Best Machine Learning Software in China - Page 11
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Compare the Top Machine Learning Software in China as of November 2025 - Page 11
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RTE Runner
Cybersoft North America
It is the artificial intelligence solution to analyze complex data, empower decision making and transform human and industrial productivity. It is the automated machine solution that has the potential to reduce the burden on already overwhelmed teams by automating the main bottlenecks in the data science process. It breaks data silos with the intuitive creation of data pipelines that feed live data into deployed models and then dynamically creates model execution pipelines to obtain real-time predictions on incoming data. It monitors the health of deployed models based on the confidence of predictions to inform model maintenance. -
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SparkAI
SparkAI
SparkAI combines people and technology to resolve AI edge cases, false positives, and other exceptions encountered live in production, so you can launch & scale automation products faster than ever. -
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Amazon Monitron
Amazon
Detect machine issues before they occur with machine learning (ML), and take action. Start monitoring equipment in minutes with easy installation and automatic, secure analysis through the Amazon Monitron end-to-end system. Improve system accuracy continuously as Amazon Monitron learns from technician feedback entered in the mobile and web apps. Amazon Monitron is an end-to-end system that uses machine learning to detect abnormal conditions in industrial equipment and enable predictive maintenance. Save on costly repairs and prevent factory equipment downtime with easy-to-install hardware and the power of ML. Reduce unplanned equipment downtime with predictive maintenance and machine learning. Amazon Monitron uses machine learning on temperature and vibration data. Amazon Monitron can help you predict equipment downtime before it happens. Compare what it costs to get started with how much you could save. -
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Monitaur
Monitaur
Creating responsible AI is a business problem, not just a tech problem. We solve for the whole problem by bringing teams together onto one platform to mitigate risk, leverage your full potential, and turn intention into action. Uniting every stage of your AI/ML journey with cloud-based governance applications. GovernML is the kickstarter you need to bring good AI/ML systems into the world. We bring user-friendly workflows that document the lifecycle of your AI journey on one platform. That’s good news for your risk mitigation and your bottom line. Monitaur provides cloud-based governance applications that track your AI/ML models from policy to proof. We are SOC 2 Type II-certified to enhance your AI governance and deliver bespoke solutions on a single unifying platform. GovernML brings responsible AI/ML systems into the world. Get scalable, user-friendly workflows that document the lifecycle of your AI journey on one platform. -
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Cerebrium
Cerebrium
Deploy all major ML frameworks such as Pytorch, Onnx, XGBoost etc with 1 line of code. Don't have your own models? Deploy our prebuilt models that have been optimised to run with sub-second latency. Fine-tune smaller models on particular tasks in order to decrease costs and latency while increasing performance. It takes just a few lines of code and don't worry about infrastructure, we got it. Integrate with top ML observability platforms in order to be alerted about feature or prediction drift, compare model versions and resolve issues quickly. Discover the root causes for prediction and feature drift to resolve degraded model performance. Understand which features are contributing most to the performance of your model.Starting Price: $ 0.00055 per second -
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Amazon SageMaker Debugger
Amazon
Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values. -
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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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Amazon SageMaker Studio
Amazon
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. -
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Amazon SageMaker Studio Lab
Amazon
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately. -
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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.
