Alternatives to Google Cloud Vertex AI Workbench

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.

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

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.

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds create deployable artifacts. Artifacts built can be reused and deployed at any time. Sometimes, however, it is not enough to deploy the artifact. Qwak allows data scientists and engineers to see how a build was made and then reproduce it when necessary. Models can contain multiple variables. The data models were trained using the hyper parameter and different source code.

Founded in 2018, Pecan is a predictive analytics platform that leverages its pioneering Predictive GenAI to remove barriers to AI adoption, making predictive modeling accessible to all data and business teams. Guided by generative AI, companies can obtain precise predictions across various business domains without the need for specialized personnel. Predictive GenAI enables rapid model definition and training, while automated processes accelerate AI implementation. With Pecan's fusion of predictive and generative AI, realizing the business impact of AI is now far faster and easier.

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.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

With a single command, you can train and serve large AI models in scale. You can simply point to your S3 bucket. We take care of the rest: orchestration, efficiency and node failures. Simple and scalable. MosaicML allows you to train and deploy large AI model on your data in a secure environment. Keep up with the latest techniques, recipes, and foundation models. Our research team has developed and rigorously tested these recipes. In just a few easy steps, you can deploy your private cloud. Your data and models will never leave the firewalls. You can start in one cloud and continue in another without missing a beat. Own the model trained on your data. Model decisions can be better explained by examining them. Filter content and data according to your business needs. Integrate seamlessly with your existing data pipelines and experiment trackers. We are cloud-agnostic and enterprise-proven.

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Create, design, prototype and deploy custom AI workflows. Enhance customer engagement and team/personal productivity. Create and embed your website in just minutes. Connect your chatbot to your knowledge base. Instantly summarize and answer questions about audio, video, and website files. Create marketing copy, personalized emails, call summaries and graphics at large scale. Save time with a library of prebuilt pipelines, such as those for chatbots or document search. Share your pipelines to help the marketplace grow. Your data will not be stored on model providers' servers due to our zero-day retention policy and secure infrastructure. Our partnership begins with a free diagnostic, where we assess if your organization is AI-ready. We then create a roadmap to create a turnkey solution that fits into your processes.

Watsonx is a new enterprise-ready AI platform that will multiply the impact of AI in your business. The platform consists of three powerful components, including the watsonx.ai Studio for new foundation models, machine learning, and generative AI; the watsonx.data Fit-for-Purpose Store for the flexibility and performance of a warehouse; and the watsonx.governance Toolkit to enable AI workflows built with responsibility, transparency, and explainability. The foundation models allow AI to be fine-tuned to the unique data and domain expertise of an enterprise with a specificity previously impossible. Use all your data, no matter where it is located. Take advantage of a hybrid cloud infrastructure that provides the foundation data for extending AI into your business. Improve data access, implement governance, reduce costs, and put quality models into production quicker.

You can easily build, deploy, manage, and monitor computer vision applications using a fully managed, end to end application development environment. This reduces the time it takes to build computer vision apps from days to minutes, at a fraction of the cost of current offerings. You can quickly and easily ingest real-time video streams and images on a global scale. Drag-and-drop interface makes it easy to create computer vision applications. With built-in AI capabilities, you can store and search petabytes worth of data. Vertex AI Vision provides all the tools necessary to manage the lifecycle of computer vision applications. This includes ingestion, analysis and storage, as well as deployment. Connect application output to a data destination such as BigQuery for analytics or live streaming to drive business actions. You can import thousands of video streams from all over the world. Enjoy a monthly pricing structure that allows you to enjoy up-to one-tenth less than the previous offerings.

AutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool.

