Alternatives to Cerebrium

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.

Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness.

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.

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.

Daria's advanced automated features enable users to quickly and easily create predictive models. This significantly reduces the time and effort required to build them. Eliminate technological and financial barriers to building AI systems from scratch for businesses. Automated machine learning for data professionals can streamline and speed up workflows, reducing the amount of iterative work required. An intuitive GUI for data science beginners gives you hands-on experience with machine learning. Daria offers various data transformation functions that allow you to quickly create multiple feature sets. Daria automatically searches through millions of combinations of algorithms, modeling techniques, and hyperparameters in order to find the best predictive model. Daria's RESTful API allows you to deploy predictive models directly into production.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

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.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.

It's easy to build advanced predictive models with no math or coding in just a few clicks. The Complete Experience We provide everything you need, from cleaning data to building models to forecasting, and everything in between. Trustworthy. Learn the "why" behind AI decisions to drive business with actionable insight. Data Analytics in minutes without having to write code. In one click, you can complete the entire process of building ML algorithms, explaining results and predicting future outcomes. Machine Learning in 3 Steps You can go from raw data to actionable insights without writing a single line code. Upload your data. Ask questions in plain English Find the best model for your data. Share your results. Increase customer productivity We assist companies to use no code Machine Learning to improve their Customer Experience.

Observability allows you to detect data issues and ML problems faster, to deliver continuous improvements and to avoid costly incidents. Start with reliable data. Monitor data in motion for quality issues. Pinpoint data and models drift. Identify the training-serving skew, and proactively retrain. Monitor key performance metrics continuously to detect model accuracy degradation. Identify and prevent data leakage in generative AI applications. Protect your generative AI apps from malicious actions. Improve AI applications by using user feedback, monitoring and cross-team collaboration. Integrate in just minutes with agents that analyze raw data, without moving or replicating it. This ensures privacy and security. Use the proprietary privacy-preserving technology to integrate the WhyLabs SaaS Platform with any use case. Security approved by healthcare and banks.

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.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

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.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Deeploy allows you to maintain control over your ML models. You can easily deploy your models to our responsible AI platform without compromising transparency, control and compliance. Transparency, explainability and security of AI models are more important today than ever. You can monitor the performance of your models with confidence and accountability if you use a safe, secure environment. Over the years, our experience has shown us the importance of human interaction with machine learning. Only when machine-learning systems are transparent and accountable can experts and consumers provide feedback, overrule their decisions when necessary, and grow their trust. We created Deeploy for this reason.

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.

AI is today blocked by a lack of labeled data. Not models. The first data-centric AI platform powered by a programmatic approach will unblock AI. With its unique programmatic approach, Snorkel AI is leading a shift from model-centric AI development to data-centric AI. By replacing manual labeling with programmatic labeling, you can save time and money. You can quickly adapt to changing data and business goals by changing code rather than manually re-labeling entire datasets. Rapid, guided iteration of the training data is required to develop and deploy AI models of high quality. Versioning and auditing data like code leads to faster and more ethical deployments. By collaborating on a common interface, which provides the data necessary to train models, subject matter experts can be integrated. Reduce risk and ensure compliance by labeling programmatically, and not sending data to external annotators.

Openlayer will accept your data and models. Work with the team to align performance and quality expectations. You can quickly identify the reasons behind failed goals and find a solution. You have all the information you need to diagnose problems. Retrain the model by generating more data that looks similar to the subpopulation. Test new commits in relation to your goals, so that you can ensure a systematic progress without regressions. Compare versions side by side to make informed decisions. Ship with confidence. Save time on engineering by quickly determining what drives model performance. Find the quickest ways to improve your model. Focus on cultivating high quality and representative datasets and knowing the exact data required to boost model performance.

PredictSense is an AI-powered machine learning platform that uses AutoML to power its end-to-end Machine Learning platform. Accelerating machine intelligence will fuel the technological revolution of tomorrow. AI is key to unlocking the value of enterprise data investments. PredictSense allows businesses to quickly create AI-driven advanced analytical solutions that can help them monetize their technology investments and critical data infrastructure. Data science and business teams can quickly develop and deploy robust technology solutions at scale. Integrate AI into your existing product ecosystem and quickly track GTM for new AI solution. AutoML's complex ML models allow you to save significant time, money and effort.

A platform that offers a variety of machine learning algorithms to meet data mining and analysis needs. Machine Learning Platform for AI offers end-to-end machine-learning services, including data processing and feature engineering, model prediction, model training, model evaluation, and model prediction. Machine learning platform for AI integrates all these services to make AI easier than ever. Machine Learning Platform for AI offers a visual web interface that allows you to create experiments by dragging components onto the canvas. Machine learning modeling is a step-by-step process that improves efficiency and reduces costs when creating experiments. Machine Learning Platform for AI offers more than 100 algorithm components. These include text analysis, finance, classification, clustering and time series.

