Alternatives to Pipeshift

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

CoreWeave stands out as a cloud infrastructure service that focuses on GPU-centric computing solutions specifically designed for artificial intelligence applications. Their platform delivers scalable, high-performance GPU clusters that enhance both training and inference processes for AI models, catering to sectors such as machine learning, visual effects, and high-performance computing. In addition to robust GPU capabilities, CoreWeave offers adaptable storage, networking, and managed services that empower AI-focused enterprises, emphasizing reliability, cost-effectiveness, and top-tier security measures. This versatile platform is widely adopted by AI research facilities, labs, and commercial entities aiming to expedite their advancements in artificial intelligence technology. By providing an infrastructure that meets the specific demands of AI workloads, CoreWeave plays a crucial role in driving innovation across various industries.

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.

Deploy your machine learning model in the cloud within minutes using a consolidated packaging format that supports both online and offline operations across various platforms. Experience a performance boost with throughput that is 100 times greater than traditional flask-based model servers, achieved through our innovative micro-batching technique. Provide exceptional prediction services that align seamlessly with DevOps practices and integrate effortlessly with widely-used infrastructure tools. The unified deployment format ensures high-performance model serving while incorporating best practices for DevOps. This service utilizes the BERT model, which has been trained with the TensorFlow framework to effectively gauge the sentiment of movie reviews. Our BentoML workflow eliminates the need for DevOps expertise, automating everything from prediction service registration to deployment and endpoint monitoring, all set up effortlessly for your team. This creates a robust environment for managing substantial ML workloads in production. Ensure that all models, deployments, and updates are easily accessible and maintain control over access through SSO, RBAC, client authentication, and detailed auditing logs, thereby enhancing both security and transparency within your operations. With these features, your machine learning deployment process becomes more efficient and manageable than ever before.

Mistral AI stands out as an innovative startup in the realm of artificial intelligence, focusing on open-source generative solutions. The company provides a diverse array of customizable, enterprise-level AI offerings that can be implemented on various platforms, such as on-premises, cloud, edge, and devices. Among its key products are "Le Chat," a multilingual AI assistant aimed at boosting productivity in both personal and professional settings, and "La Plateforme," a platform for developers that facilitates the creation and deployment of AI-driven applications. With a strong commitment to transparency and cutting-edge innovation, Mistral AI has established itself as a prominent independent AI laboratory, actively contributing to the advancement of open-source AI and influencing policy discussions. Their dedication to fostering an open AI ecosystem underscores their role as a thought leader in the industry.

Kubernetes (K8s) is a powerful open-source platform designed to automate the deployment, scaling, and management of applications that are containerized. By organizing containers into manageable groups, it simplifies the processes of application management and discovery. Drawing from over 15 years of experience in handling production workloads at Google, Kubernetes also incorporates the best practices and innovative ideas from the wider community. Built on the same foundational principles that enable Google to efficiently manage billions of containers weekly, it allows for scaling without necessitating an increase in operational personnel. Whether you are developing locally or operating a large-scale enterprise, Kubernetes adapts to your needs, providing reliable and seamless application delivery regardless of complexity. Moreover, being open-source, Kubernetes offers the flexibility to leverage on-premises, hybrid, or public cloud environments, facilitating easy migration of workloads to the most suitable infrastructure. This adaptability not only enhances operational efficiency but also empowers organizations to respond swiftly to changing demands in their environments.

Kubernetes serves as a powerful foundation for transformative ideas. It enables developers to innovate and deliver projects more rapidly through the premier hybrid cloud and enterprise container solution. Red Hat OpenShift simplifies the process with automated installations, updates, and comprehensive lifecycle management across the entire container ecosystem, encompassing the operating system, Kubernetes, cluster services, and applications on any cloud platform. This service allows teams to operate with speed, flexibility, assurance, and a variety of options. You can code in production mode wherever you prefer to create, enabling a return to meaningful work. Emphasizing security at all stages of the container framework and application lifecycle, Red Hat OpenShift provides robust, long-term enterprise support from a leading contributor to Kubernetes and open-source technology. It is capable of handling the most demanding workloads, including AI/ML, Java, data analytics, databases, and more. Furthermore, it streamlines deployment and lifecycle management through a wide array of technology partners, ensuring that your operational needs are met seamlessly. This integration of capabilities fosters an environment where innovation can thrive without compromise.

