PyTorch Integrations

Dataoorts GPU Cloud was built for AI. Dataoorts offers GC2 and a X-Series GPU instance to help you excel in your development tasks. Dataoorts GPU instances ensure that computational power is available to everyone, everywhere. Dataoorts can help you with your training, scaling and deployment tasks. Serverless computing allows you to create your own inference endpoint API cost you just $5 Per month.

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.

Cyfuture Cloud is a top cloud service provider offering reliable, scalable, and secure cloud solutions. With a focus on innovation and customer satisfaction, Cyfuture Cloud provides a wide range of services, including public, private, and hybrid cloud solutions, cloud storage, GPU cloud server, and disaster recovery. One of the key offering of Cyfuture Cloud include GPU cloud server. These servers are perfect for intensive tasks like artificial intelligence, machine learning, and big data analytics. The platform offers various tools and services for building and deploying machine learning and other GPU-accelerated applications. Moreover, Cyfuture Cloud helps businesses process complex data sets faster and more accurately, keeping them ahead of the competition. With robust infrastructure, expert support, and flexible pricing--Cyfuture Cloud is the ideal choice for businesses looking to leverage cloud computing for growth and innovation.

FakeYou deep fake technology allows you to communicate with your favorite characters. FakeYou is just one component in a wide range of creative and production tools. Your brain was already capable to imagine things being said in other people's voice. This is a sign of how far computers have advanced. Computers will one day be able bring all the vivid imagery and rich details of your dreams and hopes to life. There has never been a better moment to be creative in history. The technology to clone vocals is already available, and the voices are built by a community. This is not a unique website. Many people are producing similar results at home, independently of ours. You can find thousands of examples on YouTube or social media. We are looking for talented musicians and voice actors to help us create commercial-friendly AI voices.

AWS offers a wide range of services, including database storage, compute power, content delivery, and other functionality. This allows you to build complex applications with greater flexibility, scalability, and reliability. Amazon Web Services (AWS), the world's largest and most widely used cloud platform, offers over 175 fully featured services from more than 150 data centers worldwide. AWS is used by millions of customers, including the fastest-growing startups, large enterprises, and top government agencies, to reduce costs, be more agile, and innovate faster. AWS offers more services and features than any other cloud provider, including infrastructure technologies such as storage and databases, and emerging technologies such as machine learning, artificial intelligence, data lakes, analytics, and the Internet of Things. It is now easier, cheaper, and faster to move your existing apps to the cloud.

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Microsoft Azure is a cloud computing platform that allows you to quickly develop, test and manage applications. Azure. Invent with purpose. With more than 100 services, you can turn ideas into solutions. Microsoft continues to innovate to support your development today and your product visions tomorrow. Open source and support for all languages, frameworks and languages allow you to build what you want and deploy wherever you want. We can meet you at the edge, on-premises, or in the cloud. Services for hybrid cloud enable you to integrate and manage your environments. Secure your environment from the ground up with proactive compliance and support from experts. This is a trusted service for startups, governments, and enterprises. With the numbers to prove it, the cloud you can trust.

Select the subset of data that has the greatest impact on the accuracy of your model. This allows you to improve your model by using the best data in retraining. Reduce data redundancy and bias and focus on edge cases to get the most from your data. Lightly's algorithms are capable of processing large amounts of data in less than 24 hour. Connect Lightly with your existing buckets to process new data automatically. Our API automates the entire data selection process. Use the latest active learning algorithms. Combining active- and selfsupervised learning algorithms lightly for data selection. Combining model predictions, embeddings and metadata will help you achieve your desired distribution of data. Improve your model's performance by understanding data distribution, bias and edge cases. Manage data curation and keep track of the new data for model training and labeling. Installation is easy via a Docker Image and cloud storage integration. No data leaves your infrastructure.

Alibaba Cloud is a business unit of Alibaba Group (NYSE : BABA). It provides a complete suite of cloud computing services that can be used to power both international customers' online businesses as well as Alibaba Group's own ecommerce ecosystem. In January 2017, Alibaba Cloud was made the official Cloud Services Partner by the International Olympic Committee. We are constantly working towards our vision to make it easier to do business with anyone, anywhere in the world, by leveraging and improving the latest cloud technology. Alibaba Cloud offers cloud computing services to large and small businesses, individuals, and the public sector in more than 200 countries and regions.

