Domino Enterprise MLOps Platform Integrations

GitLab is a complete DevOps platform. GitLab gives you a complete CI/CD toolchain right out of the box. One interface. One conversation. One permission model. GitLab is a complete DevOps platform, delivered in one application. It fundamentally changes the way Security, Development, and Ops teams collaborate. GitLab reduces development time and costs, reduces application vulnerabilities, and speeds up software delivery. It also increases developer productivity. Source code management allows for collaboration, sharing, and coordination across the entire software development team. To accelerate software delivery, track and merge branches, audit changes, and enable concurrent work. Code can be reviewed, discussed, shared knowledge, and identified defects among distributed teams through asynchronous review. Automate, track, and report code reviews.

Jira is a project management tool that allows you to plan and track the work of your entire team. Atlassian's Jira is the #1 tool for software development teams to plan and build great products. Jira is trusted by thousands of teams. It offers a range of tools to help plan, track, and release world-class software. It also allows you to capture and organize issues, assign work, and follow team activity. It integrates with leading developer software for end-toend traceability. Jira can help you break down big ideas into manageable steps, whether they are small projects or large cross-functional programs. Organize your work, create milestones and dependencies, and more. Linking work to goals allows everyone to see how their work contributes towards company objectives, and to stay aligned with what's important. Your next step, suggested by AI. Atlassian Intelligence automatically suggests tasks to help you get your big ideas done.

GitHub stands as the leading platform for developers globally, renowned for its security, scalability, and community appreciation. By joining the ranks of millions of developers and businesses, you can contribute to the software that drives the world forward. Collaborate within the most inventive communities, all while utilizing our top-tier tools, support, and services. If you're overseeing various contributors, take advantage of our free GitHub Team for Open Source option. Additionally, GitHub Sponsors is available to assist in financing your projects. We're thrilled to announce the return of The Pack, where we’ve teamed up to provide students and educators with complimentary access to premier developer tools throughout the academic year and beyond. Furthermore, if you work for a recognized nonprofit, association, or a 501(c)(3), we offer a discounted Organization account to support your mission. With these offerings, GitHub continues to empower diverse users in their software development journeys.

All your Python development needs are consolidated in one application. While PyCharm handles routine tasks, you can save precious time and concentrate on more significant projects, fully utilizing its keyboard-centric design to explore countless productivity features. This IDE is well-versed in your code and can be trusted for features like intelligent code completion, immediate error detection, and quick-fix suggestions, alongside straightforward project navigation and additional capabilities. With PyCharm, you can write organized and maintainable code, as it assists in maintaining quality through PEP8 compliance checks, testing support, smart refactoring options, and a comprehensive range of inspections. Created by programmers specifically for other programmers, PyCharm equips you with every tool necessary for effective Python development, allowing you to focus on what matters most. Additionally, PyCharm's robust navigation and automated refactoring features further enhance your coding experience, ensuring that you remain efficient and productive throughout your projects.

Bitbucket transcends traditional Git code management by offering a unified platform where teams can plan, collaborate on code, test, and deploy all in one place. It is free for small teams of up to five members and offers scalable options with Standard and Premium plans priced at $3 and $6 per user per month, respectively. By enabling the creation of Bitbucket branches directly from Jira issues or Trello cards, it helps keep projects systematically organized. The platform supports build, test, and deployment processes with its integrated CI/CD, enhancing efficiency through configuration as code and rapid feedback cycles. Code reviews are streamlined with pull requests, allowing teams to create a merge checklist and designate approvers while facilitating discussions directly in the source code using inline comments. With Bitbucket Pipelines featuring Deployments, teams can seamlessly integrate their build, test, and deployment processes. Security is prioritized with features like IP whitelisting and mandatory two-step verification, ensuring that code remains protected in the cloud. Additionally, users can restrict access to specific individuals and manage their permissions with branch controls and merge checks to ensure the highest quality of code output. This comprehensive suite of features makes Bitbucket an invaluable tool for modern software development teams.