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Amazon SageMaker Canvas
Amazon
Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own, without requiring any ML experience or having to write a single line of code. Visual point-and-click interface to connect, prepare, analyze, and explore data for building ML models and generating accurate predictions. Automatically build ML models to run what-if analysis and generate single or bulk predictions with a few clicks. Boost collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. Import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas. With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyze models and generate accurate predictions. -
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Amazon SageMaker Edge
Amazon
The SageMaker Edge Agent allows you to capture data and metadata based on triggers that you set so that you can retrain your existing models with real-world data or build new models. Additionally, this data can be used to conduct your own analysis, such as model drift analysis. We offer three options for deployment. GGv2 (~ size 100MB) is a fully integrated AWS IoT deployment mechanism. For those customers with a limited device capacity, we have a smaller built-in deployment mechanism within SageMaker Edge. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow. Amazon SageMaker Edge Manager provides a dashboard so you can understand the performance of models running on each device across your fleet. The dashboard helps you visually understand overall fleet health and identify the problematic models through a dashboard in the console. -
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Amazon SageMaker Clarify
Amazon
Amazon SageMaker Clarify provides machine learning (ML) developers with purpose-built tools to gain greater insights into their ML training data and models. SageMaker Clarify detects and measures potential bias using a variety of metrics so that ML developers can address potential bias and explain model predictions. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias. SageMaker Clarify also includes feature importance scores that help you explain how your model makes predictions and produces explainability reports in bulk or real time through online explainability. You can use these reports to support customer or internal presentations or to identify potential issues with your model. -
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Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
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Amazon SageMaker Autopilot
Amazon
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps. -
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With Amazon SageMaker Model Monitor, you can select the data you would like to monitor and analyze without the need to write any code. SageMaker Model Monitor lets you select data from a menu of options such as prediction output, and captures metadata such as timestamp, model name, and endpoint so you can analyze model predictions based on the metadata. You can specify the sampling rate of data capture as a percentage of overall traffic in the case of high volume real-time predictions, and the data is stored in your own Amazon S3 bucket. You can also encrypt this data, configure fine-grained security, define data retention policies, and implement access control mechanisms for secure access. Amazon SageMaker Model Monitor offers built-in analysis in the form of statistical rules, to detect drifts in data and model quality. You can also write custom rules and specify thresholds for each rule.
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Amazon SageMaker Pipelines
Amazon
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK. -
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Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
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Outerbounds
Outerbounds
Design and develop data-intensive projects with human-friendly, open-source Metaflow. Run, scale, and deploy them reliably on the fully managed Outerbounds platform. One platform for all your ML and data science projects. Access data securely from your existing data warehouses. Compute with a cluster optimized for scale and cost. 24/7 managed orchestration for production workflows. Use results to power any application. Give your data scientists superpowers, approved by your engineers. Outerbounds Platform allows data scientists to develop rapidly, experiment at scale, and deploy to production confidently. All within the outer bounds of policies and processes defined by your engineers, running on your cloud account, fully managed by us. Security is in our DNA, not at the perimeter. The platform adapts to your policies and compliance requirements through multiple layers of security. Centralized auth, a strict permission boundary, and granular task execution roles. -
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Robust Intelligence
Robust Intelligence
The Robust Intelligence Platform integrates seamlessly into your ML lifecycle to eliminate model failures. The platform detects your model’s vulnerabilities, prevents aberrant data from entering your AI system, and detects statistical data issues like drift. At the core of our test-based approach is a single test. Each test measures your model’s robustness to a specific type of production model failure. Stress Testing runs hundreds of these tests to measure model production readiness. The results of these tests are used to auto-configure a custom AI Firewall that protects the model against the specific forms of failure to which a given model is susceptible. Finally, Continuous Testing runs these tests during production, providing automated root cause analysis informed by the underlying cause of any single test failure. Using all three elements of the Robust Intelligence platform together helps ensure ML Integrity. -
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Delineate
Delineate
