Why data contracts are important especially in live AI implementation

By Dr Mabrouka Abuhmida

In the context of machine learning (ML) pipelines, data contracts refer to the agreements or rules that define how data should be structured, formatted, and passed between different stages of the pipeline. These contracts ensure that data is consistently transformed and passed between stages in a predictable and reliable manner.

There are several benefits to using data contracts in ML pipelines:

1. Data contracts can help to enforce clean and well-structured data by defining specific requirements for data formatting and structure. This can make it easier to understand and work with the data, and can help to prevent errors or inconsistencies that might arise from using poorly structured data.

2. Data contracts can facilitate collaboration and communication between different teams or individuals working on an ML project by clearly defining the expectations and requirements for data input and output at each stage of the pipeline.

3. Data contracts can help to improve the reliability and robustness of ML pipelines by ensuring that data is consistently transformed and passed between stages in a predictable manner. This can help to reduce the risk of errors or failures in the pipeline, and can make it easier to troubleshoot and debug any issues that do arise.

There are several different ways that data contracts can be implemented in ML pipelines, depending on the specific needs and requirements of the project. Some common approaches include using data schemas, data dictionaries, and data pipelines.

In general, data contracts are most useful when the data is expected to be used for a specific, well-defined purpose and is not expected to change significantly over time. In these cases, data contracts can help to ensure that data producers and data consumers are clear on the ownership and use of the data, and can help to protect the privacy of individuals whose data is being collected and used.

A data pipeline is a series of processes that moves data from one place to another, typically involving the extraction of data from various sources, the transformation of that data into a desired format, and the loading of the transformed data into a destination for further processing or analysis.

In the context of machine learning (ML), data pipelines are often used to move and transform data as it flows through different stages of an ML workflow. For example, a data pipeline might be used to:

1. Extract data from various sources, such as databases, APIs, or files

2. Clean and transform the data to remove errors, missing values, or inconsistencies

3. Normalize or standardize the data to make it more usable for downstream processing

4. Split the data into training, validation, and test sets for use in an ML model

5. Load the transformed data into a storage system or database for further processing or analysis

Data pipelines can be implemented using a variety of different tools and technologies, depending on the specific needs and requirements of the project. Some common approaches include using data integration tools, such as Apache NiFi or Talend, or using programming languages, such as Python or Java, to build custom data pipelines.

There are challenges related to data quality while working on data pipelines. These challenges included modifications to services that resulted in the loss of data fields, and the reliance on change data capture (CDC) to obtain critical data directly from databases, which made it difficult to transform the data after it was loaded. These issues made it demanding to maintain the value of investments in code and tooling for data transformation, and may have required significant re-engineering efforts in the event of schema changes.

Circuit breakers can be a useful mechanism for helping to ensure the resiliency of data pipelines. Circuit breakers are essentially switches that can be used to interrupt the flow of data through a pipeline if certain conditions are met. These conditions might include things like errors or anomalies in the data, or other indicators that the data pipeline is not functioning as intended. By interrupting the flow of data when such conditions are detected, circuit breakers can help to prevent bad data from propagating through the pipeline and potentially causing further issues downstream. Circuit breakers can be particularly useful in scenarios where the data pipeline is handling sensitive or critical data, as they can help to minimize the impact of any errors or issues that might arise.