Why Choose This Project?
Data science workflows often require powerful compute resources, pre-configured libraries, and collaborative environments. Deploying Jupyter Notebook on AWS provides a scalable, cloud-based data science environment that is accessible from anywhere.
This project is ideal for students to learn cloud-based data analysis, machine learning, and collaborative development without worrying about local hardware limitations.
What You Get
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Cloud-hosted Jupyter Notebook environment for data science
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Pre-installed Python libraries for ML, AI, and data analytics
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GPU-enabled compute for training ML/DL models (optional)
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Collaboration between multiple users (via shared notebooks)
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Integration with cloud storage and databases for large datasets
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Secure access with user authentication and role management
Key Features
| Feature |
Description |
| Cloud-hosted Jupyter |
Access notebooks from anywhere with a web browser |
| Pre-installed Libraries |
NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn |
| GPU/CPU Compute |
Scale compute resources based on workload |
| Data Integration |
Connect to S3, DynamoDB, RDS, and external datasets |
| Collaboration |
Share notebooks and collaborate in real-time |
| Version Control |
Optional Git integration for notebook versioning |
| Secure Access |
User authentication and HTTPS access |
| Scalability |
Auto-scale instances for multiple users or heavy workloads |
Technology Stack
| Layer |
Tools/Technologies |
| Frontend |
Jupyter Notebook / JupyterLab (web-based UI) |
| Backend |
AWS EC2 / AWS SageMaker Notebooks / AWS EMR (optional) |
| Storage |
AWS S3 for datasets and notebook storage |
| Authentication |
AWS IAM or Cognito for secure user access |
| Compute |
EC2 instances with optional GPU (NVIDIA) |
| Monitoring |
CloudWatch for usage, logs, and performance metrics |
AWS Services Used
| AWS Service |
Purpose |
| EC2 / SageMaker Notebooks |
Host Jupyter notebooks and provide compute resources |
| S3 |
Store datasets, notebook files, and model artifacts |
| IAM / Cognito |
User authentication and access control |
| CloudWatch |
Monitor resource usage, performance, and logs |
| EMR / Lambda (Optional) |
Data processing pipelines for big datasets |
| EBS / EFS |
Persistent storage for notebooks and intermediate data |
Working Flow
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Environment Setup
Launch Jupyter Notebook on AWS EC2 or SageMaker with necessary Python libraries.
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Data Loading
Access datasets from S3 buckets, RDS, or external APIs.
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Data Analysis & Processing
Perform preprocessing, visualization, and analysis using Python libraries.
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Model Training & Evaluation
Train ML/DL models using CPU/GPU compute, save models to S3.
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Collaboration
Share notebooks with team members for collaborative editing and experiments.
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Optional Automation
Integrate Lambda or EMR for batch data processing pipelines feeding into notebooks.
Customization
Fully
Security
High
Performance
Fast
Future Updates
Free
Total Buyers
500+
Support
Lifetime
