Best AI Infrastructure Platforms in Australia

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Introducing Ametnes: Streamlined Data Application Deployment and Management Experience the future of data application deployment with Ametnes. Our cutting-edge solution revolutionizes the way you handle data applications in your private environment. Say goodbye to the complexities and security concerns of manual deployment. Ametnes addresses these challenges head-on by automating the entire process, ensuring a seamless and secure experience for our valued customers. With our intuitive platform, deploying and managing data applications has never been more astonishingly easy. Unlock the full potential of your private environment with Ametnes. Embrace efficiency, security, and simplicity like never before. Elevate your data management game - choose Ametnes today!

ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.

Build, deploy, and scale end-to-end AI applications in the cloud. Griptape gives developers everything they need to build, deploy, and scale retrieval-driven AI-powered applications, from the development framework to the execution runtime. 🎢 Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. ☁️ Griptape Cloud is a one-stop shop to hosting your AI structures, whether they are built with Griptape, another framework, or call directly to the LLMs themselves. Simply point to your GitHub repository to get started. 🔥 Run your hosted code by hitting a basic API layer from wherever you need, offloading the expensive tasks of AI development to the cloud. 📈 Automatically scale workloads to fit your needs.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required.

A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models. AutoTrain is an automatic way to train and deploy state-of-the-art Machine Learning models, seamlessly integrated with the Hugging Face ecosystem. Your training data stays on our server, and is private to your account. All data transfers are protected with encryption. Available today: text classification, text scoring, entity recognition, summarization, question answering, translation and tabular. CSV, TSV or JSON files, hosted anywhere. We delete your training data after training is done. Hugging Face also hosts an AI content detection tool.

Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.

Powerful, self-serve machine learning platform where you can turn models into scalable APIs in just a few clicks. Sign up for Deep Infra account using GitHub or log in using GitHub. Choose among hundreds of the most popular ML models. Use a simple rest API to call your model. Deploy models to production faster and cheaper with our serverless GPUs than developing the infrastructure yourself. We have different pricing models depending on the model used. Some of our language models offer per-token pricing. Most other models are billed for inference execution time. With this pricing model, you only pay for what you use. There are no long-term contracts or upfront costs, and you can easily scale up and down as your business needs change. All models run on A100 GPUs, optimized for inference performance and low latency. Our system will automatically scale the model based on your needs.

DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.

The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'

NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.

Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.

A fully-managed platform for Ray, from the creators of Ray. The best way to develop, scale, and deploy AI apps on Ray. Accelerate development and deployment for any AI application, at any scale. Everything you love about Ray, minus the DevOps load. Let us run Ray for you, hosted on cloud infrastructure fully managed by us so that you can focus on what you do best, and ship great products. Anyscale automatically scales your infrastructure and clusters up or down to meet the dynamic demands of your workloads. Whether it’s executing a production workflow on a schedule (for eg. retraining and updating a model with fresh data every week) or running a highly scalable and low-latency production service (for eg. serving a machine learning model), Anyscale makes it easy to create, deploy, and monitor machine learning workflows in production. Anyscale will automatically create a cluster, run the job on it, and monitor the job until it succeeds.

Machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses. We built the Tensor Processing Unit (TPU) in order to make it possible for anyone to achieve similar breakthroughs. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Here’s how you can put the TPU and machine learning to work accelerating your company’s success, especially at scale. Cloud TPU is designed to run cutting-edge machine learning models with AI services on Google Cloud. And its custom high-speed network offers over 100 petaflops of performance in a single pod, enough computational power to transform your business or create the next research breakthrough. Training machine learning models is like compiling code: you need to update often, and you want to do so as efficiently as possible. ML models need to be trained over and over as apps are built, deployed, and refined.

The single development environment for the entire data science workflow. Natively analyze your data with a reduction in context switching between services. Data to training at scale. Build and train models 5X faster, compared to traditional notebooks. Scale-up model development with simple connectivity to Vertex AI services. Simplified access to data and in-notebook access to machine learning with BigQuery, Dataproc, Spark, and Vertex AI integration. Take advantage of the power of infinite computing with Vertex AI training for experimentation and prototyping, to go from data to training at scale. Using Vertex AI Workbench you can implement your training, and deployment workflows on Vertex AI from one place. A Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities. Explore data and train ML models with easy connections to Google Cloud's big data solutions.

Speed up compute jobs like machine learning and HPC. A wide selection of GPUs to match a range of performance and price points. Flexible pricing and machine customizations to optimize your workload. High-performance GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization. NVIDIA K80, P100, P4, T4, V100, and A100 GPUs provide a range of compute options to cover your workload for each cost and performance need. Optimally balance the processor, memory, high-performance disk, and up to 8 GPUs per instance for your individual workload. All with the per-second billing, so you only pay only for what you need while you are using it. Run GPU workloads on Google Cloud Platform where you have access to industry-leading storage, networking, and data analytics technologies. Compute Engine provides GPUs that you can add to your virtual machine instances. Learn what you can do with GPUs and what types of GPU hardware are available.

Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.

