Creating ML API/pipelines
Starting at
$
50
About this service
Summary
As part of this gig, I aim to develop an API that enables users to interact with machine learning models through a web interface. My goal is to create an API that allows users to easily upload data, select and train machine learning models, and perform predictions.
The project will involve designing and implementing a RESTful API that allows users to upload data, perform inference using pre-trained machine learning models, and retrieve results. To ensure optimal performance and scalability, the API will be designed to be efficient and integrate with cloud infrastructure services such as Amazon Web Services (AWS) or Google Cloud Platform (GCP).
I have extensive experience in machine learning, software engineering, and API development. Throughout the project, I will work closely with you to understand your specific needs and requirements, and tailor the solution to meet your goals. I will provide clear and concise documentation, as well as support and training to ensure that you and your team can effectively use the API.
What's included
Deliverables
The project will involve the following tasks: Model selection: Select appropriate machine learning models to be deployed as part of the API. API design: Design a RESTful API that allows users to interact with the machine learning models. API implementation: Implement the API using a web framework such as Flask or Django. Cloud infrastructure setup: Set up cloud infrastructure services such as AWS or GCP to host the API and machine learning models. Data preprocessing: Develop data preprocessing pipelines to prepare input data for the machine learning models. Model training: Train the machine learning models on appropriate datasets. Model integration: Integrate the trained machine learning models with the API. Testing and validation: Test and validate the API and machine learning models to ensure they are functioning correctly. Documentation: Write documentation for the API and machine learning models. Deliverables: The following deliverables will be provided at the end of the project: A deployed and functional API that allows users to interact with the machine learning models. Pre-trained machine learning models integrated with the API. Documentation on how to use the API and machine learning models. Tips for future development and improvement. Timeline: Based on the complexity of the project.
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