Amazon SageMaker offers all the tools and libraries needed to build ML models. It allows you to iteratively test different algorithms and evaluate their accuracy to determine the best one for you. Amazon SageMaker allows you to choose from over 15 algorithms that have been optimized for SageMaker. You can also access over 150 pre-built models available from popular model zoos with just a few clicks. SageMaker offers a variety model-building tools, including RStudio and Amazon SageMaker Studio Notebooks. These allow you to run ML models on a small scale and view reports on their performance. This allows you to create high-quality working prototypes. Amazon SageMaker Studio Notebooks make it easier to build ML models and collaborate with your team. Amazon SageMaker Studio notebooks allow you to start working in seconds with Jupyter notebooks. Amazon SageMaker allows for one-click sharing of notebooks.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

NVIDIA AI Enterprise is the software layer of NVIDIA AI Platform. It accelerates the data science pipeline, streamlines development and deployments of production AI including generative AI, machine vision, speech AI, and more. NVIDIA AI Enterprise has over 50 frameworks, pre-trained models, and development tools. It is designed to help enterprises get to the forefront of AI while simplifying AI to make it more accessible to all. Artificial intelligence and machine learning are now mainstream and a key part of every company's competitive strategy. Enterprises face the greatest challenges when it comes to managing siloed infrastructure in the cloud and on-premises. AI requires that their environments be managed as a single platform and not as isolated clusters of compute.

The NeoPulse Product Suite contains everything a company needs to begin building custom AI solutions using their own curated data. Server application that uses a powerful AI called "the Oracle" to automate the creation of sophisticated AI models. Manages your AI infrastructure, and orchestrates workflows for automating AI generation activities. A program that has been licensed by an organization to allow any application within the enterprise to access the AI model via a web-based (REST API). NeoPulse, an automated AI platform, enables organizations to deploy, manage and train AI solutions in heterogeneous environments. NeoPulse can handle all aspects of the AI engineering workflow: design, training, deployment, managing, and retiring.

The first hardware and software computing protocol that integrates both to train and fine tune your AI models. Reduce your training costs up to 80%. Deploy your model in seconds using open-source template models or bring your model. Debug containers easily with GPU, CPU and Memory metrics. You can monitor logs live. You can track all models and training set with cryptographic proofs to ensure complete accountability. You can control the entire training process with just a few commands. You can earn block rewards by adding your computer to the networking. Track key metrics like connectivity and uptime.

Ray's creators have created a fully-managed platform. The best way to create, scale, deploy, and maintain AI apps on Ray. You can accelerate development and deployment of any AI app, at any scale. Ray has everything you love, but without the DevOps burden. Let us manage Ray for you. Ray is hosted on our cloud infrastructure. This allows you to focus on what you do best: creating great products. Anyscale automatically scales your infrastructure to meet the dynamic demands from your workloads. It doesn't matter if you need to execute a production workflow according to a schedule (e.g. Retraining and updating a model with new data every week or running a highly scalable, low-latency production service (for example. Anyscale makes it easy for machine learning models to be served in production. Anyscale will automatically create a job cluster and run it until it succeeds.

Amazon SageMaker Model training reduces the time and costs of training and tuning machine learning (ML), models at scale, without the need for infrastructure management. SageMaker automatically scales infrastructure up or down from one to thousands of GPUs. This allows you to take advantage of the most performant ML compute infrastructure available. You can control your training costs better because you only pay for what you use. SageMaker distributed libraries can automatically split large models across AWS GPU instances. You can also use third-party libraries like DeepSpeed, Horovod or Megatron to speed up deep learning models. You can efficiently manage your system resources using a variety of GPUs and CPUs, including P4d.24xl instances. These are the fastest training instances available in the cloud. Simply specify the location of the data and indicate the type of SageMaker instances to get started.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

Platform with NVIDIA H100 Tensor core GPUs. Competitive pricing. Support from a dedicated team. Built for large-scale ML workloads. Get the most from multihost training with thousands of H100 GPUs in full mesh connections using the latest InfiniBand networks up to 3.2Tb/s. Best value: Save up to 50% on GPU compute when compared with major public cloud providers*. You can save even more by purchasing GPUs in large quantities and reserving GPUs. Onboarding assistance: We provide a dedicated engineer to ensure smooth platform adoption. Get your infrastructure optimized, and k8s installed. Fully managed Kubernetes - Simplify the deployment and scaling of ML frameworks using Kubernetes. Use Managed Kubernetes to train GPUs on multiple nodes. Marketplace with ML Frameworks: Browse our Marketplace to find ML-focused libraries and applications, frameworks, and tools that will streamline your model training. Easy to use. All new users are entitled to a one-month free trial.