Instill Core serves as a comprehensive AI infrastructure solution that effectively handles data, model, and pipeline orchestration, making the development of AI-centric applications more efficient. Users can easily access it through Instill Cloud or opt for self-hosting via the instill-core repository on GitHub. The features of Instill Core comprise: Instill VDP: A highly adaptable Versatile Data Pipeline (VDP) that addresses the complexities of ETL for unstructured data, enabling effective pipeline orchestration. Instill Model: An MLOps/LLMOps platform that guarantees smooth model serving, fine-tuning, and continuous monitoring to achieve peak performance with unstructured data ETL. Instill Artifact: A tool that streamlines data orchestration for a cohesive representation of unstructured data. With its ability to simplify the construction and oversight of intricate AI workflows, Instill Core proves to be essential for developers and data scientists who are harnessing the power of AI technologies. Consequently, it empowers users to innovate and implement AI solutions more effectively.

NVIDIA Run:ai is a cutting-edge platform that streamlines AI workload orchestration and GPU resource management to accelerate AI development and deployment at scale. It dynamically pools GPU resources across hybrid clouds, private data centers, and public clouds to optimize compute efficiency and workload capacity. The solution offers unified AI infrastructure management with centralized control and policy-driven governance, enabling enterprises to maximize GPU utilization while reducing operational costs. Designed with an API-first architecture, Run:ai integrates seamlessly with popular AI frameworks and tools, providing flexible deployment options from on-premises to multi-cloud environments. Its open-source KAI Scheduler offers developers simple and flexible Kubernetes scheduling capabilities. Customers benefit from accelerated AI training and inference with reduced bottlenecks, leading to faster innovation cycles. Run:ai is trusted by organizations seeking to scale AI initiatives efficiently while maintaining full visibility and control. This platform empowers teams to transform resource management into a strategic advantage with zero manual effort.

The Intel® Tiber™ AI Cloud serves as a robust platform tailored to efficiently scale artificial intelligence workloads through cutting-edge computing capabilities. Featuring specialized AI hardware, including the Intel Gaudi AI Processor and Max Series GPUs, it enhances the processes of model training, inference, and deployment. Aimed at enterprise-level applications, this cloud offering allows developers to create and refine models using well-known libraries such as PyTorch. Additionally, with a variety of deployment choices, secure private cloud options, and dedicated expert assistance, Intel Tiber™ guarantees smooth integration and rapid deployment while boosting model performance significantly. This comprehensive solution is ideal for organizations looking to harness the full potential of AI technologies.

Intel® Tiber™ AI Studio serves as an all-encompassing machine learning operating system designed to streamline and unify the development of artificial intelligence. This robust platform accommodates a diverse array of AI workloads and features a hybrid multi-cloud infrastructure that enhances the speed of ML pipeline creation, model training, and deployment processes. By incorporating native Kubernetes orchestration and a meta-scheduler, Tiber™ AI Studio delivers unparalleled flexibility for managing both on-premises and cloud resources. Furthermore, its scalable MLOps framework empowers data scientists to seamlessly experiment, collaborate, and automate their machine learning workflows, all while promoting efficient and cost-effective resource utilization. This innovative approach not only boosts productivity but also fosters a collaborative environment for teams working on AI projects.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

SwarmOne is an innovative platform that autonomously manages infrastructure to enhance the entire lifecycle of AI, from initial training to final deployment, by optimizing and automating AI workloads across diverse environments. Users can kickstart instant AI training, evaluation, and deployment with merely two lines of code and a straightforward one-click hardware setup. It accommodates both traditional coding and no-code approaches, offering effortless integration with any framework, integrated development environment, or operating system, while also being compatible with any brand, number, or generation of GPUs. The self-configuring architecture of SwarmOne takes charge of resource distribution, workload management, and infrastructure swarming, thus removing the necessity for Docker, MLOps, or DevOps practices. Additionally, its cognitive infrastructure layer, along with a burst-to-cloud engine, guarantees optimal functionality regardless of whether the system operates on-premises or in the cloud. By automating many tasks that typically slow down AI model development, SwarmOne empowers data scientists to concentrate solely on their scientific endeavors, which significantly enhances GPU utilization. This allows organizations to accelerate their AI initiatives, ultimately leading to more rapid innovation in their respective fields.

