Business Software for Amazon Elastic Container Service (Amazon ECS)

A comprehensive enterprise data virtualization solution enables seamless access to a variety of data sources while establishing a robust foundation of datasets and IT-managed data services suitable for virtually any application. The TIBCO® Data Virtualization system, functioning as a contemporary data layer, meets the dynamic demands of organizations with evolving architectures. By eliminating bottlenecks, it fosters consistency and facilitates reuse by providing on-demand access to all data through a unified logical layer that is secure, governed, and accessible to a wide range of users. With immediate availability of all necessary data, organizations can derive actionable insights and respond swiftly in real-time. Users benefit from the ability to effortlessly search for and choose from a self-service directory of virtualized business data, utilizing their preferred analytics tools to achieve desired outcomes. This shift allows them to concentrate more on data analysis rather than on the time-consuming task of data retrieval. Furthermore, the streamlined process enhances productivity and enables teams to make informed decisions quickly and effectively.

Tanzu Observability by Broadcom is an advanced observability solution designed to provide businesses with deep visibility into their cloud-native applications and infrastructure. The platform aggregates metrics, traces, and logs to deliver real-time insights into application performance and operational health. By leveraging AI and machine learning, Tanzu Observability automatically detects anomalies, accelerates root cause analysis, and offers predictive analytics to optimize system performance. With its scalable architecture, the platform supports large deployments, enabling businesses to manage and improve the performance of their digital ecosystems efficiently.

AWS Fargate serves as a serverless compute engine tailored for containerization, compatible with both Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS). By utilizing Fargate, developers can concentrate on crafting their applications without the hassle of server management. This service eliminates the necessity to provision and oversee servers, allowing users to define and pay for resources specific to their applications while enhancing security through built-in application isolation. Fargate intelligently allocates the appropriate amount of compute resources, removing the burden of selecting instances and managing cluster scalability. Users are billed solely for the resources their containers utilize, thus avoiding costs associated with over-provisioning or extra servers. Each task or pod runs in its own kernel, ensuring that they have dedicated isolated computing environments. This architecture not only fosters workload separation but also reinforces overall security, greatly benefiting application integrity. By leveraging Fargate, developers can achieve operational efficiency alongside robust security measures, leading to a more streamlined development process.

Amazon Elastic Kubernetes Service (EKS) is a comprehensive Kubernetes management solution that operates entirely under AWS's management. High-profile clients like Intel, Snap, Intuit, GoDaddy, and Autodesk rely on EKS to host their most critical applications, benefiting from its robust security, dependability, and ability to scale efficiently. EKS stands out as the premier platform for running Kubernetes for multiple reasons. One key advantage is the option to deploy EKS clusters using AWS Fargate, which offers serverless computing tailored for containers. This feature eliminates the need to handle server provisioning and management, allows users to allocate and pay for resources on an application-by-application basis, and enhances security through inherent application isolation. Furthermore, EKS seamlessly integrates with various Amazon services, including CloudWatch, Auto Scaling Groups, IAM, and VPC, ensuring an effortless experience for monitoring, scaling, and load balancing applications. This level of integration simplifies operations, enabling developers to focus more on building their applications rather than managing infrastructure.

Effx offers an effortless approach to managing and navigating your microservices architecture. No matter if your setup consists of just a couple or a vast number of microservices, effx will monitor and assist you, whether you're using a public cloud, an orchestration system, or an on-premises solution. Handling incidents across a collection of microservices can often be complicated. With effx, you gain valuable context that allows you to pinpoint potential causes of outages in real-time effectively. You've made significant investments to be aware of any production disruptions. Our platform enhances your preparedness by evaluating services based on critical attributes that ensure their operational readiness, ultimately empowering your team to respond swiftly and efficiently.

Amazon GameLift is a specialized hosting solution for game servers that facilitates the deployment, management, and scaling of cloud servers tailored for multiplayer gaming experiences. Whether you require a comprehensive managed service or only specific features, GameLift harnesses the capabilities of AWS to ensure optimal latency, reduced player wait times, and significant cost efficiency. Utilizing the extensive AWS global network, GameLift effectively handles game server management. It allows for the seamless matchmaking of players into game sessions while enabling the autoscaling of one, hundreds, or even thousands of instances simultaneously, all without the need for manual scaling adjustments in response to varying player demand. The platform provides essential multiplayer game functionalities, including matchmaking, session directories, player data management, and analytics. Additionally, with AWS, you can access services like AWS Lambda for serverless computing that is both scalable and adaptable, as well as features from Amazon GameLift FlexMatch to enhance your matchmaking processes. Overall, GameLift offers a comprehensive solution that caters to the complex needs of modern multiplayer gaming.

