Recommended architecture for a ML project using AWS

Every ML project is unique and has different requirements. However, here is a recommended architecture using AWS technologies that involves end to end life cycle of a ML Project:

1. Data ingestion: The first step is to collect and store the data that will be used to train the model. Depending on the data source and format, this may involve using tools like Apache Kafka, AWS Kinesis, or Google Cloud Pub/Sub.
2. Data Storage: Store the dataset in Amazon S3 as a columnar format, such as Parquet, to optimize query performance and reduce storage costs.
3. Data Processing: Use AWS Glue to preprocess and transform the data, applying data augmentation techniques such as adding noise or generating synthetic samples. AWS Glue can handle large volumes of data and perform data cleaning, normalization, and feature engineering.
4. Training: Use Amazon SageMaker to train the machine learning model on the preprocessed data. SageMaker provides managed Jupyter notebooks for model development, as well as pre-built algorithms such as XGBoost, Random Cut Forest, or TensorFlow. You can also bring your own algorithms, and SageMaker will handle the scaling and deployment of the training job.
5. Model Deployment: After training, deploy the model to Amazon SageMaker hosting services, where it can receive inference requests and provide predictions. SageMaker hosting provides scalable and secure hosting for the trained model, with automatic scaling based on incoming traffic.
6. Continuous Retraining: Use Amazon SageMaker Automatic Model Tuning to automatically retrain the model every week. This feature allows you to define a hyperparameter search space and a performance metric, and SageMaker will launch multiple training jobs with different hyperparameters, select the best-performing one, and deploy it automatically.
7. Monitoring: Monitor the performance of the deployed model using Amazon CloudWatch. You can set alarms based on metrics such as prediction latency, prediction accuracy, or endpoint health, and receive notifications if the metrics go beyond predefined thresholds.
8. Feedback Loop: Use Amazon S3 to store feedback data from users and use Amazon SageMaker Ground Truth to label the data and improve the training dataset for the next retraining cycle.

Overall, this architecture leverages several AWS services to provide a scalable, secure, and cost-effective solution for a machine learning project with continuous retraining.