Set-Up Cloud Environment and Model Training:

1.Set Up AWS Account:

• Sign in to your AWS account or create a new one.
• Ensure you have the necessary permissions to launch EC2 instances and work with S3 buckets if needed for data storage.

2.Launch EC2 Instances for Training:

• Navigate to the EC2 dashboard and launch four instances.
• Choose an Ubuntu Server image.
• Select instance types suitable for parallel processing.
• Configure instance details to launch all instances within the same VPC and, preferably, in the same Availability Zone to minimize network latency.
• Add necessary storage and tags as required.
• Configure the security group to allow traffic on required ports, particularly for Spark communication and SSH.
• Review and launch instances, making sure to select or create a new key pair for SSH access.

3.Set Up Apache Spark:

• SSH into each EC2 instance using the key pair.
• Install Java, as it's required for Spark.
• Download and install Apache Spark on all instances.
• Configure one instance to act as the master and the others as workers.
• Start the Spark cluster by running the start-master.sh script on the master node and start-slave.sh on each worker node, pointing to the master.

4.Data Preparation:

• Upload the TrainingDataset.csv, ValidationDataset.csv, Dockerfile and training.py to a location accessible by all EC2 instances, such as an S3 bucket or distributed file system supported by Spark.

5.Model Training:

• Write a Spark application in Java for training your ML model using MLlib.
• Utilize the Spark context to distribute the job across the cluster.
• Train the model with TrainingDataset.csv and validate with ValidationDataset.csv to optimize the model's parameters.
• Save the trained model to a persistent storage location.

Application Prediction:

1.Single EC2 Instance for Prediction:

• Launch a separate EC2 instance using the same previous steps but only a single instance is needed.
• Deploy the Spark application for prediction on this instance.
• Ensure your model and the TestDataset.csv (or ValidationDataset.csv for testing purposes) are accessible by this instance.

2.Prediction Without Docker:

• Run the Spark application directly on the instance, making sure Spark submits the job with the trained model and test dataset.
• The application should output the prediction performance, specifically the F1 score.

3.Prediction With Docker:

• Install Docker on the EC2 instance if not already present.
• Create a Dockerfile for your prediction application.
• Build the Docker image and push it to Docker Hub.
• Pull the Docker image from Docker Hub onto your EC2 instance.
• Run the Docker container with necessary volume mounts to access the model and test data.
• The Docker container should run the Spark application and output the F1 score.

4.Github Account:

• Create or Login to the github account.
• Now create the new repository in github account.
• And push all the codes to the github accout.