NVIDIA Base Command Manager provides rapid deployment and comprehensive management for diverse AI and high-performance computing clusters, whether at the edge, within data centers, or across multi- and hybrid-cloud settings. This platform automates the setup and management of clusters, accommodating sizes from a few nodes to potentially hundreds of thousands, and is compatible with NVIDIA GPU-accelerated systems as well as other architectures. It facilitates orchestration through Kubernetes, enhancing the efficiency of workload management and resource distribution. With additional tools for monitoring infrastructure and managing workloads, Base Command Manager is tailored for environments that require accelerated computing, making it ideal for a variety of HPC and AI applications. Available alongside NVIDIA DGX systems and within the NVIDIA AI Enterprise software suite, this solution enables the swift construction and administration of high-performance Linux clusters, thereby supporting a range of applications including machine learning and analytics. Through its robust features, Base Command Manager stands out as a key asset for organizations aiming to optimize their computational resources effectively.

Foundry represents a revolutionary type of public cloud, driven by an orchestration platform that simplifies access to AI computing akin to the ease of flipping a switch. Dive into the impactful features of our GPU cloud services that are engineered for optimal performance and unwavering reliability. Whether you are overseeing training processes, catering to client needs, or adhering to research timelines, our platform addresses diverse demands. Leading companies have dedicated years to developing infrastructure teams that create advanced cluster management and workload orchestration solutions to minimize the complexities of hardware management. Foundry democratizes this technology, allowing all users to take advantage of computational power without requiring a large-scale team. In the present GPU landscape, resources are often allocated on a first-come, first-served basis, and pricing can be inconsistent across different vendors, creating challenges during peak demand periods. However, Foundry utilizes a sophisticated mechanism design that guarantees superior price performance compared to any competitor in the market. Ultimately, our goal is to ensure that every user can harness the full potential of AI computing without the usual constraints associated with traditional setups.

A versatile and straightforward workload orchestrator designed to deploy and oversee both containerized and non-containerized applications seamlessly across on-premises and cloud environments at scale. This efficient tool comes as a single 35MB binary that effortlessly fits into your existing infrastructure. It provides an easy operational experience whether on-prem or in the cloud, maintaining minimal overhead. Capable of orchestrating various types of applications—not limited to just containers—it offers top-notch support for Docker, Windows, Java, VMs, and more. By introducing orchestration advantages, it helps enhance existing services. Users can achieve zero downtime deployments, increased resilience, and improved resource utilization without the need for containerization. A single command allows for multi-region, multi-cloud federation, enabling global application deployment to any region using Nomad as a cohesive control plane. This results in a streamlined workflow for deploying applications to either bare metal or cloud environments. Additionally, Nomad facilitates the development of multi-cloud applications with remarkable ease and integrates smoothly with Terraform, Consul, and Vault for efficient provisioning, service networking, and secrets management, making it an indispensable tool in modern application management.

QpiAI Pro is an innovative no-code AutoML and MLOps platform that simplifies AI development by leveraging generative AI tools for tasks such as automated data annotation, fine-tuning foundation models, and facilitating scalable deployment. The platform provides a range of flexible deployment options designed to accommodate the specific requirements of enterprises, including cloud VPC deployment within an enterprise VPC on public clouds, a managed service on public cloud featuring an integrated QpiAI serverless billing system, and deployment within enterprise data centers to ensure full control over security and compliance. These deployment solutions significantly boost operational efficiency while granting comprehensive access to the platform's features. Additionally, QpiAI Pro is an integral component of QpiAI’s product suite, which synergizes AI and quantum technology to address intricate scientific and business challenges across diverse sectors. This robust integration empowers organizations to harness cutting-edge technology for improved decision-making and innovation.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

Replicate is a comprehensive platform designed to help developers and businesses seamlessly run, fine-tune, and deploy machine learning models with just a few lines of code. It hosts thousands of community-contributed models that support diverse use cases such as image and video generation, speech synthesis, music creation, and text generation. Users can enhance model performance by fine-tuning models with their own datasets, enabling highly specialized AI applications. The platform supports custom model deployment through Cog, an open-source tool that automates packaging and deployment on cloud infrastructure while managing scaling transparently. Replicate’s pricing model is usage-based, ensuring customers pay only for the compute time they consume, with support for a variety of GPU and CPU options. The system provides built-in monitoring and logging capabilities to track model performance and troubleshoot predictions. Major companies like Buzzfeed, Unsplash, and Character.ai use Replicate to power their AI features. Replicate’s goal is to democratize access to scalable, production-ready machine learning infrastructure, making AI deployment accessible even to non-experts.

