Apolo Integrations

Grafana Labs delivers the leading AI-powered observability platform, built around Grafana—the most widely adopted open source technology for dashboards and visualization. Recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Observability Platforms, Grafana Labs supports more than 25 million users and thousands of organizations worldwide, from startups to Fortune 500 enterprises. Grafana Cloud is the open observability cloud, designed to help engineering teams observe everything and solve anything. Built on open source, open standards, and open ecosystems, it unifies metrics, logs, traces, and profiles in a single platform for full-stack visibility across applications, infrastructure, and digital experiences. At the core is the open-source LGTM stack: Grafana for dashboards and visualization, Mimir for metrics, Loki for logs, and Tempo for distributed tracing. Native OpenTelemetry and Prometheus support allow teams to ingest telemetry from virtually any environment, while hundreds of integrations connect existing tools and data sources without costly rip-and-replace migrations. Grafana Cloud combines powerful analytics with AI-driven observability. Grafana Assistant helps engineers investigate issues, explore telemetry, and troubleshoot faster. Adaptive Telemetry identifies the data that matters most and aggregates the rest, helping organizations reduce telemetry costs while preserving valuable insights . With solutions for Kubernetes monitoring, application observability, digital experience monitoring, incident response, synthetic monitoring, and performance testing, Grafana Cloud delivers a complete observability platform that scales with your business.

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

Visual Studio Code is a highly extensible AI-powered code editor built for developers who demand flexibility and performance. It combines intelligent coding assistance, modern debugging tools, and collaboration features in one lightweight package. With Agent Mode, VS Code reads your codebase, runs terminal commands, and edits across files automatically until tasks are complete. Its Next Edit Suggestions feature predicts and completes your next move as you type, enhancing speed and code accuracy. The Model Context Protocol (MCP) enables developers to connect their favorite AI models—from OpenAI, Anthropic, Azure, or Google—and extend functionality through custom servers. Developers can work in any language, from JavaScript and Python to C#, Java, and Go, while leveraging over 75,000 extensions for added productivity. Seamless integration with GitHub Codespaces, cloud storage, and CI/CD tools allows teams to code, collaborate, and deploy anywhere. Open-source at its core, VS Code empowers both individuals and enterprises to innovate without limits.

Git is a powerful and freely available distributed version control system that is built to manage projects of any size swiftly and effectively. Its user-friendly nature and minimal resource requirements contribute to its remarkable speed. Git surpasses traditional source control management tools such as Subversion, CVS, Perforce, and ClearCase by offering advantages like inexpensive local branching, user-friendly staging areas, and diverse workflow options. Additionally, you can interact with configurations through this command, where the name represents the section and the key separated by a dot, while the value is appropriately escaped. This versatility in handling version control makes Git an essential tool for developers and teams alike.

The Jupyter Notebook is a web-based open-source tool that enables users to create and distribute documents featuring live code, visualizations, equations, and written explanations. Its applications are diverse and encompass tasks such as data cleaning and transformation, statistical modeling, numerical simulations, data visualization, machine learning, among others, showcasing its versatility in various fields. Additionally, it serves as an excellent platform for collaboration and sharing insights within the data science community.

RabbitMQ is a lightweight solution that can be effortlessly deployed both on-premises and in cloud environments. It is compatible with various messaging protocols, making it versatile for different use cases. Furthermore, RabbitMQ can be configured in distributed and federated setups, which cater to demanding scalability and high availability needs. With a vast user base, it stands out as one of the leading open-source message brokers available today. Organizations ranging from T-Mobile to Runtastic leverage RabbitMQ, showcasing its adaptability for both startups and large enterprises. Additionally, RabbitMQ is compatible with numerous operating systems and cloud platforms, offering a comprehensive suite of development tools for popular programming languages. Users can deploy RabbitMQ using tools like Kubernetes, BOSH, Chef, Docker, and Puppet, facilitating seamless integration into their existing workflows. Developers can also create cross-language messaging solutions using their preferred programming languages, such as Java, .NET, PHP, Python, JavaScript, Ruby, and Go, enhancing its utility across various projects.

At the heart of extensible programming lies the definition of functions. Python supports both mandatory and optional parameters, keyword arguments, and even allows for arbitrary lists of arguments. Regardless of whether you're just starting out in programming or you have years of experience, Python is accessible and straightforward to learn. This programming language is particularly welcoming for beginners, while still offering depth for those familiar with other programming environments. The subsequent sections provide an excellent foundation to embark on your Python programming journey! The vibrant community organizes numerous conferences and meetups for collaborative coding and sharing ideas. Additionally, Python's extensive documentation serves as a valuable resource, and the mailing lists keep users connected. The Python Package Index (PyPI) features a vast array of third-party modules that enrich the Python experience. With both the standard library and community-contributed modules, Python opens the door to limitless programming possibilities, making it a versatile choice for developers of all levels.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://sourceforge.net/projects/prometheus.mirror/

Easily implement machine learning models on a large scale while enhancing their accuracy. Transform research and development into return on investment by accelerating the deployment of numerous models effectively and reliably. Seldon speeds up the time-to-value, enabling models to become operational more quickly. With Seldon, you can expand your capabilities with certainty, mitigating risks through clear and interpretable results that showcase model performance. The Seldon Deploy platform streamlines the journey to production by offering high-quality inference servers tailored for well-known machine learning frameworks or custom language options tailored to your specific needs. Moreover, Seldon Core Enterprise delivers access to leading-edge, globally recognized open-source MLOps solutions, complete with the assurance of enterprise-level support. This offering is ideal for organizations that need to ensure coverage for multiple ML models deployed and accommodate unlimited users while also providing extra guarantees for models in both staging and production environments, ensuring a robust support system for their machine learning deployments. Additionally, Seldon Core Enterprise fosters trust in the deployment of ML models and protects them against potential challenges.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.

An open-source tool for load testing, Locust allows you to define user interactions using Python code and can bombard your system with millions of concurrent users. Forget about cumbersome user interfaces or complex XML configurations—it's all about straightforward coding. This tool also supports distributed load testing across multiple machines, enabling the simulation of vast numbers of simultaneous users. A key aspect of Locust is that you can articulate all your tests directly in Python code, eliminating the need for unwieldy interfaces. Installation is a breeze, as you can simply obtain Locust from PyPI via pip. By leveraging Python for test definitions, users can easily customize and optimize their load tests as needed.