AWS Ninja

Create a CloudWatch billing alarm to send email alerts when charges exceed a chosen threshold, using SNS notifications and a six-hour monitoring period for proactive cost control.

Get started with Node.js by using npm to manage dependencies, install tools, and set up a JavaScript development environment across Windows and other platforms.

Install and verify the AWS CLI by following simple package installation steps, including uninstalling old versions, using a package installer, and confirming successful setup in the terminal.

Install and configure the serverless framework to build a backend, deploy Lambda functions and API Gateway, and manage programmatic access with AWS credentials for a scalable app.

Learn to set up a react framework by installing npm packages, creating an app with create-react-app, and exploring the generated structure (node_modules, public, src, package.json) before starting the dev server.

Discover serverless architecture and how to deploy code without provisioning, with auto scaling and pay-per-use compute using function as a service, backed by managed services like S3, Lambda, and DynamoDB.

Build a serverless audio solution with AWS Polly to convert text into lifelike speech across languages, using API Gateway, Lambda, DynamoDB, S3, and SNS, orchestrated via CloudFormation.

Create an IAM policy for lambda to access essential services, define resources in a policy document, attach it to a role, and review monitoring with CloudWatch.

Learn to wire AWS Lambda with SNS by creating a new SNS topic and configuring an environment variable. Test messaging that stores posts in the database and enables video playback.

Open the api gateway console, create a REST API, enable get and post methods, configure lambda integration, enable cors, and deploy to generate a usable endpoint for your web app.

Build a react front end with axios for API calls and semantic ui styling, featuring tables and an audio player, and outline local deployment to a bucket for end-to-end architecture.

Build a production-ready web app, create a distribution, and deploy to an S3 bucket with static website hosting enabled, public access, and a bucket policy, with API Gateway integration.

Recap core AWS services like API gateway, Lambda, S3, Polly, and DynamoDB, highlighting key features, security, scaling, and serverless architectures.

Learn how to build a backend with the serverless framework, deploying three lambda functions, api gateway endpoints, and supporting resources like s3 buckets and dynamodb via cloud formation.

We deploy the frontend with the serverless framework, create an S3 bucket and API endpoints, configure the API key, build locally with npm, and deploy via CloudFormation.

Use API keys and usage plans to monetize your API with free and paid tiers, such as 100 requests per month and up to 20,000, plus throttling and auto scaling.

Explore the evolution from traditional data warehouses to data lakes, Hadoop, and cloud-based architectures, and learn how storage and processing split enables analytics at scale.

Explore lambda architecture as a scalable big data design with a batch processing layer and a real-time speed layer, backed by storage like S3 or Dynamo.

Build a near real-time data pipeline using lambda architecture, streaming clickstream data to Genesis, processing with lake and Glue, querying with Athena, and visualizing in quick site via Airflow.

Build and run a near real-time analytics React app that streams clickstream data from an Amazon customer dataset, wiring Amplify, Cognito authentication, and Kinesis Firehose for analytics.

Set up Cognito identity pools to grant temporary credentials for a React app to publish streaming data to Kinesis, configure Firehose to S3 and Parquet conversion, then query and visualize.

Explore visualizing data with AWS QuickSight by creating accounts, connecting data sources, building simple visualizations like bar charts, and publishing dashboards with basic permissions.

Explore near real-time data pipelines using AWS services like kinesis and firehose, with glue data catalog, crawlers, cognito, presto, and quick sight, plus batch processing and data transformation.

Build a batch data pipeline with Airflow to orchestrate a DAG using EMR, Livy, and Spark to transform data from S3 into a new format, then store results.

Learn how the Hadoop ecosystem, including Spark, EMR, YARN, and the distributed file system, enables scalable big-data processing with MapReduce, data nodes, and Hive and Livy integrations.

Provision an IAM role, security group, and S3 bucket to run Airflow on an EMR cluster, then deploy startup scripts and Livy REST API integration.

Set up an RDS instance for airflow using Postgres, configure a username and password, enable password authentication, make it publicly accessible, and note the endpoint for the next lecture.

Learn to design and run a data pipeline with Airflow, EMR, and Livy, including cluster creation, transformation, spark sessions, and batch execution.

Kick off the airflow pipeline to transform data into an S3 bucket using a temporary EMR cluster, monitor Livy Spark jobs, and securely terminate resources to control costs.

Differentiate artificial intelligence, machine learning, data science, and examine supervised, unsupervised, and reinforcement learning with neural networks powering deep learning. See how these techniques solve problems with models and classification.

Master the machine learning lifecycle from problem framing to model deployment, including data preparation, feature engineering, and training versus testing with evaluation metrics.

Learn to set up a Jupyter notebook on AWS EC2 or your local machine, using Anaconda for a complete data science toolkit and an interactive ML development environment.

Explore how a neural network mimics the brain, from perceptron inputs to deep networks and CNNs, and learn how activation functions, loss, learning rate, and backpropagation drive training.

Learn to deploy Seesmic end-to-end, moving from notebook work on local or instance environments to scalable endpoints. Manage artifacts, endpoints, and automated scaling with Lambda API Gateway.

Explore the SageMaker lifecycle from setting up a notebook instance and labeling data with Amazon Mechanical Turk to training a CNN, deploying an endpoint, and using accelerators for scalable inference.

Explore how a self-driving system uses a camera vision setup, a processing unit such as Jetson Nano, and a motor driver to run a neural network for collision avoidance.

Access the Jupyter notebook on Jetson Nano, using the IP address and default credentials, explore Jupyter Lab with collision-avoidance examples, and begin training a model while wiring up the hardware.

Run your self-driving car robot with basic motion control and collision avoidance. Train neural networks on prepared datasets using Python, then deploy models to cloud and explore augmented reality testing.

Explore extending a data lake solution with machine learning and deep learning, review current data lake architecture, and preview self-driving car examples to demonstrate practical hands-on value.

Explore natural climate solutions like trees, mangroves, peat bogs, and oceans that remove carbon. Urge leaving fossil fuels in the ground and empower individuals to act.