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Data Ops：從實驗室走進生產線, 談談怎麼和資料科學家合作
- 3. 我以為的資料科學家
- 6. 當我想做一個「資料科學」專案的時候 資料清洗 資料分析 資料驗證 資料切分 訓練模型 驗證模型
- 8. 當我想做一個「資料科學」產品的時候 資料清洗 資料分析 資料驗證 資料切分 訓練模型 驗證模型
- 9. 當我想做一個「資料科學」產品的時候 資料清洗 資料分析 資料驗證 資料切分 訓練模型 驗證模型 規模訓練 模型更新 模型上線 模型監控 模型日誌 模型優化
- 10. 資料科學工作流程 • 一致的編排與環境 • 可擴張的團隊建模協作 • 持續滿足需求 • 改進迭代週期自動部署 • 可重現的結果 • 監控品質與效能測試監控
- 12. 資料＋運維 • 資料＋運維＝DataOps • 讓所有資料從業人員（包含資料分析師、資料科學 家、資料工程師和 IT 人員等等）一起來持續地遞 交有品質的資料給應用及商業流程。
- 17. 實踐 DataOps Source: KubeCon Europe 2018
- 21. SQL DB Cosmos DB Datawarehouse Data lake Blob storage … Prepare Data Build & Train Deploy Machine Learning Process
- 23. Model Creation Is Typically Time-Consuming Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf Others Model Which algorithm? Which parameters?Which features? Car brand Year of make
- 24. Criterion Loss Min Samples Split Min Samples Leaf Others N Neighbors Weights Metric P Others Which algorithm? Which parameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Nearest Neighbors Model Iterate Gradient BoostedMileage Car brand Year of make Car brand Year of make Condition Model Creation Is Typically Time-Consuming
- 25. Which algorithm? Which parameters?Which features? Iterate Model Creation Is Typically Time-Consuming
- 27. Dataset Training Algorithm 1 Hyperparameter Values – config 1 Model 1 Hyperparameter Values – config 2 Model 2 Hyperparameter Values – config 3 Model 3 Model Training InfrastructureTraining Algorithm 2 Hyperparameter Values – config 4 Model 4 Model Selection & Hyperparameter Tuning
- 29. Enter data Define goals Apply constraints Output Automated ML Accelerates Model Development Input Intelligently test multiple models in parallel Optimized model
- 30. Automated ML Customer Testimonials • Press-coverage from public preview: • CNET • VentureBeat • PRNewswire “I quite like your AutoML function. It gives me good results compared to other libraries I tested before (tpot and auto-sklearn) that I believe was only looking at scores and often gave me models that over-trained my data. And of course the model from your suggested code is better.” - Big oil company “I will start with AutoML and use the algorithm that AutoML recommends to further tune the model” - Data Scientist “I actually enjoy being able to use AutoML in a Jupyter notebook. The DataRobot interface was nice for non-experts, but for someone like me, it felt a bit basic.” - Data Scientist
- 32. Automated ML Capabilities • Based on Microsoft Research • Brain trained with several million experiments • Collaborative filtering and Bayesian optimization • Privacy preserving: No need to “see” the data
- 33. Automated ML Capabilities • ML Scenarios: Classification & Regression, Forecasting • Integration: Azure Machine Learning, Azure Notebooks, Jupyter Notebooks • Data Type: Numeric, Text • Languages: Python SDK for deployment and hosting for inference • Training Compute: Local Machine, Remote Azure DSVM (Linux), Azure Batch AI, Databricks • Transparency: View run history, model metrics • Scale: Faster model training using multiple cores and parallel experiments
- 34. • Dropping high cardinality or no variance features • Missing value imputation • Generating additional features • Transformations and encodings Feature Engineering
- 36. Q & A