알리바바 클라우드 PAI (machine learning Platform for AI)
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The document outlines Alibaba Cloud's Machine Learning Platform for AI (PAI), detailing its various components such as data processing, machine learning frameworks, and predictive services. Key features include a user-friendly GUI, cloud-native capabilities, and support for multiple algorithms and hardware options. This platform offers tools for both data scientists and developers to streamline AI model training and deployment efficiently.
알리바바 클라우드 PAI (machine learning Platform for AI)
- 1. 1 Machine Learning Platform for AI Senior Solutions Architect 임종진 Alibaba Cloud 2022.02
- 3. 3 Machine Learning Platform for AI (PAI) PAI , .
- 4. 4 Machine Learning Framework (Alink / MPI / PS / Graph / TensorFlow / PyTorch / Caffe…) Compute Engine (MaxCompute / EMR / Realtime Compute) PAI-EAS online prediction • One-click deployment • High performance • Blue-green deployment • Blade compilation optimization Basic hardware (CPU, GPU, FPGA, NPU) Alibaba Cloud Container Service for K8S (ACK) Visual modeling PAI-Studio • Nearly 200 algorithm components • Drag-drop method to build an experiment • Supports many feature samples PAI-DSW Interactive modeling • Deep integration of big data engines • Multi-frame TF, PyTorch • JupyterLab, WebIDE, Terminal Cloud-native deep training PAI-DLC • Cloud-native and container • Elasticity • Out of the box Automatic learning PAI AutoLearning • Zero threshold use • Migration learning framework • One-stop solution Intelligent ecological market • AI solution • Algorithms & Models • Business application API AI Taobao platform Data collection Intelligent labeling • Multi-scene Template: Image Detection, segmentation, and comprehensive annotation • Data set management • Active learning * • Smart pre-labeling • Elastic scaling Machine Learning Platform for AI (PAI) PAI Studio, , ML All-in-One .
- 5. 5 PAI-Studio GUI and Distributed Modeling Platform Data preprocessing, feature engineering, model training, modelEvaluation prediction 200 ML 7 90% PAI Studio PySpark, Spark PAI-Studio GUI ML .
- 6. 6 PAI-DSW Data Science Workshop PAI-DSW GPU CPU , Deep Learning Lifecycle R&D Cloud AI Notebook .
- 7. 7 PAI console Create Deploy Update Destroy Arena CLT Control Panel Manage DLC Cluster Manage Jobs ECS Clusters VPC ECS Instance VPC ECS Instance VPC ECS Instance . . . . . . DLC DLC DLC DLC Clusters TF Jobs PT Jobs Jupyter Dash Board EIP API Server Scheduler Cloud-native Linear acceleration Fully cloud-based infrastructure Kubernetes-native, containerization / Support ACK/ECI Semi-Hosting & full hosting Elastic resources, dynamic scaling Data parallelism & Model parallelism 10 Classification Task High cost performance (GN6V, GN5, ) PAI-DLC Deep Learning Containers cloud-native deep learning training platform PAI-DLC Deep Learning .
- 8. 8 PAI-TF Network layer AutoML-Tuning automatic parameter adjustment TransferLearning framework Business layer Image , , visual ResNet LeNet VGG GoogLeNet InceptionNet... ... 0 threshold use Small amount of data 1 station solution , , Open the box, small white friendly TF . PAI-AutoLearning the underlying framework based on a PAI-TF-developed migration learning framework
- 9. 9 PAI-Tensorflow Tensorflow • • Compilation passes • • Pruning • Code generation • I/O PAI-Tensorflow Tensorflow .
- 10. 10 Cloud-native online services • , 40W+ QPS • (traditional learning and deep learning) • , scaling, blue-green • Processor SDK • PAI-Studio, PAI-DSW customer • PAI-Blade, model compilation PAI-EAS Online prediction Elastic Algorithm Service .
- 11. 11 PAI-EAS Online prediction Elastic Algorithm Service Cloud Native ML/DL Model Support TensorFlow、Caffe、PMML 、 OfflineModel…. CalculationDifferent alarm timepoints Every task needs an alarm timepoint RESTful API .
- 12. 12 : • CNN(Image) TF 4 • CRNN (image TF 1.3 • Segmentation (Image class) TF 1.3 • Bert (natural language) TF 2.8 • ASR (voice) TF 2.5 PAI-Blade One-stop compilation and optimization tool PAI TF TF 4 .
- 13. 13 PAI Compilation Volta GPU Tensorflow . PAI / 3 .
- 14. 14 Solution Components Input REST API ML , End-to-End DevOps .
- 16. 16 PAI • , VOC , , , Workflow : • PAI Studio • PAI EAS Product PAI + MaxCompute + DataWorks NAS OSS，… MaxCompute Distributed Training OCR Machine Translation NLP Content Security DataWorks DataHub Image Identification Risk Control Brain Public Opinion Marketing Cloud Resources PAI Studio PAI Notebook Service ( DSW ) Developer PAI EAS ( Elastic Algorithm Service ) AI Security PAI .
- 17. 17 PAI PAI ML AI .
- 18. 18 PAI Alibaba . PAI , RDMA NCCL .
- 19. 19 1 ‒ PAI . DB Dataworks PAI Studio . PAI EAS Redis .
- 20. 20 Underlying basic data User data Data processing and storage (Offline) User/Material Feature engineering Data integration hourly cycle import Training (offline) Material data Third-party portrait RDS: MySQL Nginx User Behavior Log Kafka Flume DRDS Comment data MaxCompute DW User table Material table MaxCompute User characteristics Material characteristics Behavior characteristics DW Flink real-time computing ETL Statistics business Real-time features Kafka PAI-Studio Sample generation Recall algorithm Sample generation PAI-Studio Sorting algorithm RDS: MySQL User/material recommendation list Online recommendation storage Redis User/Material Features User vector PAI-EAS Model services Inference service Faiss Server Vector service OSS transit Item vector Model file Online shop service (Online) Sub-Table 1: Reading history POLARDB Sub-Table 2: Reading history K8S User exposure request Recommended module Multi-Channel recall Exposure deduplication filtering Sorting Query K most similar items Real-TIME 2 - PAI . RDS, POLAR DB , . PAI .
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