Free AWS Crash Course with AI Integration – Coming Soon to Kubesimplify YouTube

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Saiyam Pathak, in collaboration with Sandip Das, is launching a FREE AWS Crash Course with an AI touch on the Kubesimplify YouTube channel by the end of this month. This course will cover:

  • Cloud fundamentals and why businesses are migrating to the cloud
  • Deep dives into AWS services (EC2, S3, VPC, Lambda, EKS)
  • Hands-on demos and best practices for cost management
  • AWS AI/ML tools in the ecosystem

Whether you’re a beginner or looking to enhance your cloud and AI skills, this course will provide practical knowledge with real-world demos.

https://www.youtube.com/c/Kubesimplify

You Should Know:

1. Basic AWS CLI Commands

 Configure AWS CLI 
aws configure

List S3 Buckets 
aws s3 ls

Launch an EC2 Instance 
aws ec2 run-instances --image-id ami-0abcdef1234567890 --instance-type t2.micro --key-name MyKeyPair

Create an S3 Bucket 
aws s3 mb s3://my-unique-bucket-name

Lambda Function Deployment 
aws lambda create-function --function-name my-function --runtime python3.9 --handler lambda_function.lambda_handler --role arn:aws:iam::123456789012:role/lambda-role --zip-file fileb://function.zip

2. Kubernetes (EKS) Commands

 Install eksctl 
curl --silent --location "https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_$(uname -s)_amd64.tar.gz" | tar xz -C /tmp 
sudo mv /tmp/eksctl /usr/local/bin

Create an EKS Cluster 
eksctl create cluster --name my-cluster --region us-west-2 --nodegroup-name my-nodes --node-type t3.medium --nodes 3

Deploy a Sample App 
kubectl create deployment nginx --image=nginx 
kubectl expose deployment nginx --port=80 --type=LoadBalancer

3. AI/ML with AWS SageMaker

 Install AWS SDK for Python (Boto3) 
pip install boto3

Train a Model 
import boto3 
sagemaker = boto3.client('sagemaker') 
response = sagemaker.create_training_job( 
TrainingJobName='my-training-job', 
AlgorithmSpecification={ 
'TrainingImage': '123456789012.dkr.ecr.us-west-2.amazonaws.com/my-algorithm:latest', 
'TrainingInputMode': 'File' 
}, 
RoleArn='arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole', 
InputDataConfig=[ 
{ 
'ChannelName': 'train', 
'DataSource': { 
'S3DataSource': { 
'S3DataType': 'S3Prefix', 
'S3Uri': 's3://my-bucket/train/', 
'S3DataDistributionType': 'FullyReplicated' 
} 
} 
} 
], 
OutputDataConfig={ 
'S3OutputPath': 's3://my-bucket/output/' 
}, 
ResourceConfig={ 
'InstanceType': 'ml.m4.xlarge', 
'InstanceCount': 1, 
'VolumeSizeInGB': 10 
}, 
StoppingCondition={ 
'MaxRuntimeInSeconds': 3600 
} 
)

What Undercode Say:

This AWS + AI crash course will be a game-changer for cloud engineers and AI enthusiasts. With hands-on AWS CLI, Kubernetes (EKS), and SageMaker integrations, learners can expect real-world cloud automation and AI deployment skills.

🔗 Expected Output:

  • AWS Cloud Practitioner-level knowledge
  • Automated deployments using AWS CLI
  • Kubernetes cluster management
  • AI model training with SageMaker

Prediction:

This course will rapidly gain traction, helping 10,000+ learners in the first month, making AWS + AI skills more accessible to developers worldwide. 🚀

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