Sync Computing's Gradient is an advanced AI-driven optimization engine designed to streamline and enhance cloud-based data infrastructure. Utilizing cutting-edge machine learning technology developed at MIT, Gradient enables organizations to optimize the performance of their cloud workloads on CPUs and GPUs while significantly reducing costs. The platform offers up to 50% savings on Databricks compute expenses, ensuring workloads consistently meet runtime service level agreements (SLAs). With continuous monitoring and dynamic adjustments, Gradient adapts to changing data sizes and workload patterns, delivering peak efficiency across complex pipelines. Seamlessly integrating with existing tools and supporting various cloud providers, Sync Computing provides a robust solution for optimizing modern data infrastructure.

Nebula provides a streamlined solution for deploying and scaling AI projects quickly, efficiently, and at a lower cost on highly reliable, on-demand GPU infrastructure. With Nebula’s cloud, powered by cutting-edge Nvidia GPUs, you can securely train and infer your models while managing your containerized workloads through an intuitive orchestration layer. The platform offers MLOps and low-code/no-code tools that empower business teams to create and implement AI use cases effortlessly, enabling the fast deployment of AI-driven applications with minimal coding required. You have the flexibility to choose between the Nebula containerized AI cloud, your own on-premises setup, or any preferred cloud environment. With Nebula Unify, organizations can develop and scale AI-enhanced business applications in just weeks, rather than the traditional months, making AI adoption more accessible than ever. This makes Nebula an ideal choice for businesses looking to innovate and stay ahead in a competitive marketplace.

ModelArts, an all-encompassing AI development platform from Huawei Cloud, is crafted to optimize the complete AI workflow for both developers and data scientists. This platform encompasses a comprehensive toolchain that facilitates various phases of AI development, including data preprocessing, semi-automated data labeling, distributed training, automated model creation, and versatile deployment across cloud, edge, and on-premises systems. It is compatible with widely used open-source AI frameworks such as TensorFlow, PyTorch, and MindSpore, while also enabling the integration of customized algorithms to meet unique project requirements. The platform's end-to-end development pipeline fosters enhanced collaboration among DataOps, MLOps, and DevOps teams, resulting in improved development efficiency by as much as 50%. Furthermore, ModelArts offers budget-friendly AI computing resources with a range of specifications, supporting extensive distributed training and accelerating inference processes. This flexibility empowers organizations to adapt their AI solutions to meet evolving business challenges effectively.

Barbara is the Edge AI Platform in the industry space. Barbara helps Machine Learning Teams, manage the lifecycle of models in the Edge, at scale. Now companies can deploy, run, and manage their models remotely, in distributed locations, as easily as in the cloud. Barbara is composed by: .- Industrial Connectors for legacy or next-generation equipment. .- Edge Orchestrator to deploy and control container-based and native edge apps across thousands of distributed locations .- MLOps to optimize, deploy, and monitor your trained model in minutes. .- Marketplace of certified Edge Apps, ready to be deployed. .- Remote Device Management for provisioning, configuration, and updates. More --> www. barbara.tech

Businesses now have numerous options to efficiently train their deep learning and machine learning models without breaking the bank. AI accelerators cater to various scenarios, providing solutions that range from economical inference to robust training capabilities. Getting started is straightforward, thanks to an array of services designed for both development and deployment purposes. Custom-built ASICs known as Tensor Processing Units (TPUs) are specifically designed to train and run deep neural networks with enhanced efficiency. With these tools, organizations can develop and implement more powerful and precise models at a lower cost, achieving faster speeds and greater scalability. A diverse selection of NVIDIA GPUs is available to facilitate cost-effective inference or to enhance training capabilities, whether by scaling up or by expanding out. Furthermore, by utilizing RAPIDS and Spark alongside GPUs, users can execute deep learning tasks with remarkable efficiency. Google Cloud allows users to run GPU workloads while benefiting from top-tier storage, networking, and data analytics technologies that improve overall performance. Additionally, when initiating a VM instance on Compute Engine, users can leverage CPU platforms, which offer a variety of Intel and AMD processors to suit different computational needs. This comprehensive approach empowers businesses to harness the full potential of AI while managing costs effectively.

OpenSVC is an innovative open-source software solution aimed at boosting IT productivity through a comprehensive suite of tools that facilitate service mobility, clustering, container orchestration, configuration management, and thorough infrastructure auditing. The platform is divided into two primary components: the agent and the collector. Acting as a supervisor, clusterware, container orchestrator, and configuration manager, the agent simplifies the deployment, management, and scaling of services across a variety of environments, including on-premises systems, virtual machines, and cloud instances. It is compatible with multiple operating systems, including Unix, Linux, BSD, macOS, and Windows, and provides an array of features such as cluster DNS, backend networks, ingress gateways, and scalers to enhance functionality. Meanwhile, the collector plays a crucial role by aggregating data reported by agents and retrieving information from the site’s infrastructure, which encompasses networks, SANs, storage arrays, backup servers, and asset managers. This collector acts as a dependable, adaptable, and secure repository for data, ensuring that IT teams have access to vital information for decision-making and operational efficiency. Together, these components empower organizations to streamline their IT processes and maximize resource utilization effectively.

