How to create a scalable data pipeline for training ML models?
There are three fundamental steps to creating a scalable pipeline:
Data discovery: Before data is fed into the system, it must be discovered and classified based on characteristics such as value, risk and structure. Since a wide variety of information is needed to train the ML algorithm, AI data platforms are used to extract information from heterogeneous sources, such as databases, cloud systems, and user inputs.
Data ingestion: Automatic data ingestion is used to develop scalable data pipelines using webhooks and API calls. The two basic approaches to data ingestion are:
- Batch Ingestion: In batch ingestion, batches or groups of information are retrieved in response to some form of trigger, such as after a certain amount of time or after reaching a particular file size or number.
- Streaming ingestion: With streaming ingestion, data is fed into the pipeline in real time as it is generated, discovered, and classified.
Data cleaning and transformation: Since most of the data collected is unstructured, it is important to clean, separate and identify it. The main goal of data cleansing before transformation is to remove duplication, fake data, and corrupted data so that only the most useful data is retained.
Pretreatment:
During this stage, unstructured data is categorized, formatted, classified and stored for processing.
Model processing and management:
During this stage, the model is trained, tested, and processed using the ingested data. The model is refined based on the domain and requirements. In model management, the code is stored in a version that facilitates faster development of the machine learning model.
Model deployment:
During the model deployment step, the artificial intelligence The solution is deployed for use by businesses or end users.
Data Pipelines – Benefits
The data pipeline enables smarter, more scalable, and more accurate ML models to be developed and deployed in a significantly shorter time frame. Some benefits of ML data pipeline include
Optimized planning: Planning is important to ensure your machine learning models run smoothly. As ML evolves, you will find that some elements of the ML pipeline are used multiple times by the team. To reduce calculation time and eliminate cold starts, you can plan deployment for frequently used algorithm calls.
Independence of technology, framework and language: If you are using a traditional monolithic software architecture, you will need to be consistent with the coding language and ensure you load all required dependencies simultaneously. However, with an ML data pipeline using API endpoints, the disparate parts of the code are written in several different languages and use their specific frameworks.
The main benefit of using an ML pipeline is the ability to scale the initiative by allowing elements of the model to be reused multiple times across the technology stack, regardless of framework or language.
Data Pipeline Challenges
Scaling AI models from testing and development to deployment is not easy. In testing scenarios, business users or customers can be much more demanding, and such errors can prove costly for the business. Some data pipeline challenges are:
Technical difficulties: As data volumes increase, technical difficulties also increase. These complexities can also cause architectural issues and expose physical limitations.
Cleaning and preparation challenges: In addition to the technical challenges of the data pipeline, there is the challenge of cleaning and data preparation. THE raw data needs to be prepared on a large scale, and if the labeling is not done accurately, it can lead to problems with the AI solution.
Organizational challenges: When a new technology is introduced, the first major problem arises at the organizational and cultural level. Unless a cultural change occurs or people are prepared before implementation, it can spell disaster for the AI Pipeline project.
Data security: When scaling your ML project, estimating data security and governance can pose a major problem. Because initially, a large part of the data would be stored in one place; there could be issues with theft, exploitation, or opening of new vulnerabilities.
Building a data pipeline should be aligned with your business goals, the requirements of the scalable ML model, and the level of quality and consistency you need.
Setting up a scalable data pipeline to machine learning models can be difficult, long and complex. Shaip makes the whole process easier and error-free. With our extensive experience in data collection, partnering with us will help you deliver faster, high performanceintegrated, and end-to-end machine learning solutions at a fraction of the cost.