ProActive Parallel Suite, a member of the OW2 Open Source Community for acceleration and orchestration, seamlessly integrated with the management and operation of high-performance Clouds (Private, Public with bursting capabilities). ProActive Parallel Suite platforms offer high-performance workflows and application parallelization, enterprise Scheduling & Orchestration, and dynamic management of private Heterogeneous Grids & Clouds. Our users can now simultaneously manage their Enterprise Cloud and accelerate and orchestrate all of their enterprise applications with the ProActive platform.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

Explore a new library and dataset in a notebook. A 2orkflow automates preprocessing, training, and testing. A deployment brings your application to life. You can use notebooks, workflows, or deployments separately. Compatible with all. Gradient is compatible with all major frameworks. Gradient is powered with Paperspace's top-of-the-line GPU instances. Source control integration makes it easier to move faster. Connect to GitHub to manage your work and compute resources using git. In seconds, you can launch a GPU-enabled Jupyter Notebook directly from your browser. Any library or framework is possible. Invite collaborators and share a link. This cloud workspace runs on free GPUs. A notebook environment that is easy to use and share can be set up in seconds. Perfect for ML developers. This environment is simple and powerful with lots of features that just work. You can either use a pre-built template, or create your own. Get a free GPU

The workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone.

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.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

Giskard provides interfaces to AI & Business teams for evaluating and testing ML models using automated tests and collaborative feedback. Giskard accelerates teamwork to validate ML model validation and gives you peace-of-mind to eliminate biases, drift, or regression before deploying ML models into production.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

spaCy is designed for real work, real products and real insights. The library respects your time, and tries not to waste it. It is easy to install and the API is simple and efficient. spaCy excels in large-scale information extraction tasks. It is written in Cython, which is carefully managed for memory. SpaCy is the library to use if your application requires to process large web dumps. spaCy was released in 2015 and has been a industry standard with a large ecosystem. You can choose from a wide range of plugins and integrate them with your machine-learning stack to create custom components and workflows. You can use these components to recognize named entities, part-of speech tagging, dependency parsing and sentence segmentation. Easy extensible with custom components or attributes Model packaging, deployment, workflow management made easy.

Akira.ai delivers Agentic AI solutions that integrate autonomous AI agents into business processes to improve operational efficiency. These AI agents help automate tasks, generate insights, and assist with decision-making, thereby allowing teams to focus on strategic objectives. Akira’s platform seamlessly integrates with existing enterprise systems, optimizing workflows in industries ranging from manufacturing to telecom. By empowering organizations with AI-driven automation and real-time problem-solving capabilities, Akira fosters enhanced productivity, scalability, and faster decision-making.

Simplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

Select the configurations and resources required for specific code segments within your project. It only takes seconds to save and apply changes in a training scenario. Select the right configuration of computing resources to launch training models in a matter of seconds. All will be created automatically, without the need to manage infrastructure. Select a serverless or dedicated operating mode. All in one interface, manage project data, save to datasets and connect to databases, object storage or other repositories. Create a ML model with colleagues from around the world, share the project and set budgets across your organization. Launch your ML within minutes, without developers' help. Try out experiments with different models being published simultaneously.

CodeQwen, developed by the Qwen Team, Alibaba Cloud, is the code version. It is a transformer based decoder only language model that has been pre-trained with a large number of codes. A series of benchmarks shows that the code generation is strong and that it performs well. Supporting long context generation and understanding with a context length of 64K tokens. CodeQwen is a 92-language coding language that provides excellent performance for text-to SQL, bug fixes, and more. CodeQwen chat is as simple as writing a few lines of code using transformers. We build the tokenizer and model using pre-trained methods and use the generate method for chatting. The chat template is provided by the tokenizer. Following our previous practice, we apply the ChatML Template for chat models. The model will complete the code snippets in accordance with the prompts without any additional formatting.

Collimator is a simulation and modeling platform for hybrid dynamical system. Engineers can design and test complex, mission-critical systems in a reliable, secure, fast, and intuitive way with Collimator. Our customers are control system engineers from the electrical, mechanical, and control sectors. They use Collimator to improve productivity, performance, and collaborate more effectively. Our out-of-the-box features include an intuitive block diagram editor, Python blocks for developing custom algorithms, Jupyter notebooks for optimizing their systems, high performance computing in cloud, and role-based access controls.

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.

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.

Neptune.ai, a platform for machine learning operations, is designed to streamline tracking, organizing and sharing of experiments, and model-building. It provides a comprehensive platform for data scientists and machine-learning engineers to log, visualise, and compare model training run, datasets and hyperparameters in real-time. Neptune.ai integrates seamlessly with popular machine-learning libraries, allowing teams to efficiently manage research and production workflows. Neptune.ai's features, which include collaboration, versioning and reproducibility of experiments, enhance productivity and help ensure that machine-learning projects are transparent and well documented throughout their lifecycle.

One development environment for all data science workflows. Natively analyze your data without the need to switch between services. Data to training at scale Models can be built and trained 5X faster than traditional notebooks. Scale up model development using simple connectivity to Vertex AI Services. Access to data is simplified and machine learning is made easier with BigQuery Dataproc, Spark and Vertex AI integration. Vertex AI training allows you to experiment and prototype at scale. Vertex AI Workbench allows you to manage your training and deployment workflows for Vertex AI all from one location. Fully managed, scalable and enterprise-ready, Jupyter-based, fully managed, scalable, and managed compute infrastructure with security controls. Easy connections to Google Cloud's Big Data Solutions allow you to explore data and train ML models.

