data labeling.
Discover how 9 industry leading companies are employing data annotation solutions to accelerate their machine learning projects and deliver the true promise of AI.
Leverage our digital identity cloud API Personator to protect against fraud, verify customer data and ensure compliance at point-of-entry. Cross verify all contact information – address, name, email and phone – and SSN and ID documentation with Personator. Try it Free!
This white paper tells the story of GE Aviation’s data revolution. Discover the history of their data teams, the technological and organizational setup that enabled transformation, use cases, how they handle data education, and more.
This white paper provides a deep dive into how AutoML came to be, the difference between it and Augmented Analytics, and how they both have brought about the rise of the citizen data scientist.
We surveyed more than 50 Chief Data Officers (CDOs) worldwide to uncover how they overcome their data and organizational challenges. This report explores the data landscape and maps the Data Revolution. Learn more.
Whether you’re in the process of building a data team from the ground up or looking to scale a data team that already exists, this white paper will detail how to address, avoid, and fix challenges. Learn more.
Use this guide to learn how to find the common ground between data and IT teams, empowering them to work together to operationalize data projects - quickly. Get the details behind the ten recommendations to go from data project development to operationalizion. Learn more.
Read this In-Depth Report to find out more about the prominent role Artificial Intelligence (AI) is taking in the healthcare industry including medical records management, predictive analytics, early diagnosis, and treatment design. Learn more.