Easily build, deploy, and manage computer vision applications with a fully managed, end-to-end application development environment that reduces the time to build computer vision applications from days to minutes at one-tenth the cost of current offerings. Quickly and conveniently ingest real-time video and image streams at a global scale. Easily build computer vision applications using a drag-and-drop interface. Store and search petabytes of data with built-in AI capabilities. Vertex AI Vision includes all the tools needed to manage the life cycle of computer vision applications, across ingestion, analysis, storage, and deployment. Easily connect application output to a data destination, like BigQuery for analytics, or live streaming to drive real-time business actions. Ingest thousands of video streams from across the globe. With a monthly pricing model, enjoy up to one-tenth lower costs than previous offerings.

Our infrastructure is equipped to meet your computing requirements, be it one or thousands of GPUs, or one vCPU to tens of thousands of vCPUs, we've got you covered. Our resources are readily available to cater to your needs, whenever you need them. Switching between GPU and CPU instances is a breeze with our platform. You have the flexibility to deploy, modify, and rescale your instances according to your needs, without any hassle. Outstanding machine learning performance without breaking the bank. The latest technology at a significantly lower cost. Cutting-edge GPUs are designed to meet the demands of your workloads. Gain access to computational resources that are tailored to suit the intricacies of your models. Leverage our infrastructure to perform large-scale inference and access necessary libraries with our OblivusAI OS. Unleash the full potential of your gaming experience by utilizing our robust infrastructure to play games in the settings of your choice.

Hyperstack is the ultimate self-service, on-demand GPUaaS Platform offering the H100, A100, L40 and more, delivering its services to some of the most promising AI start-ups in the world. Hyperstack is built for enterprise-grade GPU-acceleration and optimised for AI workloads, offering NexGen Cloud’s enterprise-grade infrastructure to a wide spectrum of users, from SMEs to Blue-Chip corporations, Managed Service Providers, and tech enthusiasts. Running on 100% renewable energy and powered by NVIDIA architecture, Hyperstack offers its services at up to 75% more cost-effective than Legacy Cloud Providers. The platform supports a diverse range of high-intensity workloads, such as Generative AI, Large Language Modelling, machine learning, and rendering.

With Mystic you can deploy ML in your own Azure/AWS/GCP account or deploy in our shared GPU cluster. All Mystic features are directly in your own cloud. In a few simple steps, you get the most cost-effective and scalable way of running ML inference. Our shared cluster of GPUs is used by 100s of users simultaneously. Low cost but performance will vary depending on real-time GPU availability. Good AI products need good models and infrastructure; we solve the infrastructure part. A fully managed Kubernetes platform that runs in your own cloud. Open-source Python library and API to simplify your entire AI workflow. You get a high-performance platform to serve your AI models. Mystic will automatically scale up and down GPUs depending on the number of API calls your models receive. You can easily view, edit, and monitor your infrastructure from your Mystic dashboard, CLI, and APIs.

Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.

Scale your machine learning development from research to production with an end-to-end solution that gives your data science team all the tools they need in one place. As the leading data science platform for MLOps and model management, cnvrg.io is a pioneer in building cutting-edge machine learning development solutions so you can build high-impact machine learning models in half the time. Bridge science and engineering teams in a clear and collaborative machine learning management environment. Communicate and reproduce results with interactive workspaces, dashboards, dataset organization, experiment tracking and visualization, a model repository and more. Focus less on technical complexity and more on building high impact ML models. Cnvrg.io container-based infrastructure helps simplify engineering heavy tasks like tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment.

D-I-Y has its place, like weed control. Building out your AI infrastructure is another story. ONTAP AI consolidates a data center’s worth of analytics, training, and inference compute into a single, 5-petaflop AI system. Powered by NVIDIA DGX™ systems and NetApp cloud-connected all-flash storage, NetApp ONTAP AI helps you fully realize the promise of AI and deep learning (DL). You can simplify, accelerate, and integrate your data pipeline with the ONTAP AI proven architecture. Streamline the flow of data reliably and speed up analytics, training, and inference with your data fabric that spans from edge to core to cloud. NetApp ONTAP AI is one of the first converged infrastructure stacks to incorporate NVIDIA DGX A100, the world’s first 5-petaflop AI system, and NVIDIA Mellanox® high-performance Ethernet switches. You get unified AI workloads, simplified deployment, and fast return on investment.

Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale.

Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.

Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.

Amazon SageMaker allows you to identify raw data such as images, text files, and videos; add informative labels and generate labeled synthetic data to create high-quality training data sets for your machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which give you the flexibility to use an expert workforce to create and manage data labeling workflows on your behalf or manage your own data labeling workflows. data labeling. If you want the flexibility to create and manage your own personal and data labeling workflows, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes data labeling easy and gives you the option of using human annotators via Amazon Mechanical Turk, third-party providers, or your own private staff.

Watsonx is our upcoming enterprise-ready AI and data platform designed to multiply the impact of AI across your business. The platform comprises three powerful components: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability. Watsonx is our enterprise-ready AI and data platform designed to multiply the impact of AI across your business. The platform comprises three powerful products: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose data store, built on an open lakehouse architecture; and the watsonx.governance toolkit, to accelerate AI workflows that are built with responsibility, transparency and explainability.