Machine Learning Architecture
Last updated
Last updated
In the Aurory AI framework, the machine learning architecture is a systematic process that begins with data collection and extends to model deployment, ensuring robust and efficient AI solutions.
Data and Program are inputs to the computation process.
Computation is performed based on predefined rules and algorithms.
The Output is generated directly from this computation.
Data and Desired Output are used to train a model.
Training involves creating an algorithm that learns from the data to produce a Program (aka "Model").
The trained Model then processes New Data to produce the desired Output.
Task orchestration is the central hub that coordinates all other components in the pipeline. It ensures that each step, from data generation to prediction, is executed in the correct sequence and without errors.
Data generation is the initial step where synthetic or real-world data is produced. This data forms the foundation for training and validating machine learning models.
Data Sources: The pipeline begins with sourcing data from various repositories, which is crucial for feeding the machine learning models.
Data Labeling: Using tools like Scale, Labelbox, Snorkel, and SageMaker, data is annotated to prepare it for training and development.
Query Engines: Engines such as Presto and Hive are employed to execute queries and manipulate data effectively.
Data Science Libraries: Libraries like Spark, Pandas, Numpy, and Dask provide extensive functionality for processing and analyzing data.
Workflow Manager: Tools like Airflow, Prefect, Pachyderm, Elementl, Dagster, Tecton, and Kubeflow manage and orchestrate data workflows, ensuring smooth operations and transformations.
Data Science/Machine Learning Platform: Platforms like Jupyter, Databricks, Domino, SageMaker, H2O, Colab, Deepnote, and Notable are used to process and transform data, forming the foundation for model training.
ML Frameworks: Frameworks such as Scikit-Learn, XGBoost, and MLlib are employed for building and training machine learning models.
DL Frameworks: Deep learning frameworks like TensorFlow, Keras, PyTorch, and H2O are utilized for developing deep learning models.
RL Libraries: Libraries such as Gym, Dopamine, RLlib, and Coach support the creation of reinforcement learning models.
Distributed Processing: Tools like Spark, Ray, Dask, Kubeflow, PyTorch, and TensorFlow enable large-scale data processing, facilitating the handling of massive datasets.
Experiment Tracking: Tools like Weights & Biases, MLflow, Comet, and ClearML track experiments, ensuring reproducibility and aiding in the analysis of results.
Low Code ML: Solutions like DataRobot, H2O, Databricks AutoML, Google AutoML, Continual, Mage, MindsDB, Obviously AI, Roboflow, and Akkio simplify model development, making machine learning accessible to a broader audience.
Model Diagnostics: Tools like Labelbox, Scale, Nucleus, and Aquarium diagnose model performance and data quality, providing insights into potential improvements.
Feature Store: Feature stores like Tecton, Feast, and Databricks manage features for models, ensuring consistency and efficiency.
Pre-Trained Models: Models from Hugging Face, ModelZoo, and PyTorch/TensorFlow are utilized for transfer learning, accelerating the development process.
Model Registry: Registries like MLflow, SageMaker, Algorithmia, and Hugging Face manage machine learning models, facilitating version control and deployment.
Compilers: Tools like OctoML/TVM optimize models for deployment, enhancing performance and efficiency.
Validation: Tools like Robust Intelligence and Calypso ensure model robustness and accuracy, validating them against various criteria.
Auditing: Tools like Credo and Arize audit models for fairness, bias, and compliance, ensuring ethical AI practices.
Feature Server: Servers like Tecton, Feast, and Databricks serve features for real-time inference, enabling immediate application of models.
Batch Predictor: Predictors such as Spark perform batch predictions, handling large volumes of data efficiently.
Online Model Server: Servers like TensorFlow Serving, Ray Serve, and Seldon deploy models for real-time inference, facilitating instantaneous responses.
ML APIs: APIs from OpenAI, Cohere, AWS, GCP, and Azure provide interfaces for deploying machine learning models, making them accessible for integration with various applications.
App Framework: Frameworks like Flask, Streamlit, and Rasa build applications that utilize machine learning models, enabling interactive and user-friendly interfaces.
Vector Database: Databases such as Faiss, Milvus, and Pinecone store and manage vector embeddings, supporting efficient similarity searches and data retrieval.
Monitoring: Tools like Arize, Fiddler, Arthur, Truera, WhyLabs, and Gantry monitor model performance, ensuring reliability and continuous improvement.
Clients: End users or systems interact with the deployed machine learning models, benefiting from the AI solutions provided by Aurory AI.
The Machine Learning Architecture for Aurory AI is a comprehensive framework encompassing data transformation, model training, inference, and integration. The first phase, Data Transformation, involves labeling, diagnostics, and workflow management using tools like Scale, Labelbox, and Airflow to ensure clean and structured data. Model Training and Development then employs platforms and frameworks such as Jupyter, TensorFlow, and Scikit-Learn for developing, training, and optimizing models. This phase also includes experiment tracking and feature storage to support robust model creation. Finally, Model Inference and Integration ensures models are efficiently deployed and monitored. Batch prediction, online serving, and integration with APIs and app frameworks ensure seamless interaction with end-users. Overall, Aurory AI’s architecture ensures a streamlined, efficient, and scalable approach to deploying advanced machine learning solutions.
