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Introduction
Batch scoring is a crucial process in machine learning applications, particularly in industries such as banking, telecom, and retail. It involves running trained models on large datasets to generate insights and make predictions. In this article, we will explore how to design and implement batch scoring pipelines using DVC (Data Version Control) and Airflow.
What is Batch Scoring?
Batch scoring is the process of applying a trained model to a new dataset to generate predictions. It is commonly used in industries where data is collected over a period of time and then processed in batches. For example, a CRM department in retail banking may use batch scoring to identify customers who are most likely to buy a new credit product next month.
Goals for this Post
In this post, we will cover the design and implementation of batch scoring pipelines using DVC and Airflow. We will also provide a step-by-step guide on how to set up a CI/CD pipeline for batch scoring applications.
Design ML Pipelines with DVC
Machine learning pipelines for batch scoring applications typically involve the following steps:
1. Data preparation: The first step is to clean, pre-process, and transform the data into a format that can be used for training machine learning models.
2. Feature engineering: In this step, relevant features are extracted or created from the data and transformed into a format that can be used for training machine learning models.
3. Model selection and training: Next, multiple machine learning models are selected and trained using the prepared data.
4. Model evaluation: The trained models are then evaluated to determine their accuracy and performance on new data.
Setup CI Job deploy
The deploy CI job is responsible for delivering the scoring DAG to the Airflow cluster. There are various strategies for delivering the DAG to the cluster, but in this example, we will use a simple approach that involves copying the DAG files from the repo to the Airflow home directory.
Results
The proposed approach demonstrates how DVC, CML, and Iterative Studio can help in batch scoring applications at the experimentation and production phases. Solutions discussed in this post may benefit similar use cases in a few ways:
* Help with system design and tools integration.
* Automate ML experiments.
* Increasing speed of Proof-Of-Concept (POC) and Operationalization (MLOps) stages.
* Saving time and money for similar projects.
Conclusion
In conclusion, batch scoring is a crucial process in machine learning applications, and DVC and Airflow can help in designing and implementing batch scoring pipelines. By following the steps outlined in this post, you can set up a CI/CD pipeline for batch scoring applications and automate the process of scoring large datasets.
Frequently Asked Questions
Q: What is batch scoring?
A: Batch scoring is the process of applying a trained model to a new dataset to generate predictions.
Q: Why is batch scoring important?
A: Batch scoring is important because it allows companies to make predictions and generate insights from large datasets.
Q: What is DVC?
A: DVC is a tool that helps in managing and versioning data and configurations used for machine learning experiments.
Q: What is Airflow?
A: Airflow is a tool that helps in scheduling and managing workflows and pipelines for machine learning applications.
Q: How can I set up a CI/CD pipeline for batch scoring applications?
A: You can set up a CI/CD pipeline for batch scoring applications by following the steps outlined in this post, including designing and implementing batch scoring pipelines using DVC and Airflow.
