Ready to learn how to use Python to build free serverless service? This free course has got you covered - Serverless ML Course
You should not need to be an expert in Kubernetes or cloud computing to build an end-to-end service that makes intelligent decisions with the help of a ML model. Serverless ML makes it easy to build a system that uses ML models to make predictions. You do not need to install, upgrade, or operate any systems. You only need to be able to write Python programs that can be scheduled to run as pipelines. The features and models your pipelines produce are managed by a serverless feature store / model registry. We will also show you how to build a UI for your prediction service by writing Python and some HTML.
- Learn to develop and operate AI-enabled (prediction) services on serverless infrastructure
- Develop and run serverless feature pipelines
- Deploy features and models to serverless infrastructure
- Train models and and run batch/inference pipelines
- Develop a serverless UI for your prediction service
- Learn MLOps fundamentals: versioning, testing, data validation, and operations
- Develop and run a real-time serverless machine learning system
- Pandas and ML Pipelines in Python. Write your first serverless App.
- The Feature Store for Machine Learning. Feature engineering for a credit-card fraud serverless App.
- Training Pipelines and Inference Pipelines
- Bring a Prediction Service to Life with a User Interface (Gradio, Github Pages, Streamlit)
- Automated Testing and Versioning of features and models
- Real-time serverless machine learning systems. Project presentation.
You have taken a course in machine learning (ML) and you can program in Python. You want to take the next step beyond training models on static datasets in notebooks. You want to be able to build a prediction service around your model. Maybe you work at an Enterprise and want to demonstrate your models’ value to stakeholders in the stakeholder's own language. Maybe you want to include ML in an existing application or system.
You don’t need any operations experience beyond using GitHub and writing Python code. You will learn the essentials of MLOps: versioning artifacts, testing artifacts, validating artifacts, and monitoring and upgrading running systems. You will work with raw and live data - you will need to engineer features in pipelines. You will learn how to select, extract, compute, and transform features.
No. You will become a serveless machine learning engineer without having to pay to run your serverless pipelines or to manage your features/models/user-interface. We will use Github Actions and Hopsworks that both have generous time-unlimited free tiers.
Register now at Serveless ML Course