AI Cloud is a new approach that addresses the challenges and opportunities presented by AI today. A single system of records that accelerates the delivery of AI to production in every organization. All users can collaborate in a single environment that optimizes the entire AI lifecycle. The AI Catalog facilitates seamlessly finding, sharing and tagging data. This helps to increase collaboration and speed up time to production. The catalog makes it easy to find the data you need to solve a business problem. It also ensures security, compliance, consistency, and consistency. Contact Support if your database is protected by a network rule that allows connections only from certain IP addresses. An administrator will need to add addresses to your whitelist.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

NVIDIA Base Command™, Platform is a software platform for enterprise-class AI training. It enables businesses and data scientists to accelerate AI developments. Base Command Platform is part of NVIDIA DGX™. It provides centralized, hybrid management of AI training projects. It can be used with NVIDIA DGX Cloud or NVIDIA DGX SUPERPOD. The Base Command Platform is combined with NVIDIA-accelerated AI infrastructure to provide a cloud-hosted solution that allows users to avoid the overheads and pitfalls of setting up and maintaining a do it yourself platform. Base Command Platform efficiently configures, manages, and executes AI workloads. It also provides integrated data management and executions on the right-sized resources, whether they are on-premises or cloud. The platform is continuously updated by NVIDIA's engineers and researchers.

We are ready to meet all your business needs, whether it is quick engineering, fine-tuning or training. The Together Inference API makes it easy to integrate your new model in your production application. Together AI's elastic scaling and fastest performance allows it to grow with you. To increase accuracy and reduce risks, you can examine how models are created and what data was used. You are the owner of the model that you fine-tune and not your cloud provider. Change providers for any reason, even if the price changes. Store data locally or on our secure cloud to maintain complete data privacy.

Build, deploy and scale AI applications from end-to-end in the cloud. Griptape provides developers with everything they need from the development framework up to the execution runtime to build, deploy and scale retrieval driven AI-powered applications. Griptape, a Python framework that is modular and flexible, allows you to build AI-powered apps that securely connect with your enterprise data. It allows developers to maintain control and flexibility throughout the development process. Griptape Cloud hosts your AI structures whether they were built with Griptape or another framework. You can also call directly to LLMs. To get started, simply point your GitHub repository. You can run your hosted code using a basic API layer, from wherever you are. This will allow you to offload the expensive tasks associated with AI development. Automatically scale your workload to meet your needs.

Your platform for developing generative AI and custom copilots. Use pre-built and customizable AI model on your data to build solutions faster. Explore a growing collection of models, both open-source and frontier-built, that are pre-built and customizable. Create AI models using a code first experience and an accessible UI validated for accessibility by developers with disabilities. Integrate all your OneLake data into Microsoft Fabric. Integrate with GitHub codespaces, Semantic Kernel and LangChain. Build apps quickly with prebuilt capabilities. Reduce wait times by personalizing content and interactions. Reduce the risk for your organization and help them discover new things. Reduce the risk of human error by using data and tools. Automate operations so that employees can focus on more important tasks.

