Phoenix - Your AI AssistantBuild and deploy a machine learning API with FastAPI and Docker
Delivery in
3 days
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What you get with this Offer
Move your Machine Learning project beyond a notebook and turn it into a working, deployable API.
I will handle the complete workflow—from preparing your data and building the model to creating a tested API, packaging it with Docker and deploying it to an agreed hosting environment.
My approach includes:
• Understanding your business objective and success criteria
• Reviewing, cleaning and preparing your data
• Building and comparing suitable Machine Learning models
• Optimising performance against an agreed baseline
• Selecting the final model according to both technical results and business value
• Creating a FastAPI service for real-time predictions
• Containerising the application with Docker
• Testing the prediction and health endpoints
• Deploying the service to one agreed hosting platform
• Adding basic logging and health monitoring
The model will not be selected based on accuracy alone. Where relevant, I will consider the business cost of false predictions, operational constraints, response time and the expected value of the model.
You will receive:
• A trained and evaluated Machine Learning model
• Clean and documented Python source code
• A FastAPI prediction API
• Input validation and error handling
• /predict and /health endpoints
• Dockerfile and dependency configuration
• Essential API and model tests
• Interactive API documentation
• Deployment and usage instructions
• A deployed endpoint on the agreed platform
• Seven days of support for delivery-related bugs
Base offer scope:
• One CSV or Excel dataset
• Up to 50,000 rows and 30 input features
• One Machine Learning objective
• One trained model
• One prediction API
• One deployment environment
• A standard CPU-based classification or regression model
Cloud usage fees, GPU models, custom front-end development, real-time data ingestion, enterprise security, scheduled retraining and complex database integrations are not included in the base offer.
Please contact me before ordering to confirm your data, hosting platform, expected traffic and deployment requirements.
I will handle the complete workflow—from preparing your data and building the model to creating a tested API, packaging it with Docker and deploying it to an agreed hosting environment.
My approach includes:
• Understanding your business objective and success criteria
• Reviewing, cleaning and preparing your data
• Building and comparing suitable Machine Learning models
• Optimising performance against an agreed baseline
• Selecting the final model according to both technical results and business value
• Creating a FastAPI service for real-time predictions
• Containerising the application with Docker
• Testing the prediction and health endpoints
• Deploying the service to one agreed hosting platform
• Adding basic logging and health monitoring
The model will not be selected based on accuracy alone. Where relevant, I will consider the business cost of false predictions, operational constraints, response time and the expected value of the model.
You will receive:
• A trained and evaluated Machine Learning model
• Clean and documented Python source code
• A FastAPI prediction API
• Input validation and error handling
• /predict and /health endpoints
• Dockerfile and dependency configuration
• Essential API and model tests
• Interactive API documentation
• Deployment and usage instructions
• A deployed endpoint on the agreed platform
• Seven days of support for delivery-related bugs
Base offer scope:
• One CSV or Excel dataset
• Up to 50,000 rows and 30 input features
• One Machine Learning objective
• One trained model
• One prediction API
• One deployment environment
• A standard CPU-based classification or regression model
Cloud usage fees, GPU models, custom front-end development, real-time data ingestion, enterprise security, scheduled retraining and complex database integrations are not included in the base offer.
Please contact me before ordering to confirm your data, hosting platform, expected traffic and deployment requirements.
What the Freelancer needs to start the work
To get started, please provide:
• Your business objective and expected outcome
• Your dataset or existing trained model
• A description of the dataset columns
• The input data the API will receive
• The expected prediction output
• Your preferred hosting platform
• Expected traffic or number of prediction requests
• Any response-time, security or compliance requirements
• Your current performance baseline, if available
• Temporary deployment access or API keys when required
The Buyer is responsible for hosting, cloud and third-party service charges. Please anonymise sensitive data and do not share permanent passwords.