Building a Complete AI Based Search Engine with Elasticsearch, Kubeflow and Katib

By TOPBOTS
Building a Complete AI Based Search Engine with Elasticsearch, Kubeflow and Katib Building search systems is hard. For each, we compute their expected judgment and build the matrix of features X. In practice, what will happen is that we’ll first prepare all this data and feed it to the Learn-To-Rank plugin of Elasticsearch which will result in a trained ranking model. It then tests the new model and observe results which are used for future steps. The file create_k8.sh that runs in step 4 is responsible for creating the Kubernetes cluster on top of Google Kubernetes Engine (GKE) as well as deploying Elasticsearch, Kubeflow and Katib.

 

Topics

  1. Backend (0.33)
  2. NLP (0.26)
  3. UX (0.16)

Similar Articles

K-fold Cross Validation with PyTorch

Explanations and code examples showing you how to use K-fold Cross Validation for Machine Learning model evaluation/testing with PyTorch. ...

Automated Machine Learning in Python

An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning m ...