AWS Trainium, the second-generation machine-learning (ML) accelerator, is specifically designed by AWS for deep learning training with 100B+ parameter model. Each Amazon Elastic Comput Cloud (EC2) Trn1 example deploys up to sixteen AWS Trainium accelerations to deliver a low-cost, high-performance solution for deep-learning (DL) in the cloud. The use of deep-learning is increasing, but many development teams have fixed budgets that limit the scope and frequency at which they can train to improve their models and apps. Trainium based EC2 Trn1 instance solves this challenge by delivering a faster time to train and offering up to 50% savings on cost-to-train compared to comparable Amazon EC2 instances.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

Amazon SageMaker Clarify is a machine learning (ML), development tool that provides purpose-built tools to help them gain more insight into their ML training data. SageMaker Clarify measures and detects potential bias using a variety metrics so that ML developers can address bias and explain model predictions. SageMaker Clarify detects potential bias in data preparation, model training, and in your model. You can, for example, check for bias due to age in your data or in your model. A detailed report will quantify the different types of possible bias. SageMaker Clarify also offers feature importance scores that allow you to explain how SageMaker Clarify makes predictions and generates explainability reports in bulk. These reports can be used to support internal or customer presentations and to identify potential problems with your model.

Tencent Cloud TI Platform, a machine learning platform for AI engineers, is a one stop shop. It supports AI development at every stage, from data preprocessing, to model building, to model training, to model evaluation, as well as model service. It is preconfigured with diverse algorithms components and supports multiple algorithm frameworks for adapting to different AI use-cases. Tencent Cloud TI Platform offers a machine learning experience in a single-stop shop. It covers a closed-loop workflow, from data preprocessing, to model building, training and evaluation. Tencent Cloud TI Platform allows even AI beginners to have their models automatically constructed, making the entire training process much easier. Tencent Cloud TI Platform’s auto-tuning feature can also improve the efficiency of parameter optimization. Tencent Cloud TI Platform enables CPU/GPU resources that can elastically respond with flexible billing methods to different computing power requirements.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

Machine learning has led to business and research breakthroughs in everything from network security to medical diagnosis. To make similar breakthroughs possible, we created the Tensor Processing unit (TPU). Cloud TPU is a custom-designed machine learning ASIC which powers Google products such as Translate, Photos and Search, Assistant, Assistant, and Gmail. Here are some ways you can use the TPU and machine-learning to accelerate your company's success, especially when it comes to scale. Cloud TPU is designed for cutting-edge machine learning models and AI services on Google Cloud. Its custom high-speed network provides over 100 petaflops performance in a single pod. This is enough computational power to transform any business or create the next breakthrough in research. It is similar to compiling code to train machine learning models. You need to update frequently and you want to do it as efficiently as possible. As apps are built, deployed, and improved, ML models must be trained repeatedly.

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

Make AI an integral part of your business. With the flexibility and power of a cloud native platform, you can build better, deploy faster and audit easier. Import models and search cataloged model from across your organization. Save time by quickly annotating data with model-in the-loop hinting. Flyte's integration with Chariot allows you to quickly create and launch custom workflows. Understand the full origin of your data, models and workflows. Deploy models wherever you need them. This includes edge and IoT applications. Data scientists are not the only ones who can get valuable insights from their data. With Chariot's low code interface, teams can collaborate effectively.

Unstructured data is the future for AI. This future is now possible. Build an ML/AI pipeline to scale all your unstructured data from one place. Graviti allows you to use better data to create better models. Learn about Graviti, the data platform that allows AI developers to manage, query and version control unstructured data. Quality data is no longer an expensive dream. All your metadata, annotations, and predictions can be managed in one place. You can customize filters and see the results of filtering to find the data that meets your needs. Use a Git-like system to manage data versions and collaborate. Role-based access control allows for safe and flexible team collaboration. Graviti's built in marketplace and workflow creator makes it easy to automate your data pipeline. No more grinding, you can quickly scale up to rapid model iterations.

IBM Watson OpenScale provides visibility into the creation and use of AI-powered applications in an enterprise-scale environment. It also allows businesses to see how ROI is delivered. IBM Watson OpenScale provides visibility to companies about how AI is created, used, and how ROI is delivered at business level. You can create and deploy trusted AI using the IDE you prefer, and provide data insights to your business and support team about how AI affects business results. Capture payload data, deployment output, and alerts to monitor the health of business applications. You can also access an open data warehouse for custom reporting and access to operations dashboards. Based on business-determined fairness attributes, automatically detects when artificial Intelligence systems produce incorrect results at runtime. Smart recommendations of new data to improve model training can reduce bias.