With more than ten years of experience in developing media technology, we have created a range of solutions aimed at elevating your media and content creation efforts. You can maintain control while utilizing your current tools and workflows, connecting with your audience in your envisioned manner. Production companies, editorial facilities, visual effects studios, audio houses, and finishing houses can collaborate with diverse vendors and securely exchange files across teams and various branch locations. Envoi, utilizing IBM Aspera on Cloud, Amazon S3, and Equinix, will automate the processing and delivery of these files through efficient workflows. You can upload files, request metadata, and collaborate with team members, storage options, and distribution partners seamlessly. By establishing ingest and distribution workflows, you can ensure that files are processed and transformed upon arrival, enhancing overall productivity and collaboration in your projects. This innovative approach not only streamlines operations but also fosters greater creativity and efficiency in media production.

Effortlessly store, share, and deploy your containerized software wherever needed. You can push container images to Amazon ECR without the necessity of installing or managing infrastructure, while also retrieving images using any preferred management tool. Securely share and download images via Hypertext Transfer Protocol Secure (HTTPS), featuring built-in encryption and access controls. Enhance the speed of accessing and distributing your images, minimize download times, and boost availability with a robust and scalable architecture. Amazon ECR serves as a fully managed container registry that provides high-performance hosting, enabling you to reliably deploy application images and artifacts across various platforms. Additionally, ensure that your organization's image compliance security needs are met through insights derived from common vulnerabilities and exposures (CVEs) alongside the Common Vulnerability Scoring System (CVSS). Easily publish containerized applications with a single command and seamlessly integrate them into your self-managed environments for a more efficient workflow. This streamlined process enhances both collaboration and productivity across teams.

Designed by engineers specifically for engineers, our Prometheus monitoring solution is incredibly simple to set up, configure, and start transmitting metrics. We manage the scaling of your Prometheus infrastructure, so you can concentrate on your work without any concerns. With our service, your data is stored long-term with triple redundancy, allowing you to leverage insights without the burden of database management. You’ll receive automatic updates and plugins, ensuring your Prometheus and Grafana stack remains current without any additional effort on your part. Everything necessary for effective management of your Prometheus metrics is at your disposal. We prioritize your autonomy, steering clear of vendor lock-in, and you can obtain a complete data export whenever you need it. This approach combines the advantages of an open-source solution with the reliability and security of a SaaS platform. We ensure your data is securely backed up with threefold redundancy and stored safely for a full year. Scale effortlessly, as we take care of all the complexities for you, and rest assured that Prometheus specialists are ready to assist you around the clock. In this way, you can consistently rely on expert support whenever you need it.

Select the tools that best suit your needs, and we will handle everything else. Create an ideal CI/CD stack tailored to your organization's objectives without the worry of vendor lock-in. By eliminating the need for manual scripts and complex toolchain automation, your engineers can concentrate on your main business activities. Our pipeline workflows utilize a declarative approach, allowing you to prioritize essential tasks over the methods used to achieve them, covering aspects such as software builds, security assessments, unit testing, and deployment processes. With the help of Blueprints, you can troubleshoot any issues directly within Opsera, thanks to a detailed console output for each step of your pipeline's execution. Gain a holistic view of your CI/CD journey with extensive software delivery analytics, tracking metrics like Lead Time, Change Failure Rate, Deployment Frequency, and Time to Restore. Additionally, benefit from contextualized logs that facilitate quicker resolutions while enhancing auditing and compliance measures, ensuring that your operations remain efficient and transparent. This streamlined approach not only promotes better productivity but also empowers teams to innovate more freely.

A comprehensive continuous delivery platform designed for various application types across multiple cloud environments, enabling engineers to deploy with increased speed and assurance. This GitOps tool facilitates deployment operations through pull requests on Git, while its deployment pipeline interface clearly illustrates ongoing processes. Each deployment benefits from a dedicated log viewer, providing clarity on individual deployment activities. Users receive real-time updates on the state of applications, along with deployment notifications sent to Slack and webhook endpoints. Insights into delivery performance are readily available, complemented by automated deployment analysis utilizing metrics, logs, and emitted requests. In the event of a failure during analysis or a pipeline stage, the system automatically reverts to the last stable state. Additionally, it promptly identifies configuration drift to alert users and showcase any modifications. A new deployment is automatically initiated upon the occurrence of specified events, such as a new container image being pushed or a Helm chart being published. The platform supports single sign-on and role-based access control, ensuring that credentials remain secure and are not exposed outside the cluster or stored in the control plane. This robust solution not only streamlines the deployment process but also enhances overall operational efficiency.