MATLAB® offers a desktop environment specifically optimized for iterative design and analysis, paired with a programming language that allows for straightforward expression of matrix and array mathematics. It features the Live Editor, which enables users to create scripts that merge code, output, and formatted text within an interactive notebook. The toolboxes provided by MATLAB are meticulously developed, thoroughly tested, and comprehensively documented. Additionally, MATLAB applications allow users to visualize how various algorithms interact with their data. You can refine your results through repeated iterations and then easily generate a MATLAB program to replicate or automate your processes. The platform also allows for scaling analyses across clusters, GPUs, and cloud environments with minimal modifications to your existing code. There is no need to overhaul your programming practices or master complex big data techniques. You can automatically convert MATLAB algorithms into C/C++, HDL, and CUDA code, enabling execution on embedded processors or FPGA/ASIC systems. Furthermore, when used in conjunction with Simulink, MATLAB enhances the support for Model-Based Design methodologies, making it a versatile tool for engineers and researchers alike. This adaptability makes MATLAB an essential resource for tackling a wide range of computational challenges.

Empowering businesses to engage in genuine data science quickly and effectively through a comprehensive machine learning platform is crucial. By minimizing the time spent managing tools and infrastructure, organizations can concentrate on developing machine learning applications that drive growth. Anaconda Enterprise alleviates the challenges associated with ML operations, grants access to open-source innovations, and lays the groundwork for robust data science and machine learning operations without confining users to specific models, templates, or workflows. Software developers and data scientists can seamlessly collaborate within AE to create, test, debug, and deploy models using their chosen programming languages and tools. Additionally, AE facilitates access to both notebooks and integrated development environments (IDEs), enhancing collaborative efficiency. Users can also select from a variety of example projects or utilize preconfigured projects tailored to their needs. Furthermore, AE automatically containerizes projects, ensuring they can be effortlessly transitioned between various environments as required. This flexibility ultimately empowers teams to innovate and adapt to changing business demands more readily.

Snowflake offers a unified AI Data Cloud platform that transforms how businesses store, analyze, and leverage data by eliminating silos and simplifying architectures. It features interoperable storage that enables seamless access to diverse datasets at massive scale, along with an elastic compute engine that delivers leading performance for a wide range of workloads. Snowflake Cortex AI integrates secure access to cutting-edge large language models and AI services, empowering enterprises to accelerate AI-driven insights. The platform’s cloud services automate and streamline resource management, reducing complexity and cost. Snowflake also offers Snowgrid, which securely connects data and applications across multiple regions and cloud providers for a consistent experience. Their Horizon Catalog provides built-in governance to manage security, privacy, compliance, and access control. Snowflake Marketplace connects users to critical business data and apps to foster collaboration within the AI Data Cloud network. Serving over 11,000 customers worldwide, Snowflake supports industries from healthcare and finance to retail and telecom.

Flask is a minimalistic WSGI web application framework that prioritizes simplicity and rapid development, making it accessible for beginners while also being capable of supporting complex projects. Originally, it served as a basic wrapper around Werkzeug and Jinja but has since evolved into one of the leading frameworks for web development in Python. It provides recommendations without imposing strict requirements on dependencies or project structures, giving developers the freedom to select their preferred tools and libraries. Furthermore, the community has contributed a wide array of extensions, facilitating the integration of additional features and functionality into applications. This flexibility and support have contributed to Flask's widespread adoption and enduring popularity among developers.

Project Jupyter is dedicated to the creation of open-source tools, standards, and services that facilitate interactive computing in numerous programming languages. At the heart of this initiative is JupyterLab, a web-based interactive development environment designed for Jupyter notebooks, coding, and data manipulation. JupyterLab offers remarkable flexibility, allowing users to customize and organize the interface to cater to various workflows in fields such as data science, scientific research, and machine learning. Its extensibility and modular nature enable developers to create plugins that introduce new features and seamlessly integrate with existing components. The Jupyter Notebook serves as an open-source web application enabling users to produce and share documents that incorporate live code, mathematical equations, visualizations, and descriptive text. Common applications of Jupyter include data cleaning and transformation, numerical simulations, statistical analysis, data visualization, and machine learning, among others. Supporting over 40 programming languages—including popular ones like Python, R, Julia, and Scala—Jupyter continues to be a valuable resource for researchers and developers alike, fostering collaborative and innovative approaches to computing challenges.

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.

Amazon SageMaker is a comprehensive machine learning platform that integrates powerful tools for model building, training, and deployment in one cohesive environment. It combines data processing, AI model development, and collaboration features, allowing teams to streamline the development of custom AI applications. With SageMaker, users can easily access data stored across Amazon S3 data lakes and Amazon Redshift data warehouses, facilitating faster insights and AI model development. It also supports generative AI use cases, enabling users to develop and scale applications with cutting-edge AI technologies. The platform’s governance and security features ensure that data and models are handled with precision and compliance throughout the entire ML lifecycle. Furthermore, SageMaker provides a unified development studio for real-time collaboration, speeding up data discovery and model deployment.

