Dynatrace

NOVEMBER 15, 2021

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes.

Analytics 208

Analytics Monitoring Artificial Intelligence DevOps 208

Dynatrace

FEBRUARY 16, 2023

That’s why, in part, major cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform are discussing cloud optimization. Traditional cloud monitoring methods can no longer scale to meet organizations’ demands, as multicloud architectures continue to expand.

Artificial Intelligence 193

Artificial Intelligence Analytics Cloud Innovation 193

Dynatrace

JANUARY 26, 2021

VMware commercialized the idea of virtual machines, and cloud providers embraced the same concept with services like Amazon EC2, Google Compute, and Azure virtual machines. You’ll benefit from serverless computing when: Authenticating users (for example, Okta , Azure Active Directory ). Monitoring serverless applications.

Serverless 243

Serverless Efficiency Lambda Azure 243

Dynatrace

FEBRUARY 10, 2022

Dynatrace provides developers, Security, DevOps, and SRE teams with integrated, end-to-end observability and application security platform that provides advanced AIOps capabilities to support organizations from planning and execution to the monitoring of pre-production and production systems. Atlassian Bitbucket. JFrog Pipelines. ChaosNative.

Artificial Intelligence 229

Artificial Intelligence DevOps Azure Cloud 229

Dynatrace

OCTOBER 30, 2023

Consolidate log management tools into a unified observability platform As businesses increasingly shift toward software-centric models, the number of specialized IT monitoring tools to manage cloud environments has proliferated. The first best practice is to consolidate log management with application monitoring in a single platform.

Best Practices 243

Best Practices Analytics Artificial Intelligence Infrastructure 243

Dynatrace

OCTOBER 4, 2022

This approach enables organizations to use this data to build artificial intelligence (AI) and machine learning models from large volumes of disparate data sets. Data lakehouses take advantage of low-cost object stores like AWS S3 or Microsoft Azure Blob Storage to store and manage data cost-effectively. Data management.

Artificial Intelligence 208

Artificial Intelligence Analytics Storage Government 208

Scalegrid

FEBRUARY 8, 2024

Amazon S3 and Microsoft Azure Blob Storage leverage distributed storage solutions. By employing distributed storage techniques, continuous monitoring becomes possible — this proves crucial for maintaining equipment that helps prevent hazardous incidents before they occur and contributes significantly to life preservation initiatives.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

OCTOBER 6, 2023

Grail: Enterprise-ready data lakehouse Grail, the Dynatrace causational data lakehouse, was explicitly designed for observability and security data, with artificial intelligence integrated into its foundation. Take the first step now Many organizations have found immediate value in working with logs in Grail.

Artificial Intelligence 261

Artificial Intelligence Analytics Metrics Storage 261

Dynatrace

NOVEMBER 22, 2021

Having recently achieved AWS Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category for its use of the AWS platform, Dynatrace has demonstrated success building AI-powered solutions on AWS. This tutorial demonstrates the role of full-stack monitoring in modern delivery pipelines.

AWS 228

AWS Lambda Artificial Intelligence Serverless 228

Dynatrace

OCTOBER 1, 2021

Although some people may think of observability as a buzzword for sophisticated application performance monitoring (APM) , there are a few key distinctions to keep in mind when comparing observability and monitoring. What is the difference between monitoring and observability? Is observability really monitoring by another name?

Metrics 363

Metrics Open Source Monitoring Infrastructure 363

Scalegrid

MARCH 14, 2024

Workloads from web content, big data analytics, and artificial intelligence stand out as particularly well-suited for hybrid cloud infrastructure owing to their fluctuating computational needs and scalability demands.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Dynatrace

JUNE 1, 2021

Application performance monitoring (APM) is the practice of tracking key software application performance metrics using monitoring software and telemetry data. Mobile apps, websites, and business applications are typical use cases for monitoring. APM can be referred to as: Application performance monitoring.

Monitoring 148

Monitoring Mobile Social Media Artificial Intelligence 148

Scalegrid

JANUARY 8, 2024

These tools play a crucial role in allowing IT teams to efficiently monitor and manage multiple cloud platforms using one unified interface, ultimately improving efficiency and productivity. Multi-Cloud Management Tools Navigating through the complexities of multi-cloud environments requires the use of effective multi-cloud management tools.

Strategy 147

Strategy Cloud Scalability Artificial Intelligence 147

Scalegrid

JANUARY 12, 2024

Utilizing cloud platforms is especially useful in areas like machine learning and artificial intelligence research. To this vital function is workload automation which optimizes scheduling, execution, and monitoring processes for each individual task or process within cloud-based workflows.

Cloud 130

Cloud Virtualization Storage Efficiency 130

O'Reilly

NOVEMBER 28, 2023

While the source code and weights for the LLaMA models are available online, the LLaMA models don’t yet have a public API backed by Meta—although there appear to be several APIs developed by third parties, and both Google Cloud and Microsoft Azure offer Llama 2 as a service. The next most needed skill is operations for AI and ML (54%).

Open Source 107

Open Source Artificial Intelligence Google Infrastructure 107