QueryPie serves as a unified platform that consolidates diverse data sources and security protocols into a single interface. Accelerate your company's journey toward achievement without altering your current data infrastructure. In an era driven by data, effective data governance has become essential. Make certain that you adhere to data governance regulations while simultaneously granting multiple users access to an increasing volume of essential data. Formulate data access policies by incorporating crucial factors such as IP addresses and time of access. You can create various privilege types based on SQL commands categorized into DML, DCL, and DDL, allowing for secure data analysis and modifications. Effortlessly monitor SQL event details and analyze user behavior along with potential security threats by examining logs filtered by permissions. Furthermore, all historical data can be exported for comprehensive reporting, ensuring that you have a clear view of your data usage and security practices. This holistic approach not only enhances oversight but also supports informed decision-making for future strategies.

It enables efficient training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances powered by AWS Trainium. Additionally, for model deployment, it facilitates both high-performance and low-latency inference utilizing AWS Inferentia-based Amazon EC2 Inf1 instances along with AWS Inferentia2-based Amazon EC2 Inf2 instances. With the Neuron SDK, users can leverage widely-used frameworks like TensorFlow and PyTorch to effectively train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal alterations to their code and no reliance on vendor-specific tools. The integration of the AWS Neuron SDK with these frameworks allows for seamless continuation of existing workflows, requiring only minor code adjustments to get started. For those involved in distributed model training, the Neuron SDK also accommodates libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and scalability for various ML tasks. By providing robust support for these frameworks and libraries, it significantly streamlines the process of developing and deploying advanced machine learning solutions.

DROPS is a sophisticated release management solution crafted to enhance, secure, and consolidate the deployment of applications across diverse infrastructures, including data centers, hybrid setups, and multi-cloud environments. It accommodates a variety of platforms, integrates effortlessly with numerous CI/CD pipelines, and provides options for both agent-based and agentless functionalities. Featuring comprehensive release management, automated infrastructure setup, and round-the-clock availability, DROPS is designed to optimize deployment workflows while ensuring dependable and uniform delivery. This tool is versatile enough to handle both legacy systems and contemporary applications, meeting the varied requirements of enterprises. Users can choose between agent-based and agentless operations without the need for any agent installation or upkeep. DROPS is capable of adapting to your specific configuration, and if agents are necessary, they will be provisioned automatically. You can plan and manage your application deployment through a user-friendly web console without requiring any scripting skills, facilitating smooth collaboration among stakeholders and technical teams, thereby enhancing overall project efficiency. Thus, DROPS not only simplifies deployment but also fosters better communication and coordination throughout the development lifecycle.

Amazon's Elastic Compute Cloud (EC2) offers P5 instances that utilize NVIDIA H100 Tensor Core GPUs, alongside P5e and P5en instances featuring NVIDIA H200 Tensor Core GPUs, ensuring unmatched performance for deep learning and high-performance computing tasks. With these advanced instances, you can reduce the time to achieve results by as much as four times compared to earlier GPU-based EC2 offerings, while also cutting ML model training costs by up to 40%. This capability enables faster iteration on solutions, allowing businesses to reach the market more efficiently. P5, P5e, and P5en instances are ideal for training and deploying sophisticated large language models and diffusion models that drive the most intensive generative AI applications, which encompass areas like question-answering, code generation, video and image creation, and speech recognition. Furthermore, these instances can also support large-scale deployment of high-performance computing applications, facilitating advancements in fields such as pharmaceutical discovery, ultimately transforming how research and development are conducted in the industry.

Amazon EC2 Capacity Blocks for Machine Learning allow users to secure accelerated computing instances within Amazon EC2 UltraClusters specifically for their machine learning tasks. This service encompasses a variety of instance types, including Amazon EC2 P5en, P5e, P5, and P4d, which utilize NVIDIA H200, H100, and A100 Tensor Core GPUs, along with Trn2 and Trn1 instances that leverage AWS Trainium. Users can reserve these instances for periods of up to six months, with cluster sizes ranging from a single instance to 64 instances, translating to a maximum of 512 GPUs or 1,024 Trainium chips, thus providing ample flexibility to accommodate diverse machine learning workloads. Additionally, reservations can be arranged as much as eight weeks ahead of time. By operating within Amazon EC2 UltraClusters, Capacity Blocks facilitate low-latency and high-throughput network connectivity, which is essential for efficient distributed training processes. This configuration guarantees reliable access to high-performance computing resources, empowering you to confidently plan your machine learning projects, conduct experiments, develop prototypes, and effectively handle anticipated increases in demand for machine learning applications. Furthermore, this strategic approach not only enhances productivity but also optimizes resource utilization for varying project scales.