Delineate offers an easy-to-use platform for generating machine learning-driven predictive models for a range of purposes. Enrich your CRM data with churn predictions, sales forecasts, and even build data products for your customers and team, just to name a few. With Delineate you can access data-driven insights to improve decision-making with ease. The platform caters to founders, revenue teams, product managers, executives, and data enthusiasts. Try Delineate and unleash your data's full potential.Starting Price: $99 per month -
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datuum.ai
Datuum
AI-powered data integration tool that helps streamline the process of customer data onboarding. It allows for easy and fast automated data integration from various sources without coding, reducing preparation time to just a few minutes. With Datuum, organizations can efficiently extract, ingest, transform, migrate, and establish a single source of truth for their data, while integrating it into their existing data storage. Datuum is a no-code product and can reduce up to 80% of the time spent on data-related tasks, freeing up time for organizations to focus on generating insights and improving the customer experience. With over 40 years of experience in data management and operations, we at Datuum have incorporated our expertise into the core of our product, addressing the key challenges faced by data engineers and managers and ensuring that the platform is user-friendly, even for non-technical specialists. -
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Layerup
Layerup
Extract and Transform any data from any data source with Natural Language connect to your data source - everything ranging from your DB to your CRM to your billing solution. Improve Productivity by 5-10x Forget about wasting time on clunky BI tools. Use Natural Language to query any complex data in seconds. Transition from DIY tools to non-DIY AI-powered tools. Generate complex dashboards and reports in a few lines. No more SQL or complex formulas - let Layerup AI do the heavy lifting for you. Layerup not only gives you instant answer to questions that would require 5-40 hours/month on SQL queries, but it will act as your personal data analyst 24/7 while providing you complex dashboards/charts that you can embed anywhere. -
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Gradio
Gradio
Build & Share Delightful Machine Learning Apps. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share. -
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Palantir AIP
Palantir
Deploy LLMs and other AI — commercial, homegrown or open-source — on your private network, based on an AI-optimized data foundation. AI Core is a real-time, full-fidelity representation of your business that includes all actions, decisions, and processes. Utilize the Action Graph, atop the AI Core, to set specific scopes of activity for LLMs and other models – including hand-off procedures for auditable calculations and human-in-the-loop operations. Monitor and control LLM activity and reach in real-time to help users promote compliance with legal, data sensitivity, and regulatory audit requirements. -
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Tencent Cloud TI Platform
Tencent
Tencent Cloud TI Platform is a one-stop machine learning service platform designed for AI engineers. It empowers AI development throughout the entire process from data preprocessing to model building, model training, model evaluation, and model service. Preconfigured with diverse algorithm components, it supports multiple algorithm frameworks to adapt to different AI use cases. Tencent Cloud TI Platform delivers a one-stop machine learning experience that covers a complete and closed-loop workflow from data preprocessing to model building, model training, and model evaluation. With Tencent Cloud TI Platform, even AI beginners can have their models constructed automatically, making it much easier to complete the entire training process. Tencent Cloud TI Platform's auto-tuning tool can also further enhance the efficiency of parameter tuning. Tencent Cloud TI Platform allows CPU/GPU resources to elastically respond to different computing power needs with flexible billing modes. -
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MosaicML
MosaicML
Train and serve large AI models at scale with a single command. Point to your S3 bucket and go. We handle the rest, orchestration, efficiency, node failures, and infrastructure. Simple and scalable. MosaicML enables you to easily train and deploy large AI models on your data, in your secure environment. Stay on the cutting edge with our latest recipes, techniques, and foundation models. Developed and rigorously tested by our research team. With a few simple steps, deploy inside your private cloud. Your data and models never leave your firewalls. Start in one cloud, and continue on another, without skipping a beat. Own the model that's trained on your own data. Introspect and better explain the model decisions. Filter the content and data based on your business needs. Seamlessly integrate with your existing data pipelines, experiment trackers, and other tools. We are fully interoperable, cloud-agnostic, and enterprise proved. -
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IBM watsonx
IBM
IBM watsonx is a powerful suite of AI products designed to accelerate the adoption of generative AI across business workflows. With tools like watsonx.ai for AI application development, watsonx.data for data management, and watsonx.governance for regulatory compliance, businesses can create, manage, and deploy AI solutions seamlessly. The platform provides an integrated developer studio to foster collaboration and optimize the entire AI lifecycle. IBM watsonx also offers tools for automating processes, boosting productivity with AI assistants and agents, and supporting responsible AI through governance and risk management. Trusted by industries worldwide, IBM watsonx enables businesses to unlock the full potential of AI to drive innovation and enhance decision-making.