Coiled makes enterprise-grade Dask easy. Coiled manages Dask clusters within your AWS or GCP account. This makes it the easiest and most secure method to run Dask in production. Coiled manages your cloud infrastructure and can deploy to your AWS account or Google Cloud account in a matter of minutes. Coiled provides a solid deployment solution that requires little effort. You can customize the cluster node types to meet your analysis needs. Run Dask in Jupyter Notebooks to get real-time dashboards, cluster insights, and other useful information. You can easily create software environments with custom dependencies for your Dask analysis. Enjoy enterprise-grade security. SLAs, user level management, and auto-termination clusters reduce costs. Coiled makes it easy for you to deploy your cluster on AWS and GCP. It takes only minutes and requires no credit card. You can launch code from anywhere you like, including cloud services like AWS SageMaker and open source solutions like JupyterHub.

You can now build what took years. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy and maintain ML in production. Superwise integrates with any ML stack, and can connect to any number of communication tools. Want to go further? Superwise is API-first. All of our APIs allow you to access everything, and we mean everything. All this from the comfort of your cloud. You have complete control over ML monitoring. You can set up metrics and policies using our SDK and APIs. Or, you can simply choose a template to monitor and adjust the sensitivity, conditions and alert channels. Get Superwise or contact us for more information. Superwise's ML monitoring policy templates allow you to quickly create alerts. You can choose from dozens pre-built monitors, ranging from data drift and equal opportunity, or you can customize policies to include your domain expertise.

TorchMetrics contains over 90+ PyTorch metrics and an easy-to use API to create custom metrics. Standardized interface to improve reproducibility. It reduces boilerplate. distributed-training compatible. It has been thoroughly tested. Automatic accumulation of batches. Automatic synchronization between multiple devices. TorchMetrics can be used in any PyTorch model or within PyTorch Lightning for additional benefits. Your data will always be on the same device that your metrics. Lightning allows you to log Metric objects directly, which reduces the amount of boilerplate. Like torch.nn's, most metrics can be logged in Lightning with both a class-based or functional version. The functional versions perform the basic operations necessary to calculate each metric. They are simple python functions which take torch.tensors as input and return the corresponding metrics as torch.tensors. Nearly all functional metrics include a class-based counterpart.

A streaming database is designed to store, process, analyze, and ingest large data streams. It is a modern data infrastructure which unifies messaging, stream processing and storage to help you get the most out of your data in real time. Massive amounts of data are continuously ingested from many sources, including IoT device sensor sensors. A specially designed distributed streaming data storage cluster can store millions of data streams securely. Subscribe to HStreamDB topics to access data streams in real time as fast as Kafka. You can access and playback data streams at any time thanks to the permanent stream storage. Data streams can be processed based on event-time using the same SQL syntax that you use to query relational databases. SQL can be used to filter, transform and aggregate multiple data streams.

Empower your product with AI. Our platform provides a wide range of pre-built models, as well as a user-friendly interface with no-code for custom models. Integrate AI seamlessly into applications to gain a competitive advantage. Sentiment analysis is also known as opinion-mining. It is the process of extracting and categorizing subjective information from text, such as reviews, comments on social media, or customer feedback. In recent years, this technology has grown in importance as more companies use it to understand the opinions and needs of their customers and make data-driven decision that can improve products, services, or marketing strategies. Sentiment analysis helps companies to understand customer feedback, and make data-driven decision that can improve their products, service, and marketing strategies.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

Voxel51, the company behind FiftyOne is responsible for the open-source software that allows you to create better computer vision workflows through improving the quality of datasets and delivering insights into your models. Explore, search and slice your datasets. Find samples and labels quickly that match your criteria. FiftyOne offers tight integrations to public datasets such as COCO, Open Images and ActivityNet. You can also create your own datasets. Data quality is one of the most important factors that affect model performance. FiftyOne can help you identify, visualize and correct the failure modes of your model. Annotation errors lead to bad models. But finding mistakes manually is not scalable. FiftyOne automatically finds and corrects label mistakes, so you can curate better-quality datasets. Manual debugging and aggregate performance metrics don't scale. Use the FiftyOne Brain for edge cases, new samples to train on, and more.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.

