dasar Machine Learning hingga Deep Learning - Membuat dan mengoptimalkan model Machine Learning hingga Deep Learning - Mempelajari berbagai algoritma Machine Learning hingga Deep learning Machine Learning in Google Cloud Series Series Machine Learning in Google Cloud berfokus pada: - Machine Learning di lingkungan produksi - Membuat model machine learning dengan bantuan Cloud Computing - Belajar melakukan deployment model Machine Learning dengan menjadikannya sebagai API Selamat Datang di Series Machine Learning in Google Cloud
system ML Code Analysis Tools Process Management Tools Feature Engineering Data Collection Data Extraction Machine Resource Management Configuration Serving Infrastructure
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments
+ 1 X = -1 Data yang diketahui Y= 2x + 1 X = -1, 0, 2, 3, 4 Data yang diketahui Y = -1, 1, 5, 7, 9 Rules Rules Answers Answers , 0, 2 , 3, 4 Y = -1 , 1 , 5 , 7, 9
Lite (For Mobile & Edge) TensorFlow Extended (TFX) (For Production) Membuat machine learning model menggunakan Python. Membuat dan menjalankan ML model di lingkungan web dengan JavaScript. Menjalankan inference di lingkungan mobile dan embedded device seperti Android, iOS, hingga Raspberry Pi. Menjalankan Machine Learning Pipeline di lingkungan produksi seperti Google Cloud.
TensorFlow Lite (For Mobile & Edge) Format Model: - .keras format - SavedModel - HDF5 Format Model: - TensorFlow.js Model Format Format Model: - FlatBuffer
model yang berhasil dilatih digunakan untuk memprediksi data baru yang belum pernah dilihat sebelumnya (selama fase training) Model Data Baru (Input) Hasil Prediksi (Output)
Model Training Model Evaluation Model Validation Trained Model Model Registry Prediction Service Performance Monitoring Experimentation/development/test environments