Be it prompt engineering, fine-tuning, or extensive training, we are fully equipped to fulfill your business needs. Seamlessly incorporate your newly developed model into your application with the Together Inference API, which offers unparalleled speed and flexible scaling capabilities. Together AI is designed to adapt to your evolving requirements as your business expands. You can explore the training processes of various models and the datasets used to enhance their accuracy while reducing potential risks. It's important to note that the ownership of the fine-tuned model lies with you, not your cloud service provider, allowing for easy transitions if you decide to switch providers for any reason, such as cost adjustments. Furthermore, you can ensure complete data privacy by opting to store your data either locally or within our secure cloud environment. The flexibility and control we offer empower you to make decisions that best suit your business.

Easily train and deploy large-scale AI models with just a single command by pointing to your S3 bucket—then let us take care of everything else, including orchestration, efficiency, node failures, and infrastructure management. The process is straightforward and scalable, allowing you to utilize MosaicML to train and serve large AI models using your own data within your secure environment. Stay ahead of the curve with our up-to-date recipes, techniques, and foundation models, all developed and thoroughly tested by our dedicated research team. With only a few simple steps, you can deploy your models within your private cloud, ensuring that your data and models remain behind your own firewalls. You can initiate your project in one cloud provider and seamlessly transition to another without any disruptions. Gain ownership of the model trained on your data while being able to introspect and clarify the decisions made by the model. Customize content and data filtering to align with your business requirements, and enjoy effortless integration with your existing data pipelines, experiment trackers, and other essential tools. Our solution is designed to be fully interoperable, cloud-agnostic, and validated for enterprise use, ensuring reliability and flexibility for your organization. Additionally, the ease of use and the power of our platform allow teams to focus more on innovation rather than infrastructure management.

Achieve prices that are 3-5 times more competitive than conventional cloud services. FluidStack combines underutilized GPUs from data centers globally to provide unmatched economic advantages in the industry. With just one platform and API, you can deploy over 50,000 high-performance servers in mere seconds. Gain access to extensive A100 and H100 clusters equipped with InfiniBand in just a few days. Utilize FluidStack to train, fine-tune, and launch large language models on thousands of cost-effective GPUs in a matter of minutes. By connecting multiple data centers, FluidStack effectively disrupts monopolistic GPU pricing in the cloud. Experience computing speeds that are five times faster while enhancing cloud efficiency. Instantly tap into more than 47,000 idle servers, all with tier 4 uptime and security, through a user-friendly interface. You can train larger models, set up Kubernetes clusters, render tasks more quickly, and stream content without delays. The setup process requires only one click, allowing for custom image and API deployment in seconds. Additionally, our engineers are available around the clock through Slack, email, or phone, acting as a seamless extension of your team to ensure you receive the support you need. This level of accessibility and assistance can significantly streamline your operations.

Oracle's Container Engine for Kubernetes (OKE) serves as a managed container orchestration solution that significantly minimizes both the time and expenses associated with developing contemporary cloud-native applications. In a departure from many competitors, Oracle Cloud Infrastructure offers OKE as a complimentary service that operates on high-performance and cost-efficient compute shapes. DevOps teams benefit from the ability to utilize unaltered, open-source Kubernetes, enhancing application workload portability while streamlining operations through automated updates and patch management. Users can initiate the deployment of Kubernetes clusters along with essential components like virtual cloud networks, internet gateways, and NAT gateways with just a single click. Furthermore, the platform allows for the automation of Kubernetes tasks via a web-based REST API and a command-line interface (CLI), covering all aspects from cluster creation to scaling and maintenance. Notably, Oracle does not impose any fees for managing clusters, making it an attractive option for developers. Additionally, users can effortlessly and swiftly upgrade their container clusters without experiencing any downtime, ensuring they remain aligned with the latest stable Kubernetes version. This combination of features positions Oracle's offering as a robust solution for organizations looking to optimize their cloud-native development processes.