Python-SQL is a library designed to facilitate the writing of SQL queries in a more Pythonic manner, supporting a variety of functionalities such as basic selects, selections with where clauses, and complex joins including multiple joins. It allows for groupings and output naming, alongside ordering results and executing sub-selects, while also enabling queries on different schemas. The library is capable of performing insert operations with default values, specific values, or even using another query for insertion. Furthermore, it accommodates updates with specified values, conditions, or lists, as well as deletions based on conditions or sub-queries. Additionally, it offers different query styles including limit style, qmark style, and numeric style to cater to diverse programming needs. This versatility makes Python-SQL a powerful tool for developers working with databases in a Python environment.

R is a comprehensive environment and programming language tailored for statistical analysis and graphical representation. As a part of the GNU project, it shares similarities with the S language, which was originally designed by John Chambers and his team at Bell Laboratories, now known as Lucent Technologies. Essentially, R serves as an alternative implementation of S, and while there are notable distinctions between the two, a significant amount of S code can be executed in R without modification. This versatile language offers a broad spectrum of statistical methods, including both linear and nonlinear modeling, classical statistical tests, time-series analytics, classification, and clustering, among others, and it boasts a high level of extensibility. The S language is frequently utilized in research focused on statistical methodologies, and R presents an Open Source avenue for engaging in this field. Moreover, one of R's key advantages lies in its capability to generate high-quality publication-ready graphics, facilitating the inclusion of mathematical symbols and formulas as needed, which enhances its usability for researchers and analysts alike. Ultimately, R continues to be a powerful tool for those seeking to explore and visualize data effectively.

Complexity is the enemy of security. Simplify and scale fine-grained data access control. Dynamically authorize and audit every query to comply with data security and privacy regulations. Okera integrates seamlessly into your infrastructure – in the cloud, on premise, and with cloud-native and legacy tools. With Okera, data users can use data responsibly, while protecting them from inappropriately accessing data that is confidential, personally identifiable, or regulated. Okera’s robust audit capabilities and data usage intelligence deliver the real-time and historical information that data security, compliance, and data delivery teams need to respond quickly to incidents, optimize processes, and analyze the performance of enterprise data initiatives.

The NVIDIA® EGX™ Platform for professional visualization is designed to enhance a variety of workloads, ranging from rendering and virtualization to engineering analysis and data science, across any device. This adaptable reference design integrates powerful NVIDIA GPUs with NVIDIA virtual GPU (vGPU) software and superior networking capabilities, offering remarkable graphics and computational strength, which allows artists and engineers to perform optimally from any location, all while significantly reducing costs, physical space, and energy consumption compared to traditional CPU-based systems. By utilizing the EGX Platform alongside NVIDIA RTX Virtual Workstation (vWS) software, organizations can easily implement a high-performance and budget-friendly infrastructure that has been rigorously tested and approved in collaboration with leading industry partners and ISV applications on reliable OEM servers. This cutting-edge solution not only empowers professionals to work remotely but also boosts productivity, enhances data center efficiency, and lowers IT management expenses, ultimately transforming how teams collaborate and innovate. Consequently, the EGX Platform exemplifies the future of professional visualization in a rapidly evolving technological landscape.

The RAPIDS software library suite, designed on CUDA-X AI, empowers users to run comprehensive data science and analytics workflows entirely on GPUs. It utilizes NVIDIA® CUDA® primitives for optimizing low-level computations while providing user-friendly Python interfaces that leverage GPU parallelism and high-speed memory access. Additionally, RAPIDS emphasizes essential data preparation processes tailored for analytics and data science, featuring a familiar DataFrame API that seamlessly integrates with various machine learning algorithms to enhance pipeline efficiency without incurring the usual serialization overhead. Moreover, it supports multi-node and multi-GPU setups, enabling significantly faster processing and training on considerably larger datasets. By incorporating RAPIDS, you can enhance your Python data science workflows with minimal code modifications and without the need to learn any new tools. This approach not only streamlines the model iteration process but also facilitates more frequent deployments, ultimately leading to improved machine learning model accuracy. As a result, RAPIDS significantly transforms the landscape of data science, making it more efficient and accessible.