Enterprise AI applications can be built, deployed, and operated. C3 AI®, Suite uses a unique model driven architecture to speed delivery and reduce the complexity of developing enterprise AI apps. The C3 AI model-driven architecture allows developers to create enterprise AI applications using conceptual models, rather than long code. This has significant benefits: AI applications and models can be used to optimize processes for every product or customer across all regions and businesses. You will see results in just 1-2 quarters. Also, you can quickly roll out new applications and capabilities. You can unlock sustained value - hundreds to billions of dollars annually - through lower costs, higher revenue and higher margins. C3.ai's unified platform, which offers data lineage as well as governance, ensures enterprise-wide governance for AI.

Declarative machine-learning systems offer the best combination of flexibility and simplicity, allowing for the fastest way to implement state-of-the art models. The system works by asking users to specify the "what" and then the system will figure out the "how". Start with smart defaults and iterate down to the code level on parameters. With Ludwig at Uber, and Overton from Apple, our team pioneered declarative machine-learning systems in industry. You can choose from our pre-built data connectors to support your databases, data warehouses and lakehouses as well as object storage. You can train state-of the-art deep learning models without having to manage infrastructure. Automated Machine Learning achieves the right balance between flexibility and control in a declarative manner. You can train and deploy models quickly using a declarative approach.

Create & Share Delightful Apps for Machine Learning. Gradio allows you to quickly and easily demo your machine-learning model. It has a friendly interface that anyone can use, anywhere. Installing Gradio is easy with pip. It only takes a few lines of code to create a Gradio Interface. You can choose between a variety interface types to interface with your function. Gradio is available as a webpage or embedded into Python notebooks. Gradio can generate a link that you can share publicly with colleagues to allow them to interact with your model remotely using their own devices. Once you have created an interface, it can be permanently hosted on Hugging Face. Hugging Face Spaces hosts the interface on their servers and provides you with a shareable link.

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.

Emly Labs, an AI framework, is designed to make AI accessible to users of all technical levels via a user-friendly interface. It offers AI project-management with tools that automate workflows for faster execution. The platform promotes team collaboration, innovation, and data preparation without code. It also integrates external data to create robust AI models. Emly AutoML automates model evaluation and data processing, reducing the need for human input. It prioritizes transparency with AI features that are easily explained and robust auditing to ensure compliance. Data isolation, role-based accessibility, and secure integrations are all security measures. Emly's cost effective infrastructure allows for on-demand resource provisioning, policy management and risk reduction.

Scores are available for factual accuracy and context retrieval, as well as guideline adherence and tonality. You can't improve if you don't measure. UpTrain continuously monitors the performance of your application on multiple evaluation criteria and alerts you if there are any regressions. UpTrain allows for rapid and robust experimentation with multiple prompts and model providers. Since their inception, LLMs have been plagued by hallucinations. UpTrain quantifies the degree of hallucination, and the quality of context retrieved. This helps detect responses that are not factually accurate and prevents them from being served to end users.

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.

Langtail is a platform that allows for the rapid development and deployment of Language Model (LLM) applications. It allows companies to quickly experiment, collaborate and launch production-grade LLM applications. The following are the key features: 1. No-code LLM Playground for quick debugging and ideas 2. Collaborative workspaces to share prompts and insights 3. Comprehensive observability suite including logging and analytics 4. Evaluation framework for systematically evaluating prompt performance 5. Deployment infrastructure to serve prompts via API across multiple environments 6. Future fine-tuning features to improve models based on user feedback Langtail enables both technical and nontechnical teams alike to quickly find LLM use cases that are high-value, refine prompts to ensure reliable performance, and deploy apps with ease. It's an all-in-one solution that will take your LLM project from prototype to production quicker than ever.

Our easy-to-use monitor builder allows you to create customized monitors for your machinelearning models. Get alerts for issues such as concept drift, model performance degradation and bias. Aporia can seamlessly integrate with any ML infrastructure. It doesn't matter if it's a FastAPI server built on top of Kubernetes or an open-source deployment tool such as MLFlow, or a machine-learning platform like AWS Sagemaker. Zoom in on specific data segments to track the model's behavior. Unexpected biases, underperformance, drifting characteristics, and data integrity issues can be identified. You need the right tools to quickly identify the root cause of problems in your ML models. Our investigation toolbox allows you to go deeper than model monitoring and take a deep look at model performance, data segments or distribution.

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).

Multi-model AI can help you build a better AI. AI models are an entirely new type of building block. Sieve makes it easy to use these building block to understand audio, create video, and more. The latest models are available in just a few line of code and there is a set of production-ready applications for many different use cases. Import your favorite models like Python packages. Visualize results using auto-generated interfaces created for your entire team. Easily deploy custom code. Define your environment computation in code and deploy it with a single command. Fast, scalable infrastructure with no hassle. Sieve is designed to scale automatically as your traffic grows with no extra configuration. Package models using a simple Python decorator, and deploy them instantly. A fully-featured observability layer that allows you to see what's going on under the hood. Pay only for the seconds you use. Take full control of your costs.