Amazon EC2 UltraClusters allow for the scaling of thousands of GPUs or specialized machine learning accelerators like AWS Trainium, granting users immediate access to supercomputing-level performance. This service opens the door to supercomputing for developers involved in machine learning, generative AI, and high-performance computing, all through a straightforward pay-as-you-go pricing structure that eliminates the need for initial setup or ongoing maintenance expenses. Comprising thousands of accelerated EC2 instances placed within a specific AWS Availability Zone, UltraClusters utilize Elastic Fabric Adapter (EFA) networking within a petabit-scale nonblocking network. Such an architecture not only ensures high-performance networking but also facilitates access to Amazon FSx for Lustre, a fully managed shared storage solution based on a high-performance parallel file system that enables swift processing of large datasets with sub-millisecond latency. Furthermore, EC2 UltraClusters enhance scale-out capabilities for distributed machine learning training and tightly integrated HPC tasks, significantly decreasing training durations while maximizing efficiency. This transformative technology is paving the way for groundbreaking advancements in various computational fields.

Amazon EC2 Trn2 instances, equipped with AWS Trainium2 chips, are specifically designed to deliver exceptional performance in the training of generative AI models, such as large language and diffusion models. Users can experience cost savings of up to 50% in training expenses compared to other Amazon EC2 instances. These Trn2 instances can accommodate as many as 16 Trainium2 accelerators, boasting an impressive compute power of up to 3 petaflops using FP16/BF16 and 512 GB of high-bandwidth memory. For enhanced data and model parallelism, they are built with NeuronLink, a high-speed, nonblocking interconnect, and offer a substantial network bandwidth of up to 1600 Gbps via the second-generation Elastic Fabric Adapter (EFAv2). Trn2 instances are part of EC2 UltraClusters, which allow for scaling up to 30,000 interconnected Trainium2 chips within a nonblocking petabit-scale network, achieving a remarkable 6 exaflops of compute capability. Additionally, the AWS Neuron SDK provides seamless integration with widely used machine learning frameworks, including PyTorch and TensorFlow, making these instances a powerful choice for developers and researchers alike. This combination of cutting-edge technology and cost efficiency positions Trn2 instances as a leading option in the realm of high-performance deep learning.

Amazon Elastic File System (Amazon EFS) effortlessly expands and contracts as files are added or deleted, eliminating the need for manual management or provisioning. It allows for the secure and organized sharing of code and other files, enhancing DevOps efficiency and enabling quicker responses to customer input. With Amazon EFS, you can persist and share data from your AWS containers and serverless applications without any management overhead. Its user-friendly scalability provides the performance and reliability essential for machine learning and big data analytics tasks. Additionally, it streamlines persistent storage for contemporary content management system workloads. By utilizing Amazon EFS, you can accelerate the delivery of your products and services to market, ensuring they are reliable and secure while also reducing costs. Notably, you can easily create and configure shared file systems for AWS compute services without the need for provisioning, deployment, patching, or ongoing maintenance. Moreover, it allows you to scale your workloads on-demand, accommodating up to petabytes of storage and gigabytes per second of throughput right from the start, making it an ideal solution for businesses looking to optimize their cloud storage capabilities.

Introducing the ultimate multicloud monitoring solution that offers real-time analytics for diverse environments, previously known as SignalFx. This platform enables monitoring across any environment using a highly scalable streaming architecture. It features open, adaptable data collection and delivers rapid visualizations of services in mere seconds. Designed specifically for dynamic and ephemeral cloud-native environments, it supports various scales including Kubernetes, containers, and serverless architectures. Users can promptly detect, visualize, and address issues as they emerge. It empowers real-time infrastructure performance monitoring at cloud scale through innovative predictive streaming analytics. With over 200 pre-built integrations for various cloud services and ready-to-use dashboards, it facilitates swift visualization of your entire operational stack. Additionally, the system can autodiscover, break down, group, and explore various clouds, services, and systems effortlessly. This comprehensive solution provides a clear understanding of how your infrastructure interacts across multiple services, availability zones, and Kubernetes clusters, enhancing operational efficiency and response times.

StackState's Topology & Relationship-Based Observability platform allows you to manage your dynamic IT environment more effectively. It unifies performance data from existing monitoring tools and creates a single topology. This platform allows you to: 1. 80% Reduced MTTR by identifying the root cause of the problem and alerting the appropriate teams with the correct information. 2. 65% Less Outages: Through real-time unified observation and more planned planning. 3. 3.3.2. 3x faster releases: Developers are given more time to implement the software. Get started today with our free guided demo: https://www.stackstate.com/schedule-a-demo

Deep Learning Containers consist of Docker images that come preloaded and verified with the latest editions of well-known deep learning frameworks. They enable the rapid deployment of tailored machine learning environments, eliminating the need to create and refine these setups from the beginning. You can establish deep learning environments in just a few minutes by utilizing these ready-to-use and thoroughly tested Docker images. Furthermore, you can develop personalized machine learning workflows for tasks such as training, validation, and deployment through seamless integration with services like Amazon SageMaker, Amazon EKS, and Amazon ECS, enhancing efficiency in your projects. This capability streamlines the process, allowing data scientists and developers to focus more on their models rather than environment configuration.