You will get Serverless AI Model Deployment with API Gateway and Lambda via Terraform.
Project details
Project Name: Serverless AI Model Deployment with API Gateway and Lambda
This project utilizes AWS's serverless architecture to deploy AI and ML models through API Gateway and Lambda. We offer a scalable, cost-effective, and fully managed deployment solution. By employing serverless technologies, we ensure your model can scale dynamically without server management hassles.
Requirements for Deployment:
Working Inference Model: Provide a trained model or an ensemble capable of making predictions.
Data Schema for Prediction: Submit a schema detailing the input data format.
Model Location: Specify the model location in Amazon S3 (s3://) or Google Cloud Storage (gs://), provided in a .txt file.
Service Tiers:
Starter: Source code deployment.
Standard: Adds data source connectivity and source code deployment.
Advanced: Includes model validation/testing, documentation, data source connectivity, and source code deployment.
This solution ensures efficient model deployment with high scalability and minimal operational overhead.
This project utilizes AWS's serverless architecture to deploy AI and ML models through API Gateway and Lambda. We offer a scalable, cost-effective, and fully managed deployment solution. By employing serverless technologies, we ensure your model can scale dynamically without server management hassles.
Requirements for Deployment:
Working Inference Model: Provide a trained model or an ensemble capable of making predictions.
Data Schema for Prediction: Submit a schema detailing the input data format.
Model Location: Specify the model location in Amazon S3 (s3://) or Google Cloud Storage (gs://), provided in a .txt file.
Service Tiers:
Starter: Source code deployment.
Standard: Adds data source connectivity and source code deployment.
Advanced: Includes model validation/testing, documentation, data source connectivity, and source code deployment.
This solution ensures efficient model deployment with high scalability and minimal operational overhead.
Machine Learning Tools
BERT, Keras, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlow, Tesseract OCR, XGBoostWhat's included
| Service Tiers |
Starter
$250
|
Standard
$375
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 30 days | 21 days | 14 days |
Number of Revisions | 2 | 3 | 6 |
Model Validation/Testing | - | - | |
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$70 - $125Frequently asked questions
About Mustafa
Machine Learning Engineer
Adapazari, Turkey - 2:21 pm local time
I’m certified as a Google Cloud Associate Cloud Engineer and have a proven track record in Kaggle competitions, where I’ve honed my skills in Python, deep learning, and data visualization. Whether you need an AI model that delivers results, cloud-native architecture that scales, or a project managed from start to finish, I’m here to help.
What I bring to the table:
• Advanced expertise in AI and machine learning, including TensorFlow, Scikit-learn, and Docker.
• Experience in deploying and managing cloud applications on platforms like Google Cloud and AWS.
• Strong project management skills, ensuring that every detail is handled and that projects are completed on time and within budget.
• Continuous communication to keep you updated every step of the way.
When I’m not working, you’ll find me playing chess, running, or diving into a good book. Let’s connect and discuss how I can help bring your project to life!
Steps for completing your project
After purchasing the project, send requirements so Mustafa can start the project.
Delivery time starts when Mustafa receives requirements from you.
Mustafa works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Setup & Review
Review the provided requirements and model. Ensure all required details and files are in order.
Model Deployment Configuration
Configure the AWS Lambda function and API Gateway settings for the model deployment based on the provided schema and model