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.

Build and deploy deep-learning models for custom use scenarios. Our automated machine-learning process allows customers create powerful AI solutions based on our best-in class models and tailored to their specific challenges. Digital platforms can quickly create custom models that fit their guidelines and requirements. Build large language models to support specialized use cases, such as bots for customer and technical service. Create image classification models for better understanding image libraries, including search, organization and more.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

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.

Delineate is an easy-to use platform that generates machine learning-driven predictive models for various purposes. You can enrich your CRM data with churn forecasts, sales forecasts, or even data products for customers and employees, just to name a few. Delineate allows you to quickly access data-driven insights that will help you make better decisions. The platform is for founders, revenue managers, product managers, executives, data enthusiasts, and others who are interested in data. Use Delineate to unlock the full potential of your data.

The list is not exhaustive. Our solution engineers will work with your team to customize Kolena to your workflows and business metrics. The aggregate metrics do not tell the whole story. Unexpected model behavior is the norm. The current testing processes are manual and error-prone. They also cannot be repeated. Models are evaluated based on arbitrary statistics that do not align with product objectives. It is difficult to track model improvement as data evolves. Techniques that are adequate for research environments do not meet the needs of production.

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.

Your ML teams can auto-diagnose and improve their data and models to create robust and performant AI models. Only platform that can automatically diagnose data and models, empowering ML team to deliver robust and performant AI model faster. Camera noise, shadows, sign stickers, and other factors are covered. Confirmed using real-world images of models that consistently fail. While improving model accuracy by 0.2%. Our mission is to transform the way that the next generation AI systems are built. We need to create AI systems that are trusted by both users and companies if we want to use AI in our homes, offices, hospitals, roads, and businesses. We are leading AI researchers and professors at ETH Zurich. Our expertise includes formal methods, symbolic reasoning and machine learning. LatticeFlow was founded with the goal to create the first platform that allows companies to develop robust AI models that can be used in the wild.

High-quality validation and training data for AI applications. Our API allows you to access human-powered data for hundreds if use cases. After you send us your data via API, our platform will review it and return accurate ground truth data. It is easy to get started with our API for developers. A team of skilled Scalers and an AI that is focused on quality ensure accuracy of over 95%. High throughput and quick results.

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.

To develop AI solutions, engineers must have a resilient, open, repeatable engineering approach to ensure quality and agility. These efforts have not been able to address the challenges of today's complex environment, which is filled with a variety of tools and rapidly changing data. Platform for collaborative development that automates the control and development of AI applications across multiple persons. To predict customer behavior in real-time, and at scale, we can derive hyper-detailed customer profiles using enterprise data. AI-powered models that can continuously learn and achieve clearly defined business results. Allows organizations to demonstrate compliance with applicable rules and regulations. CognitiveScale's Cortex AI Platform is designed to address enterprise AI use cases using modular platform offerings. Customers use and leverage its capabilities in microservices as part of their enterprise AI initiatives.

It's fast and easy to create machine learning models for websites, apps, and other applications. Teachable Machine is flexible. You can use files or capture live examples. It respects your work. You can even use it entirely on-device without having to leave any microphone or webcam data. Teachable Machine, a web-based tool, makes it easy to create machine learning models. Artists, educators, students, innovators, and makers of all types - anyone with an idea to explore. There is no need to have any prior machine learning knowledge. Without writing any machine learning code, you can train a computer how to recognize your images, sounds, poses, and sounds. You can then use your model in your own sites, apps, and other projects.

Deploy LLMs, and other AI - commercial, homegrown, or open-source - on your private network based on a AI-optimized foundation. AI Core is an accurate, real-time representation of your entire business, including all decisions, actions, and processes. Use the Action Graph on top of the AI Core to set specific scopes for LLMs and models - such as hand-off procedures and auditable calculations. Monitor and control LLM activities and reach in real time to help users promote compliance.

Zinia's artificial intelligence platform connects key business decision makers and AI. Now you can build trusted AI models without relying on technical teams. This allows you to align AI with business objectives. This breakthrough technology is simplified to make it easier to build AI backwards for your business. Reduces time to implement AI from months to days, increasing revenue by 15-20%. Zinia optimizes business results with human-centered AI. Most AI development in organizations is not aligned with business KPIs. Zinia was created with the goal of democratizing AI for you. Zinia puts cutting-edge ML technology and AI Technology in your hands. Zinia was built by a team of AI experts with over 50 years experience. It is your trusted platform that simplifies complex technology and provides the fastest route from data to business decisions.

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.

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.