Practical data science
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This document discusses implementing practical data science on AWS. It covers analyzing datasets and training ML models with AutoML, registering data with AWS Glue and querying with Athena, and performing data visualization. It also discusses measuring statistical bias with SageMaker Clarify and using SHAP to determine feature importance. The document recommends exploring datasets with AWS Data Wrangler and Glue and building, training, and deploying BERT pipelines using feature engineering, preprocessing, and the feature store. It concludes by discussing optimizing models with techniques like Hyperparameter tuning and deploying human-in-the-loop pipelines using SageMaker Ground Truth and Augmented AI.
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Practical data science
- 1. Practical Data Science Implementation on AWS Ding Li 2021.8
- 2. 2 1. Analyze Datasets and Train ML Models using AutoML
- 3. 3 Data Science and Cloud
- 4. 4 Register Data with AWS Glue and Query Data with Athena
- 6. 6 Statistical Bias and SageMaker Clarify Covariant Drift: distribution of the independent variables or the features can change. Prior Probability Drift: data distribution of your labels or the targeted variables might change. Concept Drift: relationship between the features and the labels can change. Concept drift also called as concept shift can happen when the definition of the label itself changes based on a particular feature like age or geographical location. Measure Class Imbalance (CI) • Measures the imbalance in the number of examples that are provided for different facet values. • Does a particular product category have disproportionately large number of total reviews than any other category in the dataset? Difference in Proportions of Labels (DPL) • Measures the imbalance of positive outcomes between the different facet values. • If a particular product category has disproportionately higher ratings than other categories. Amazon SageMaker Clarify
- 7. 7 Feature Importance SHAP Rank the individual features in the order of their importance and contribution to the final model. SHAP (SHapley Additive exPlanations) GitHub paper YouTube A game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions New Data Flow Import Data Add Data Analysis Feature Importance
- 8. 8 • Auto ML allows for experts to focus on those hard problems that can't be solved through Auto ML. • Auto ML can reduce the repetitive work, experts can apply their domain to analyze the results
- 9. 9 Automatic data pre-processing and feature engineering • Automatic data pre-processing and feature engineering automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps. • Automatic ML model selection automatically infers the type of predictions that best suit your data, such as binary classification, multi-class classification, or regression. SageMaker Autopilot then explores high-performing algorithms such as gradient boosting decision tree, feedforward deep neural networks, and logistic regression, and trains and optimizes hundreds of models based on these algorithms to find the model that best fits your data. • Model leaderboard can view the list of models, ranked by metrics such as accuracy, precision, recall, and area under the curve (AUC), review model details such as the impact of features on predictions, and deploy the model that is best suited to your use case.
- 10. 10 Amazon SageMaker Built-in Algorithms
- 11. 11 Explore the Use Case and Analyze the Dataset: • AWS Data Wrangler • AWS Glue • Amazon Athena • Matplotlib • Seaborn • Pandas • Numpy Data Bias and Feature Importance: • Measure Pretraining Bias - Amazon SageMaker • SHAP Automated Machine Learning: • Amazon SageMaker Autopilot Built-in algorithms: • Elastic Machine Learning Algorithms in Amazon SageMaker • Word2Vec algorithm • GloVe algorithm • FastText algorithm • Transformer architecture, "Attention Is All You Need" • BlazingText algorithm • ELMo algorithm • GPT model architecture • BERT model architecture • Built-in algorithms • Amazon SageMaker BlazingText
- 12. 12 2. Build, Train, and Deploy ML Pipelines using BERT
- 13. 13 • Dataset best fits the algorithm • Improve ML model performance Feature Engineering Steps Feature Engineering Pipeline Split Dataset Feature Engineering
- 14. 14 BERT Embedding SageMaker Processing with scikit-learn Parameters: code, processingInput, processingOutput
- 15. 15 Feature Store – Reuse the feature engineering results Centralized Reusable Discoverable
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- 22. 22 Artifact • the output of a step or task can be consumed the next step in a pipeline or deployed directly for consumption SageMaker Pipelines
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- 24. 24 Feature Engineering and Feature Store: • RoBERTa: A Robustly Optimized BERT Pretraining Approach • Fundamental Techniques of Feature Engineering for Machine Learning Train, Debug, and Profile a Machine Learning Model: • PyTorch Hub • TensorFlow Hub • Hugging Face open-source NLP transformers library • RoBERTa model • Amazon SageMaker Model Training (Developer Guide) • Amazon SageMaker Debugger: A system for real-time insights into machine learning model training • The science behind SageMaker’s cost-saving Debugger • Amazon SageMaker Debugger (Developer Guide) • Amazon SageMaker Debugger (GitHub) Deploy End-To-End Machine Learning Pipelines: • A Chat with Andrew on MLOps: From Model-centric to Data-centric AI
- 25. 25 3. Optimize ML Models and Deploy Human-in-the-Loop Pipelines
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- 41. 41 Advanced model training, tuning, and evaluation: • Hyperband • Bayesian Optimization • Amazon SageMaker Automatic Model Tuning Advanced model deployment, and monitoring: • A/B Testing • Autoscaling • Multi-armed bandit • Batch Transform • Inference Pipeline • Model Monitor Data labeling and human-in-the-loop pipelines: • Towards Automated Data Quality Management for Machine Learning • Amazon SageMaker Ground Truth Developer Guide • Create high-quality instructions for Amazon SageMaker Ground Truth labeling jobs • Amazon SageMaker Augmented AI (Amazon A2I) Developer Guide