Machine Learning & Containers on AWS

1h 33m 34s
English
Paid
In this practical course, you will learn to build a complete data pipeline on the AWS platform, from acquiring data from the Twitter API to analysis, storage, and visualization. You will create your own machine learning algorithm and deploy it on AWS using Lambda. Additionally, you will set up a Postgres database using Amazon RDS. For visualizing the results, you will develop an interactive dashboard with Streamlit and gain experience deploying it in containers using Elastic Container Registry (ECR) and Elastic Container Service (ECS). Moreover, the course will introduce you to the Poetry tool and teach you how to manage your project's dependencies.
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Course Structure

Twitter API

Twitter API is a great place for accessing open data. You will learn how to configure access to the API and retrieve tweets from a user's feed for further processing. We will delve into API configuration and the data format (payload) it returns.

RDS Database

Every platform needs a data storage. You will learn how to set up a Postgres database in Amazon RDS and understand why we will be saving JSON tweets in this database. You will also master working with virtual private clouds (VPC) to make the database accessible from the internet. With PGAdmin, you will create tables and execute queries on the database.

NLP Lambda

For text analysis, we will use a ready-made machine learning algorithm from the Natural Language Toolkit (NLTK) library. You will create a Lambda function to retrieve tweets from the API, determine their sentiment, and save the results in the database.

To run the Lambda function, you will learn to connect the necessary dependencies through layers - how to import prepared K-Layers and create your own layer. You will also learn how to set up an automatic Lambda function trigger using Event Bridge.

Dependency Management and Streamlit Application

For visualizing results, you will create an application using Streamlit. You will set up a local development environment with Anaconda3 and create a conda virtual environment. Using the provided Git repository, you will learn to manage project dependencies with Poetry. We will go through the application code step by step and demonstrate how to run it in a new virtual environment for testing.

Deploying Streamlit Application in ECS

Once the visualization is ready, you will learn to work with Docker images and containers in AWS. You will create an Elastic Container Registry (ECR) and set up AWS CLI. You will learn to create user groups and individual users with restricted access rights in IAM.

After building the Docker image, you will upload it to ECR, configure an ECS Fargate cluster, and deploy your Streamlit application as a task on the platform.

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# Title Duration
1 Introduction video 02:39
2 Project architecture explained 02:07
3 Relational DB 01:27
4 RDS setup 02:38
5 Setting VPC inbound rules for internet access 02:13
6 PG Admin installation & S3 config 04:06
7 Lambda intro & IAM setup 03:12
8 Create Lambda function 01:25
9 The Lambda function code explained 08:23
10 Insert the code into your Lambda function 00:57
11 Add layers to Lambda from Klayers 05:33
12 Create & configure custom layers for twython & psycopg2 04:41
13 Test Lambda & set environment variables 04:54
14 Schedule your Lambda with Event Bridge 03:16
15 Setup virtual conda environment 04:08
16 Poetry dependency installs & run Streamlit UI locally 05:58
17 Streamlit app code explained 07:53
18 Setup container registry ECR 01:53
19 AWS CLI install and ECR login 05:20
20 Dockerfile explained, Docker image build & push image to ECR 02:53
21 Create ECS Fargate cluster 01:35
22 ECS task IAM configuration & Streamlit task creation 05:00
23 Fixing the ECS task 05:15
24 Stopping the task on ECS after you are finished 01:00
25 Conclusion & outlook 05:08

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