Lifecycle
Data Engineering
Seven steps to prepare you data for use in a machine learning model
- Gather your data
- Handle missing data
- Feature extraction
- Decide which features are important
- Encode categorical values
- Numeric feature engineering
- Split your data into training and testing datasets
Exploratory Data Analysis
- Basic data analysis concepts and terminology
- Kinesis Data Streams
- Kinesis Data Firehose
- Kinesis Video Streams
- Kinesis Data Analytics
- Visualize data for machine learning
Modeling
Three types of models
- Supervised machine learning
- Unsupervised machine learning
- Reinforcement machine learning
Algorithms
Structured data
- Linear Learner
- Factorization Machines
- XGBoost
- K-Means
- Principal Component Analysis
- Random Cut Forest
Image data
- Image Classification
Natural Language data
- Sequence2Sequence
- Neural Topic Modeling
- Latent Dirichlet Allocation
- Blazing Text
Time Series data
- DeepAR
Implementation and Operation
- To optimize the performance of your models you can use Automatic Model Tuning to find the hyperparameters that will give you the optimal performance
- You can use SageMaker hosting to automatically scale your model to the performance needed for your model
本文由
Oscaner
创作, 采用
知识共享署名4.0
国际许可协议进行许可
本站文章除注明转载/出处外, 均为本站原创或翻译, 转载前请务必署名
-
Previous
[MLS-C01] [IMPL and OPs] Deploy and Operationalize -
Next
[MLS-C01] [Conclusion] Exam Day Tips