By integrating frameworks, the platform can efficiently adapt to technological advancements.Enhancing platform efficiency, expanding capabilities, and accelerating development.
- Deep Learning
- Data Processing
- Computer Vision
- Reinforcement Learning
- No-Code Development
- Decision
TensorFlow
Provides tools for building, training, and deploying deep learning models.
- Value
High-performance distributed training capabilities, supports multiple hardware accelerations (CPU, GPU, TPU), suitable for large-scale model deployment.
PyTorch
A user-friendly deep learning framework with dynamic computation graphs.
- Value
Highly flexible, easy to debug, widely used in research and production.
JAX
A high-performance numerical computation and automatic differentiation framework.
- Value
Accelerates matrix computations, suitable for reinforcement learning, scientific computing, and large-scale model optimization.
Keras
A high-level neural network API based on TensorFlow.
- Value
Simplifies complex model-building processes, ideal for rapid prototyping.
Hugging Face Transformers
Provides open-source tools for mainstream pre-trained language models (e.g., BERT, GPT).
- Value
Quickly load and fine-tune popular language models, boosting NLP task efficiency.
spaCy
An industrial-grade NLP framework supporting tokenization, named entity recognition, dependency parsing, etc.
- Value
Fast and efficient, ideal for production-level language processing.
Rasa
An open-source conversational AI framework supporting semantic understanding and dialogue management.
- Value
Build customized intelligent conversation systems and enhance human-computer interaction.
OpenAI API
Provides APIs for high-performance language models (e.g., GPT-4).
- Value
Instantly access powerful language models without the need for self-training.
OpenCV
A library for computer vision and image processing.
- Value
Supports various image and video processing functions, ideal for scene detection and object recognition.
Detectron2
A Facebook AI framework for object detection and segmentation.
- Value
Suitable for high-precision object detection and image segmentation tasks.
MediaPipe
A real-time multi-modal processing framework developed by Google.
- Value
Provides real-time body pose, gesture recognition, and more, ideal for AR/VR and interactive applications.
Stable-Baselines3
A Python library for reinforcement learning algorithms.
- Value
Quickly test and deploy reinforcement learning models.
RLlib
A distributed reinforcement learning framework.
- Value
Supports large-scale reinforcement learning tasks, suitable for game AI, robotics control, etc.
Ray
A distributed computing framework supporting parallelized model training.
- Value
Improves efficiency in large-scale training, ideal for combining reinforcement learning and deep learning.
Pandas
A data manipulation and analysis tool.
- Value
Simplifies data cleaning and analysis, suitable for structured data processing.
Dask
A Python framework for parallel computing.
- Value
Supports large-scale data processing and computation, ideal for distributed tasks.
Apache Spark
A distributed data processing platform.
- Value
Processes massive datasets and supports real-time data analysis.
NumPy / SciPy
Libraries for numerical and scientific computation.
- Value
Provides efficient matrix operations and numerical analysis capabilities.
OpenAI CLIP
Supports cross-modal matching between images and text.
- Value
Provides cross-modal search and classification capabilities, suitable for multi-modal AI applications.
DeepMind Perceiver
A general multi-modal AI model framework.
- Value
Unified processing across text, image, video, and other fields.
TensorFlow Hub
Stores and loads pre-trained multi-modal models.
- Value
Quickly use multi-modal models to improve development efficiency.
Apache Airflow
A data workflow orchestration tool.
- Value
Simplifies the automation of AI model training and inference workflows.
Streamlit
Quickly build data-driven web applications.
- Value
Enable model result visualization and interaction, suitable for no-code development.
FastAPI
A high-performance API framework in Python.
- Value
Offers a rapid solution for model service deployment.
SciKit-Learn
Provides libraries for traditional machine learning models and algorithms.
- Value
Supports classification, regression, and clustering tasks, suitable for finance, healthcare, etc.
Prophet
A framework for time series forecasting.
- Value
Efficient time series analysis, ideal for e-commerce and logistics.
Dash
A framework for data analysis and visualization.
- Value
Helps enterprise users display data and model results.
Boost Development Efficiency
Integrating frameworks enables the use of pre-built tools and functionalities, significantly reducing development cycles and supporting the rapid creation of complex AI applications and workflows.
Enhance Platform Capabilities and Flexibility
Frameworks provide support for cross-domain, multimodal, and distributed computing, meeting the demands of complex scenarios while improving the platform’s scalability and adaptability.
Optimize Performance and User Experience
By leveraging efficient algorithms and frameworks, the platform achieves real-time processing and large-scale data handling, delivering a seamless and superior user experience.
Support Diverse Industry Applications
Frameworks enable customized solutions tailored to the needs of various industries, empowering the platform to expand its reach into finance, healthcare, e-commerce, and more.
AI Agents not only simulate human behaviors but also automate tasks, make decisions, and drive innovation across various fields.
Looking into the Future of AI Agents