Learn how to choose the right LLM based on your needs, and how you can optimize quality, speed and cost-efficiency. With a single API and standard API, you can access all LLMs from all providers. Set your own constraints for output speed, latency and cost. Define your own quality metric. Personalize your router for your requirements. Send your queries to the fastest providers based on the latest benchmark data for the region you are in, updated every 10 minutes. Unify's dedicated walkthrough will help you get started. Discover the features that you already have and our upcoming roadmap. Create a Unify Account to access all models supported by all providers using a single API Key. Our router balances output speed, quality, and cost according to user preferences. The quality of the output is predicted using a neural scoring system, which predicts each model's ability to respond to a given prompt.

CometLLM allows you to visualize and log your LLM chains and prompts. CometLLM can be used to identify effective prompting strategies, streamline troubleshooting and ensure reproducible workflows. Log your prompts, responses, variables, timestamps, duration, and metadata. Visualize your responses and prompts in the UI. Log your chain execution to the level you require. Visualize your chain in the UI. OpenAI chat models automatically track your prompts. Track and analyze feedback from users. Compare your prompts in the UI. Comet LLM Projects are designed to help you perform smart analysis of logged prompt engineering workflows. Each column header corresponds with a metadata attribute that was logged in the LLM Project, so the exact list can vary between projects.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Vector search can give you a competitive edge. Streamline your AI/ML workflows, reduce costs and stay ahead with up to a 10x faster time-to market. ApertureDB’s unified multimodal management of data will free your AI teams from data silos and allow them to innovate. Setup and scale complex multimodal infrastructure for billions objects across your enterprise in days instead of months. Unifying multimodal data with advanced vector search and innovative knowledge graph, combined with a powerful querying engine, allows you to build AI applications at enterprise scale faster. ApertureDB will increase the productivity of your AI/ML team and accelerate returns on AI investment by using all your data. You can try it for free, or schedule a demonstration to see it in action. Find relevant images using labels, geolocation and regions of interest. Prepare large-scale, multi-modal medical scanning for ML and Clinical studies.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

Guild AI is a free, open-source toolkit for experiment tracking. It allows users to build faster and better models by bringing systematic control to machine-learning workflows. It captures all details of training runs and treats them as unique experiments. This allows for comprehensive tracking and analysis. Users can compare and analyse runs to improve their understanding and incrementally enhance models. Guild AI simplifies hyperparameter optimization by applying state-of the-art algorithms via simple commands, eliminating complex trial setups. It also supports pipeline automation, accelerating model creation, reducing errors and providing measurable outcomes. The toolkit runs on all major operating system platforms and integrates seamlessly with existing software engineering applications. Guild AI supports a variety of remote storage types including Amazon S3, Google Cloud Storage and Azure Blob Storage.

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

NeevCloud offers cutting-edge GPU cloud services powered by NVIDIA GPUs such as the H200, GB200 NVL72 and others. These GPUs offer unmatched performance in AI, HPC and data-intensive workloads. Flexible pricing and energy-efficient graphics cards allow you to scale dynamically, reducing costs while increasing output. NeevCloud is ideal for AI model training and scientific research. It also ensures seamless integration, global accessibility, and media production. NeevCloud GPU Cloud Solutions offer unparalleled speed, scalability and sustainability.

Our platform allows you to create AI products, train, fine-tune, and deploy models on the cloud. You don't have to worry about scaling, infrastructure, cost management, or other technical issues. Prebuilt, fully customizable modular components make it easy to train, fine tune, and deploy models. The science, not the engineering, should be your focus. Lightning components organize code to run on the cloud and manage its own infrastructure, cloud cost, and other details. 50+ optimizations to lower cloud cost and deliver AI in weeks, not months. Enterprise-grade control combined with consumer-level simplicity allows you to optimize performance, reduce costs, and take on less risk. Get more than a demo. In days, not months, you can launch your next GPT startup, diffusion startup or cloud SaaSML service.

Data scientists and developers can collaborate on ML projects by empowering them. Before publishing to end-users, build, load, optimize, and test models and their integrations. Data science workload can be reduced and decision-making improved by sharing and storing ML models throughout the organization. Publish updates to automatically push any changes to production models. ML-powered insights can be instantly provided within any web-based business app to increase efficiency and boost productivity. Our browser extension allows analysts and business users to seamlessly integrate models into any web application using drag-and-drop.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

MLReef allows domain experts and data scientists secure collaboration via a hybrid approach of pro-code and no-code development. Distributed workloads lead to a 75% increase in productivity. This allows teams to complete more ML project faster. Domain experts and data scientists can collaborate on the same platform, reducing communication ping-pong to 100%. MLReef works at your location and enables you to ensure 100% reproducibility and continuity. You can rebuild all work at any moment. To create interoperable, versioned, explorable AI modules, you can use git repositories that are already well-known. Your data scientists can create AI modules that you can drag and drop. These modules can be modified by parameters, ported, interoperable and explorable within your organization. Data handling requires a lot of expertise that even a single data scientist may not have. MLReef allows your field experts to assist you with data processing tasks, reducing complexity.