Spectro Cloud’s Palette platform provides enterprises with a powerful and scalable solution for managing Kubernetes clusters across multiple environments, including cloud, edge, and on-premises data centers. By leveraging full-stack declarative orchestration, Palette allows teams to define cluster profiles that ensure consistency while preserving the freedom to customize infrastructure, container workloads, OS, and Kubernetes distributions. The platform’s lifecycle management capabilities streamline cluster provisioning, upgrades, and maintenance across hybrid and multi-cloud setups. It also integrates with a wide range of tools and services, including major cloud providers like AWS, Azure, and Google Cloud, as well as Kubernetes distributions such as EKS, OpenShift, and Rancher. Security is a priority, with Palette offering enterprise-grade compliance certifications such as FIPS and FedRAMP, making it suitable for government and regulated industries. Additionally, the platform supports advanced use cases like AI workloads at the edge, virtual clusters, and multitenancy for ISVs. Deployment options are flexible, covering self-hosted, SaaS, or airgapped environments to suit diverse operational needs. This makes Palette a versatile platform for organizations aiming to reduce complexity and increase operational control over Kubernetes.

Amazon SageMaker simplifies the process of deploying machine learning models for making predictions, also referred to as inference, ensuring optimal price-performance for a variety of applications. The service offers an extensive range of infrastructure and deployment options tailored to fulfill all your machine learning inference requirements. As a fully managed solution, it seamlessly integrates with MLOps tools, allowing you to efficiently scale your model deployments, minimize inference costs, manage models more effectively in a production environment, and alleviate operational challenges. Whether you require low latency (just a few milliseconds) and high throughput (capable of handling hundreds of thousands of requests per second) or longer-running inference for applications like natural language processing and computer vision, Amazon SageMaker caters to all your inference needs, making it a versatile choice for data-driven organizations. This comprehensive approach ensures that businesses can leverage machine learning without encountering significant technical hurdles.

Create your generative AI solutions in just a few minutes with GMI GPU Cloud. GMI Cloud goes beyond simple bare metal offerings by enabling you to train, fine-tune, and run cutting-edge models seamlessly. Our clusters come fully prepared with scalable GPU containers and widely-used ML frameworks, allowing for immediate access to the most advanced GPUs tailored for your AI tasks. Whether you seek flexible on-demand GPUs or dedicated private cloud setups, we have the perfect solution for you. Optimize your GPU utility with our ready-to-use Kubernetes software, which simplifies the process of allocating, deploying, and monitoring GPUs or nodes through sophisticated orchestration tools. You can customize and deploy models tailored to your data, enabling rapid development of AI applications. GMI Cloud empowers you to deploy any GPU workload swiftly and efficiently, allowing you to concentrate on executing ML models instead of handling infrastructure concerns. Launching pre-configured environments saves you valuable time by eliminating the need to build container images, install software, download models, and configure environment variables manually. Alternatively, you can utilize your own Docker image to cater to specific requirements, ensuring flexibility in your development process. With GMI Cloud, you'll find that the path to innovative AI applications is smoother and faster than ever before.

Efficiently manage multicluster environments for Azure Kubernetes Service (AKS) that involve tasks such as workload distribution, north-south traffic load balancing for incoming requests to various clusters, and coordinated upgrades across different clusters. The fleet cluster offers a centralized management system for overseeing all your clusters on a large scale. A dedicated hub cluster manages the upgrades and the configuration of your Kubernetes clusters seamlessly. Through Kubernetes configuration propagation, you can apply policies and overrides to distribute resources across the fleet's member clusters effectively. The north-south load balancer regulates the movement of traffic among workloads situated in multiple member clusters within the fleet. You can group various Azure Kubernetes Service (AKS) clusters to streamline workflows involving Kubernetes configuration propagation and networking across multiple clusters. Furthermore, the fleet system necessitates a hub Kubernetes cluster to maintain configurations related to placement policies and multicluster networking, thereby enhancing operational efficiency and simplifying management tasks. This approach not only optimizes resource usage but also helps in maintaining consistency and reliability across all clusters involved.

Streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with an extensive array of efficient tools for swiftly building, training, and deploying machine learning models. Enhance the speed of market readiness and promote collaboration among teams through leading-edge MLOps—akin to DevOps but tailored for machine learning. Drive innovation within a secure, reliable platform that prioritizes responsible AI practices. Cater to users of all expertise levels with options for both code-centric and drag-and-drop interfaces, along with automated machine learning features. Implement comprehensive MLOps functionalities that seamlessly align with existing DevOps workflows, facilitating the management of the entire machine learning lifecycle. Emphasize responsible AI by providing insights into model interpretability and fairness, securing data through differential privacy and confidential computing, and maintaining control over the machine learning lifecycle with audit trails and datasheets. Additionally, ensure exceptional compatibility with top open-source frameworks and programming languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, thus broadening accessibility and usability for diverse projects. By fostering an environment that promotes collaboration and innovation, teams can achieve remarkable advancements in their machine learning endeavors.