Amazon SageMaker JumpStart can help you speed up your machine learning (ML). SageMaker JumpStart gives you access to pre-trained foundation models, pre-trained algorithms, and built-in algorithms to help you with tasks like article summarization or image generation. You can also access prebuilt solutions to common problems. You can also share ML artifacts within your organization, including notebooks and ML models, to speed up ML model building. SageMaker JumpStart offers hundreds of pre-trained models from model hubs such as TensorFlow Hub and PyTorch Hub. SageMaker Python SDK allows you to access the built-in algorithms. The built-in algorithms can be used to perform common ML tasks such as data classifications (images, text, tabular), and sentiment analysis.

Optimize ML models with real-time training metrics capture and alerting when anomalies are detected. To reduce the time and costs of training ML models, stop training when the desired accuracy has been achieved. To continuously improve resource utilization, automatically profile and monitor the system's resource utilization. Amazon SageMaker Debugger reduces troubleshooting time from days to minutes. It automatically detects and alerts you when there are common errors in training, such as too large or too small gradient values. You can view alerts in Amazon SageMaker Studio, or configure them through Amazon CloudWatch. The SageMaker Debugger SDK allows you to automatically detect new types of model-specific errors like data sampling, hyperparameter value, and out-of bound values.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

Self-service machine learning platform that allows you to turn models into APIs with just a few mouse clicks. Sign up for a Deep Infra Account using GitHub, or login using GitHub. Choose from hundreds of popular ML models. Call your model using a simple REST API. Our serverless GPUs allow you to deploy models faster and cheaper than if you were to build the infrastructure yourself. Depending on the model, we have different pricing models. Some of our models have token-based pricing. The majority of models are charged by the time it takes to execute an inference. This pricing model allows you to only pay for the services you use. You can easily scale your business as your needs change. There are no upfront costs or long-term contracts. All models are optimized for low latency and inference performance on A100 GPUs. Our system will automatically scale up the model based on your requirements.

AIxBlock is an end-to-end blockchain-based platform for AI that harnesses unused computing resources of BTC miners, as well as all global consumer GPUs. Our platform's training method is a hybrid machine learning approach that allows simultaneous training on multiple nodes. We use the DeepSpeed-TED method, a three-dimensional hybrid parallel algorithm which integrates data, tensor and expert parallelism. This allows for the training of Mixture of Experts models (MoE) on base models that are 4 to 8x larger than the current state of the art. The platform will identify and add compatible computing resources from the computing marketplace to the existing cluster of training nodes, and distribute the ML model for unlimited computations. This process unfolds dynamically and automatically, culminating in decentralized supercomputers which facilitate AI success.

Amazon SageMaker Studio Lab provides a free environment for machine learning (ML), which includes storage up to 15GB and security. Anyone can use it to learn and experiment with ML. You only need a valid email address to get started. You don't have to set up infrastructure, manage access or even sign-up for an AWS account. SageMaker Studio Lab enables model building via GitHub integration. It comes preconfigured and includes the most popular ML tools and frameworks to get you started right away. SageMaker Studio Lab automatically saves all your work, so you don’t have to restart between sessions. It's as simple as closing your computer and returning later. Machine learning development environment free of charge that offers computing, storage, security, and the ability to learn and experiment using ML. Integration with GitHub and preconfigured to work immediately with the most popular ML frameworks, tools, and libraries.

Build an AI-powered machine-learning engineer to handle all of your data science and ML requirements. Xero’s artificial analyst is the next step in data science and ML. Ask Xara to do something with your data. Explore your data, create custom visuals and generate insights using natural language. Cleanse and transform your data to extract new features as seamlessly as possible. XARA allows you to create, train and test machine learning models that are completely customizable.