Cloud AI taught on week 12 of DSA 441 Cloud Computing

1. DSA 441 – Cloud Computing
Week 12: Cloud AI
Asst. Prof. Dr. Ferdin Joe John Joseph
Faculty of Information Technology
Thai-Nichi Institute of Technology, Bangkok

2. Cloud AI (Machine Learning Platform for AI)

3. Cloud AI
• Machine Learning Platform for AI
• Used for creating machine learning models in cloud
• Deployed models are used capable of using in other cloud products
and services
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Technology
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4. Machine Learning
Machine learning uses statistical algorithms to train models with a large amount of
historical data, and uses the generated models to help you make informed business
decisions. Machine learning can be applied to the following scenarios:
• Marketing: commodity recommendation, user profiling, and targeted advertising.
• Finance: credit risk prediction for loans, financial risk management, stock
forecast, and gold price forecast.
• Social network: analytics of key opinion leaders and relational networks.
• Text processing: news classification, keyword extraction, text summarization, and
text analytics.
• Unstructured data processing: image classification and text extraction based on
optical character recognition (OCR).
• Other forecast scenarios: rainfall forecast and football match result forecast.
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5. Machine Learning Types
Machine learning includes traditional machine learning and deep learning.
Traditional machine learning is divided into the following learning modes:
• Supervised learning: Each sample has an expected value. You can create a model
to map input feature vectors to target values. Supervised learning can be used to
solve regression and classification issues.
• Unsupervised learning: Samples do not have target values. Unsupervised learning
is used to discover potential regular patterns from the sample data. You can use
unsupervised learning to solve clustering issues.
• Reinforcement learning: This learning mode is complex. A system constantly
interacts with the external environment to obtain feedback and determines its
own behavior to achieve a long-term optimization of targets. Examples of
reinforcement learning are AlphaGo and autonomous driving.
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6. Why Machine Learning Platform for AI
• Machine Learning Platform for AI is designed to serve business within
Alibaba Group, such as Taobao, Alipay, and Amap.com.
• It enables developers of Alibaba Group to use AI technologies in an
efficient, concise, and standard way. Machine Learning Platform for AI
was officially released in 2018.
• It has gained tens of thousands of enterprises and individual
developers, and has become one of the leading machine learning
platforms on the cloud in China.
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7. Machine Learning Frameworks
• Flink, a stream computing framework.
• TensorFlow, an optimized deep learning framework based on open
source TensorFlow.
• Parameter Server, a computing framework that can process hundreds
of billions of samples in parallel.
• Spark, PySpark, MapReduce, and other mainstream open source
computing frameworks.
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8. Services
• Machine Learning Studio: a service for visualized modeling and
distributed training.
• Data Science Workshop (DSW): a Notebook-based service for
interactive AI research and development.
• AutoLearning: a service for automated modeling.
• Elastic Algorithm Service (EAS): a service that allows you deploy
models as online prediction services.
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9. Benefits
• Services provided by Machine Learning Platform for AI can be used separately or
in combination. Machine Learning Platform for AI provides an all-in-one platform
for machine learning. After training data is prepared in Object Storage Service
(OSS) or MaxCompute, you can use Machine Learning Platform for AI to
streamline all workflows, including data uploading, data preprocessing, feature
engineering, model training, model evaluation, and model publishing (to both
online and offline environments).
• Machine Learning Platform for AI can be integrated with DataWorks and allows
you to process data by using Structured Query Language (SQL), user-defined
functions (UDFs), user-defined aggregation functions (UDAFs), and MapReduce.
This ensures higher flexibility and efficiency.
• Experiments that are used to train and generate models can be scheduled in
DataWorks. You can run scheduled tasks in the staging or production
environment. This enables data isolation.
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10. Architecture
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11. Architecture
• Infrastructure layer: includes CPU, GPU, Field Programmable Gate
Array (FPGA), and Neural network Processing Unit (NPU) resources.
• Computing engines and container services layer: includes
MaxCompute, E-MapReduce (EMR), Realtime Compute, and Alibaba
Cloud Container Service for Kubernete (ACK).
• Computing framework layer: includes Alink, TensorFlow, PyTorch,
Caffe, MapReduce, SQL, and Message Passing Interface (MPI). You can
run distributed computing tasks in these frameworks.
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12. Architecture (cont’d)
Machine Learning Platform for AI streamlines the workflows of machine learning, including
data preparation, model creation and training, and model deployment.
• Data preparation: Smart labeling of Machine Learning Platform for AI allows you to label
data and manage datasets in multiple scenarios.
• Model creation and training: Machine Learning Platform for AI provides diverse services
to meet different modeling requirements. These services are Machine Learning Studio,
Data Science Workshop (DSW), Deep Learning Containers (DLC), and AutoLearning.
Machine Learning Studio is a service for visualized modeling. DSW allows you to create
models by interactive programming. DLC is a cloud-native platform for training deep
learning models. AutoLearning is a service for end-to-end automated model creation.
• Model deployment: Machine Learning Platform for AI provides Elastic Algorithm Service
(EAS) and Blade to help you deploy models as services. EAS is a cloud-native online
inference platform and Blade is a tool used to accelerate model inference. Machine
Learning Platform for AI also provides an intelligent marketplace where you can obtain
recommended solutions and model algorithms to solve business issues and improve
production efficiency.
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13. Architecture (cont’d)
• Business layer: Machine Learning Platform for AI is widely used in
finance, medical care, education, transportation, and security sectors.
Search systems, recommendation systems, and financial service
systems of Alibaba Group all use Machine Learning Platform for AI to
explore data values for making informed business decisions.
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14. Gartner statistics
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15. Benefits
Rich machine learning algorithms
• The algorithms of Machine Learning Platform for AI have been tested
by business within Alibaba Group for many years. Machine Learning
Platform for AI supports basic algorithms such as clustering and
regression, and complex algorithms such as text analysis and feature
engineering.
Compatibility with Alibaba Cloud services
• Models trained by Machine Learning Platform for AI are stored in
MaxCompute and can be used with other Alibaba Cloud services.
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16. Benefits
All-in-one machine learning platform
• Machine Learning Platform for AI allows you to streamline workflows
including data uploading, data preprocessing, feature engineering,
model training, and model evaluation and publishing.
Mainstream deep learning frameworks
• Machine Learning Platform for AI supports mainstream deep learning
frameworks such as TensorFlow, Caffe, and MXNet.
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17. Benefits
Visualized modeling methods
• Machine Learning Platform for AI is developed with classic machine
learning algorithms. These algorithms provide the following benefits:
• You can drag and drop components to create machine learning
experiments.
• You can use the built-in Automated Machine Learning (AutoML)
module to tune parameters. AutoML can automatically explore model
parameters, evaluate models, pass down generated models to
downstream nodes, and optimize models.
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18. Benefits
Quick model deployment
• Machine Learning Platform for AI allows you to deploy models trained
by Machine Learning Studio, Data Science Workshop (DSW), and
AutoLearning as Restful APIs.
• This way, you can use models by calling their APIs to meet your
requirements.
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19. Machine Learning Studio
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20. DSW
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21. EAS
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22. DSW Demonstration
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23. Next Week
• Capstone Project Preparation
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