Utilize sophisticated coding and language models across a diverse range of applications. Harness the power of expansive generative AI models that possess an intricate grasp of both language and code, paving the way for enhanced reasoning and comprehension skills essential for developing innovative applications. These advanced models can be applied to multiple scenarios, including writing support, automatic code creation, and data reasoning. Moreover, ensure responsible AI practices by implementing measures to detect and mitigate potential misuse, all while benefiting from enterprise-level security features offered by Azure. With access to generative models pretrained on vast datasets comprising trillions of words, you can explore new possibilities in language processing, code analysis, reasoning, inferencing, and comprehension. Further personalize these generative models by using labeled datasets tailored to your unique needs through an easy-to-use REST API. Additionally, you can optimize your model's performance by fine-tuning hyperparameters for improved output accuracy. The few-shot learning functionality allows you to provide sample inputs to the API, resulting in more pertinent and context-aware outcomes. This flexibility enhances your ability to meet specific application demands effectively.

Introducing a pioneering compute protocol that combines integrated hardware and software for the training and fine-tuning of AI models. Experience a reduction in training expenses by as much as 80%. You can deploy your models in mere seconds, utilizing either open-source templates or your own customized models. Effortlessly debug your containers while having access to vital resources such as GPU, CPU, Memory, and other performance metrics. Real-time log monitoring allows for immediate insights into your processes. Maintain complete accountability by tracing all models and training datasets with cryptographically verified proofs. Command the entire training workflow effortlessly with just a few straightforward commands. Additionally, you can earn block rewards by contributing your computer to the network, while also tracking essential metrics like connectivity and uptime to ensure optimal performance. The innovative design of this system not only enhances efficiency but also promotes a collaborative environment for AI development.

FPT Cloud represents an advanced cloud computing and AI solution designed to enhance innovation through a comprehensive and modular suite of more than 80 services, encompassing areas such as computing, storage, databases, networking, security, AI development, backup, disaster recovery, and data analytics, all adhering to global standards. Among its features are scalable virtual servers that provide auto-scaling capabilities and boast a 99.99% uptime guarantee; GPU-optimized infrastructure specifically designed for AI and machine learning tasks; the FPT AI Factory, which offers a complete AI lifecycle suite enhanced by NVIDIA supercomputing technology, including infrastructure, model pre-training, fine-tuning, and AI notebooks; high-performance object and block storage options that are S3-compatible and encrypted; a Kubernetes Engine that facilitates managed container orchestration with portability across different cloud environments; as well as managed database solutions that support both SQL and NoSQL systems. Additionally, it incorporates sophisticated security measures with next-generation firewalls and web application firewalls, alongside centralized monitoring and activity logging features, ensuring a holistic approach to cloud services. This multifaceted platform is designed to meet the diverse needs of modern enterprises, making it a key player in the evolving landscape of cloud technology.

The NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process.

Simplifying Multi-Cloud and Multi-Cluster Kubernetes application deployment and migration is now easier than ever with CAPE. Unlock the full potential of your Kubernetes capabilities with its key features, including Disaster Recovery that allows seamless backup and restore for stateful applications. With robust Data Mobility and Migration, you can securely manage and transfer applications and data across on-premises, private, and public cloud environments. CAPE also facilitates Multi-cluster Application Deployment, enabling stateful applications to be deployed efficiently across various clusters and clouds. Its intuitive Drag & Drop CI/CD Workflow Manager simplifies the configuration and deployment of complex CI/CD pipelines, making it accessible for users at all levels. The versatility of CAPE™ enhances Kubernetes operations by streamlining Disaster Recovery processes, facilitating Cluster Migration and Upgrades, ensuring Data Protection, enabling Data Cloning, and expediting Application Deployment. Moreover, CAPE provides a comprehensive control plane for federating clusters and managing applications and services seamlessly across diverse environments. This innovative tool brings clarity and efficiency to Kubernetes management, ensuring your applications thrive in a multi-cloud landscape.

