- Course Details
- Syllabus: Available on Canvas
- TA Hours: Updated on Slack
- Most labs require an Amazon Web Services account to deploy and run. Signup for an AWS Account here.
- Python 3.7+
- Refer to the
requirements.txtfile orREADME.mdinside the respective directories to install all dependencies.
Sign up for an AWS Account here. Additonally, the AWS Command Line Interface is required to interact with AWS Services. Download AWS CLI from here
Download your AWS Access and Secret access keys for your AWS Account. Steps to generate and download your keys can be found here
Open command line tool of choice on your machine and run aws configure. Enter your access and secret access keys and leave the default region name and output format as null.
$ aws configure
AWS Access Key ID [None]: AKIAIOSFODNN7EXAMPLE
AWS Secret Access Key [None]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Default region name [None]:
Default output format [None]: json
Set up billing alerts for your AWS Account here
Refer README.md inside the respective directories for setup instructions.
- ✅ Getting Started with AWS:
aws-basics - ✅ Kafka:
kafka - ✅ Dask:
dask - ✅ Plotly Dash:
plotly-dash - ✅ SQLAlchemy:
sql-alchemy - ✅ Streamlit:
streamlit - ✅ Airflow TFX:
airflow_tfx - ✅ FastAPI:
fast-api - ✅ Airflow:
airflow_cnn_pipeline - ✅ Tensorboard:
tensorboard - ✅ MLflow:
mlflow - ✅ Docker + FastAPI:
docker - ✅ Tensorboard:
tensorboard - ✅ Graphana, Prometheus, Kubernetes:
kubernetes-ml-monitoring - ✅ AWS Lambda - ML Inference:
ml-inference-with-lambda - ✅ ML Flow + DeltaLake:
mlflow-deltalake