A web-based notebook facilitates interactive data analytics and collaborative documentation using SQL, Scala, and other languages. With an IPython interpreter, it delivers a user experience similar to that of Jupyter Notebook. The latest version introduces several enhancements, including a dynamic form at the note level, a note revision comparison tool, and the option to execute paragraphs sequentially rather than simultaneously, as was the case in earlier versions. Additionally, an interpreter lifecycle manager ensures that idle interpreter processes are automatically terminated, freeing up resources when they are not actively being utilized. This improvement not only optimizes performance but also enhances the overall user experience.

SPARK digitizes the entire process of loan origination, making it easier, safer, and more efficient for banks and customers. Our platform has helped lenders originate over $8 billion worth of loans by eliminating manual processes and network sharing.

The NVIDIA HPC Software Development Kit (SDK) offers a comprehensive suite of reliable compilers, libraries, and software tools that are crucial for enhancing developer efficiency as well as the performance and adaptability of HPC applications. This SDK includes C, C++, and Fortran compilers that facilitate GPU acceleration for HPC modeling and simulation applications through standard C++ and Fortran, as well as OpenACC® directives and CUDA®. Additionally, GPU-accelerated mathematical libraries boost the efficiency of widely used HPC algorithms, while optimized communication libraries support standards-based multi-GPU and scalable systems programming. The inclusion of performance profiling and debugging tools streamlines the process of porting and optimizing HPC applications, and containerization tools ensure straightforward deployment whether on-premises or in cloud environments. Furthermore, with compatibility for NVIDIA GPUs and various CPU architectures like Arm, OpenPOWER, or x86-64 running on Linux, the HPC SDK equips developers with all the necessary resources to create high-performance GPU-accelerated HPC applications effectively. Ultimately, this robust toolkit is indispensable for anyone looking to push the boundaries of high-performance computing.

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.

H2O.ai stands at the forefront of open source AI and machine learning, dedicated to making artificial intelligence accessible to all. Our cutting-edge platforms, which are designed for enterprise readiness, support hundreds of thousands of data scientists across more than 20,000 organizations worldwide. By enabling companies in sectors such as finance, insurance, healthcare, telecommunications, retail, pharmaceuticals, and marketing, we are helping to foster a new wave of businesses that harness the power of AI to drive tangible value and innovation in today's marketplace. With our commitment to democratizing technology, we aim to transform how industries operate and thrive.

NVIDIA GPU Cloud (NGC) serves as a cloud platform that harnesses GPU acceleration for deep learning and scientific computations. It offers a comprehensive catalog of fully integrated containers for deep learning frameworks designed to optimize performance on NVIDIA GPUs, whether in single or multi-GPU setups. Additionally, the NVIDIA train, adapt, and optimize (TAO) platform streamlines the process of developing enterprise AI applications by facilitating quick model adaptation and refinement. Through a user-friendly guided workflow, organizations can fine-tune pre-trained models with their unique datasets, enabling them to create precise AI models in mere hours instead of the traditional months, thereby reducing the necessity for extensive training periods and specialized AI knowledge. If you're eager to dive into the world of containers and models on NGC, you’ve found the ideal starting point. Furthermore, NGC's Private Registries empower users to securely manage and deploy their proprietary assets, enhancing their AI development journey.

Dask is a freely available open-source library that is developed in collaboration with various community initiatives such as NumPy, pandas, and scikit-learn. It leverages the existing Python APIs and data structures, allowing users to seamlessly transition between NumPy, pandas, and scikit-learn and their Dask-enhanced versions. The schedulers in Dask are capable of scaling across extensive clusters with thousands of nodes, and its algorithms have been validated on some of the most powerful supercomputers globally. However, getting started doesn't require access to a large cluster; Dask includes schedulers tailored for personal computing environments. Many individuals currently utilize Dask to enhance computations on their laptops, taking advantage of multiple processing cores and utilizing disk space for additional storage. Furthermore, Dask provides lower-level APIs that enable the creation of customized systems for internal applications. This functionality is particularly beneficial for open-source innovators looking to parallelize their own software packages, as well as business executives aiming to scale their unique business strategies efficiently. In essence, Dask serves as a versatile tool that bridges the gap between simple local computations and complex distributed processing.