SambaNova is the leading purpose-built AI system for generative and agentic AI implementations, from chips to models, that gives enterprises full control over their model and private data. We take the best models, optimize them for fast tokens and higher batch sizes, the largest inputs and enable customizations to deliver value with simplicity. The full suite includes the SambaNova DataScale system, the SambaStudio software, and the innovative SambaNova Composition of Experts (CoE) model architecture. These components combine into a powerful platform that delivers unparalleled performance, ease of use, accuracy, data privacy, and the ability to power every use case across the world's largest organizations. At the heart of SambaNova innovation is the fourth generation SN40L Reconfigurable Dataflow Unit (RDU). Purpose built for AI workloads, the SN40L RDU takes advantage of a dataflow architecture and a three-tiered memory design. The dataflow architecture eliminates the challenges that GPUs have with high performance inference. The three tiers of memory enable the platform to run hundreds of models on a single node and to switch between them in microseconds. We give our customers the optionality to experience through the cloud or on-premise.

WhiteFiber operates as a comprehensive AI infrastructure platform that specializes in delivering high-performance GPU cloud services and HPC colocation solutions specifically designed for AI and machine learning applications. Their cloud services are meticulously engineered for tasks involving machine learning, expansive language models, and deep learning, equipped with advanced NVIDIA H200, B200, and GB200 GPUs alongside ultra-fast Ethernet and InfiniBand networking, achieving an impressive GPU fabric bandwidth of up to 3.2 Tb/s. Supporting a broad range of scaling capabilities from hundreds to tens of thousands of GPUs, WhiteFiber offers various deployment alternatives such as bare metal, containerized applications, and virtualized setups. The platform guarantees enterprise-level support and service level agreements (SLAs), incorporating unique cluster management, orchestration, and observability tools. Additionally, WhiteFiber’s data centers are strategically optimized for AI and HPC colocation, featuring high-density power, direct liquid cooling systems, and rapid deployment options, while also ensuring redundancy and scalability through cross-data center dark fiber connectivity. With a commitment to innovation and reliability, WhiteFiber stands out as a key player in the AI infrastructure ecosystem.

Manage and orchestrate applications seamlessly on a Kubernetes platform that is fully managed, utilizing a centralized SaaS approach for overseeing distributed applications through a unified interface and advanced observability features. Streamline operations by handling deployments uniformly across on-premises, cloud, and edge environments. Experience effortless management and scaling of applications across various Kubernetes clusters, whether at customer locations or within the F5 Distributed Cloud Regional Edge, all through a single Kubernetes-compatible API that simplifies multi-cluster oversight. You can deploy, deliver, and secure applications across different sites as if they were all part of one cohesive "virtual" location. Furthermore, ensure that distributed applications operate with consistent, production-grade Kubernetes, regardless of their deployment sites, which can range from private and public clouds to edge environments. Enhance security with a zero trust approach at the Kubernetes Gateway, extending ingress services backed by WAAP, service policy management, and comprehensive network and application firewall protections. This approach not only secures your applications but also fosters a more resilient and adaptable infrastructure.

Easily fine-tune and receive completions from private LLMs through a user-friendly web API without any need for complex infrastructure. Instantly create AI applications that comply with SOC2 standards while ensuring privacy. Our developer platform allows you to tailor models to fit your specific needs effortlessly—just specify the data you'd like to use for training and select the base model, and we’ll handle everything else for you. Integrate private LLMs into your applications with a single API call, eliminating the challenges of deployment, orchestration, and infrastructure management. Experience the most advanced open-source model available, which boasts remarkable narrative and reasoning skills along with highly generalized capabilities. Leverage a fully unlocked LLM to develop top-tier internal automation solutions for your organization, ensuring efficiency and innovation in your workflows. With our comprehensive tools, you can transform your AI aspirations into reality in no time.

Crusoe delivers a cloud infrastructure tailored for artificial intelligence tasks, equipped with cutting-edge GPU capabilities and top-tier data centers. This platform is engineered for AI-centric computing, showcasing high-density racks alongside innovative direct liquid-to-chip cooling to enhance overall performance. Crusoe’s infrastructure guarantees dependable and scalable AI solutions through features like automated node swapping and comprehensive monitoring, complemented by a dedicated customer success team that assists enterprises in rolling out production-level AI workloads. Furthermore, Crusoe emphasizes environmental sustainability by utilizing clean, renewable energy sources, which enables them to offer economical services at competitive pricing. With a commitment to excellence, Crusoe continuously evolves its offerings to meet the dynamic needs of the AI landscape.