This training workshop will take place before the main conference. It will be given in a small classroom, to maximize interaction and so you can ask even more questions than in a conference setting.IMPORTANT:- A specific ticket is required to get access to the workshop ("Workshop WORKSHOP_NAME + Conference 24-26/6").
- The venue is different from that of the main conference. The workshop will be held at Dafiti — many thanks to them for providing the space!
- Students should bring their own laptops, for practical work.
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SUMMARYWe will implement and deploy a whole recommender system from scratch, going from simple techniques to more advanced ones. Numpy and Scipy will be used at first, to understand the basic principles; then, a TensorFlow adaptation will be implemented and deployed to production. For this, we will be querying data simulated from Dafiti (which amounts to a few gigabytes), preparing it for the algorithm, training the model serverlessly, and finally building the frontend architecture for the serving step. Google Cloud AI Platform will be used through the whole process (the code and steps that we will present can be used with other public cloud providers, or in private clouds).
TARGET AUDIENCEMachine learning practitioners interested in learning more about recommender systems and how to fully deploy them into production, going from a real source of data input to recommendations offered to the customers.
LEARNING OBJECTIVES- Basic and advanced Collaborative Filtering algorithms
- Data processing
- Deployment of a serverless system following best practices
- Implement a serverless frontend infrastructure to serve recommendations
PROGRAMRecommendation algorithms:
- Exploring concepts of collaborative filtering with implicit feedback in Python
- Going from naive implementations to more sophisticated ones using Numpy / Scipy
- Adapting the code to TensorFlow (so we can later deploy in a serverless environment)
- Unit testing
Data preparation:
- Querying data simulated from Dafiti (~10 GB)
- Analyzing the data, cleaning it, and preparing it for usage in the recommendation algorithm
Deployment:
- Preparing the TensorFlow model previously implemented for training on Google Cloud AI Platform
- Executing a training / test strategy for validating performance
Frontend serving:
- After our model has been deployed, we then proceed on building a simple Python web service that will handle input requests for recommendations, process the whole thing and send back the processed response
- A final examination will be made to analyze recommendations performance
GET YOUR WORKSHOP TICKET NOW!
