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DeepFlow
Supports all stages of the MLOps life cycle, including development, training, inference, monitoring, and retraining to improve model results.
AI functions expand easily
Various expansion features make model building more efficient
Effective management
Clear roles enhance efficiency
More Flexibility
Scheduling resolves resource distribution
7 features to simplify project management
AI Workspaces
Each workspace is an independent project, allowing for multi-person collaboration and enhanced data security.
Workspace Management
Use workspaces for project permissions with strong protection. Managers allocate computing resources when creating them.
AutoML
Over a dozen built-in algorithms let users set parameters and ranges via the interface—no coding needed—to find the best model.
Model Repository
Users can create model libraries to store versions, access history, and view inference results.
Model Deployment & Health
One-click deployment publishes APIs and monitors models.
Job Scheduler
Assist users to perform regular tasks, such as retraining models, and supporting breakpoint execution.
APP Market
Tools like Streamlit, Label Studio, and MLflow enable quick platform integration.
Resource Allocation
Managers can query resource usage status and confirm whether resource configuration needs to be adjusted.
See more success cases
Industry
Medical
Product
DeepFlow
MLOps speeds medical AI adoption
Industry
Financial
Product
DeepFlow
Monitoring model, ensure privacy, boost efficiency
Industry
Transportation
Product
DeepFlow
Optimize operations, cut disruptions and costs
Product related Q&A
Q1. What is MLOps?
MLOps, automates and simplifies machine learning (ML) workflows and deployment. Machine learning and artificial intelligence (AI) are actionable core capabilities that enable you to solve complex real-world problems and deliver value to your customers. MLOps is an ML culture and practice that unifies ML application development (Dev) and ML system deployment and operations (Ops). Your organization can use MLOps to automate and standardize processes throughout the ML lifecycle. These processes include model development, testing, integration, release, and infrastructure management.
Q2. Does the product support cloud version?
This product supports cloud, on-premises or cloud-on-premises hybrid.
Q3. Advantages of DeepFlow?
It can be built in conjunction with the company’s internal processes to achieve corporate AI governance.
