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The AWS Developer Center has introduced a new learning pathway titled “Generative AI for Developers”, which is a comprehensive resource for anyone looking to dive into generative AI and build AI applications on AWS. This pathway includes courses, videos, code walkthroughs, and hands-on tutorials, all designed to help you master generative AI using professional tools like Amazon Bedrock, Amazon Q, and Amazon SageMaker JumpStart.
Check out the pathway here: AWS Generative AI for Developers
You Should Know:
Here are some practical commands and code snippets to get you started with AWS and generative AI:
1. Installing AWS CLI:
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" unzip awscliv2.zip sudo ./aws/install
2. Configuring AWS CLI:
aws configure
You’ll be prompted to enter your AWS Access Key, Secret Key, region, and output format.
3. Creating an S3 Bucket:
aws s3 mb s3://my-generative-ai-bucket
4. Deploying a SageMaker Notebook Instance:
aws sagemaker create-notebook-instance --notebook-instance-name my-notebook --instance-type ml.t2.medium --role-arn arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001
5. Using Amazon Bedrock:
aws bedrock create-model --model-name my-gen-ai-model --model-artifact-location s3://my-generative-ai-bucket/model.tar.gz --role-arn arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001
6. Running a SageMaker Training Job:
aws sagemaker create-training-job --training-job-name my-training-job --algorithm-specification TrainingImage=382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest,TrainingInputMode=File --role-arn arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001 --input-data-config '[{"ChannelName":"train","DataSource":{"S3DataSource":{"S3DataType":"S3Prefix","S3Uri":"s3://my-generative-ai-bucket/train/","S3DataDistributionType":"FullyReplicated"}}}]' --output-data-config S3OutputPath=s3://my-generative-ai-bucket/output/ --resource-config InstanceType=ml.m4.xlarge,InstanceCount=1,VolumeSizeInGB=50 --stopping-condition MaxRuntimeInSeconds=3600
7. Using Amazon Q for Code Assistance:
aws q start-session --region us-east-1 --profile my-profile
8. Monitoring AWS Resources:
aws cloudwatch get-metric-statistics --namespace AWS/SageMaker --metric-name CPUUtilization --start-time 2023-10-01T00:00:00Z --end-time 2023-10-31T23:59:59Z --period 3600 --statistics Average
What Undercode Say:
The AWS Developer Center’s Generative AI for Developers pathway is an excellent resource for developers looking to upskill in AI and cloud technologies. The hands-on tutorials and professional tools provided by AWS make it easier to transition from theoretical knowledge to practical application. Whether you’re working with Amazon Bedrock, SageMaker, or Amazon Q, these tools are essential for building and deploying AI models at scale. The AWS CLI commands provided above will help you get started with managing your AI resources efficiently. For more advanced use cases, consider exploring the AWS documentation and community forums.
For further reading, visit the AWS Developer Center.
References:
Reported By: Nikkisiapno This – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
