The Ultimate Guide on How to Streamline AI Data Pipelines

With data volume and variety growing exponentially, organizations must invest in robust pipelines to build efficient artificial intelligence (AI) models. According to Forbes’ latest data estimates, users generated around 104 terabytes of data in 2022, making it extremely challenging to manage data assets.

Streamlining AI data pipelines will ensure high-quality training data for developing complex AI frameworks. However, implementing an AI pipeline requires organizations to understand their AI workflows and use relevant tools to help achieve the company’s broader goals.

This post will discuss AI data pipelines, their components, implementation challenges, strategies to streamline them, and how you can use Encord to enhance data pipelines for computer vision.

What is an AI Data Pipeline?

AI data pipelines are extract-transform-load (ETL) frameworks that automate the data flow between multiple stages. The stages include data collection, processing, transformation, and storage to ensure datasets are readily available for various tasks.

Data Lifecycle

For instance, organizations can develop pipelines for business intelligence applications to derive valuable insights regarding business operations, profitability, and future outlook. They can also use the pipelines to build AI and machine learning models (ML models) for use cases such as fraud detection, predictive maintenance, and personalized marketing.

The pipelines can handle structured data, such as relational databases; semi-structured data, like XML and JSON; and unstructured data, such as text, images, and videos. Robust data pipelines help organizations maintain high data quality and reduce the time required to prepare data for ML workflows.

Components of AI Data Pipelines

AI data pipelines comprise several components, ensuring data moves seamlessly across the data lifecycle stages. Let’s examine these components more closely.

Data Ingestion

Data ingestion is the first step in the AI pipeline. It gathers raw data from internal sources, such as databases, customer interactions, Internet-of-Things (IoT) devices, and external data sources, such as web scraping platforms, APIs, and third-party data providers.

Users can build pipelines to ingest data in batches or in real time. With batch-based ingestion, the pipeline collects data at regular intervals, whereas real-time ingestion continuously loads data when it arrives at the source.

Batch ingestion is helpful for historical data analysis. In contrast, real-time ingestion is essential for applications that require immediate insights, such as anomaly detection in financial transactions or predictive maintenance in manufacturing.

Data Cleaning

Data cleaning is the next phase that ensures the ingested data is accurate, consistent, and reliable. The tasks include removing duplicate entries, handling missing values, identifying outliers, and standardizing data formats.

The process performs several validation checks to assess whether the data aligns with pre-defined standards. For instance, the pipeline can check whether data points fall within a desired range or have the correct data type.

Undergoing the cleaning process allows data scientists to remove biases from datasets and maintain data integrity for better decision-making.

Data Preprocessing

Data preprocessing helps further transform data into a format compatible with model requirements. This includes a series of operations, such as normalization, aggregation, and encoding, which help prepare the data for model training.

The step reduces noise and makes data from multiple sources comparable. For instance, data from different sources can have values that vary in scale. Preprocessing can apply normalization techniques to bring the values between a scale of 0 and 1

Embeddings

The methods for unstructured data can include annotating images, text, and video data, resizing photos, removing stop words from documents, and transforming such data into embeddings using AI algorithms.

Feature Engineering

After data preprocessing comes the feature engineering component, which extracts meaningful features or variables for input to AI models. This includes feature selection, which identifies the most relevant variables, and feature creation, where experts can derive new features from existing data to better represent patterns within the dataset.

For instance, data scientists building a recommendation app for an e-commerce platform may consider features such as a customer’s purchase history, reviews, and search queries. They can also create features such as an engagement score by combining the data on the customer’s interactions with the support staff and other buyers.

Effective feature engineering can substantially improve model performance, helping the model capture essential relationships between disparate data points. This step often requires domain expertise to identify features relevant to and representative of the underlying problem.

Once identified, data engineers can incorporate the relevant code in the pipeline to orchestrate the feature engineering process. This helps make AI systems more scalable, as data scientists can simply trigger the pipeline to get the relevant features.

Data Storage

The data storage component holds the preprocessed data and associated features in a centralized repository for smooth access. Experts often store structured data in SQL-based relational databases or data warehouses such as Google BigQuery.

However, cloud-based data lakes, such as Databricks, AWS S3, and Azure Data Lake, are more popular for storing unstructured data. Organizations can also build in-house storage solutions depending on the nature of their data and project requirements.

Data Utilization in ML/AI Model Building

The data utilization component in the pipeline fetches relevant data from the storage repository, allowing data scientists to train and validate models. The component can include functions that split the data into a training, validation, and test set.

It can also include data analysis modules that compute the data distributions, descriptive statistics, and correlations between different features. In addition, it may contain visualization methods to present the preliminary analysis in the form of graphs, tables, and heat maps.

Data scientists can then judge the appropriate model, training technique, and validation methods for optimal results. Once satisfied, they can deploy the model in production, where it uses real-world data to make predictions.

Data Monitoring

Since the real world changes over time, pipelines can have a monitoring component that continuously analyzes data patterns in production.

For instance, the pipeline can detect data drift, which occurs when data distributions change beyond certain thresholds. It can also detect drops in model performance, data errors, and latency issues.

Data monitoring helps data scientists track performance issues and fix problems quickly to prevent failures during production.

Challenges in Building AI Data Pipelines

The AI data pipeline has so many components that implementing it can be challenging, especially for organizations new to AI.

Below are a few challenges and potential strategies that can help you understand what bottlenecks you may face and what you can do to prevent them.

• Scalability: With increasing data volumes and variety, building scalable pipelines that can handle multiple data types is tricky. As data expands, pipelines may suffer from increased latency and server downtime. One way to mitigate the problem is to use parallel processing to distribute the workload. You can also implement cloud-based infrastructure to scale pipelines cost-effectively.
• Data Quality: Maintaining data quality in transit as data moves across different stages in the pipeline is challenging. For instance, pipelines may apply identical scaling methods to data from various sources, leading to illogical and inconsistent values. Implementing automated validation checks at each pipeline stage or using image annotation tools to improve quality of visual data can help address such issues. The validations can occur at pre-defined checkpoints, generating alerts if the pipeline finds a problem.
• Integration: Integrating data from diverse sources is complex due to varying types, formats, and structures. Straightforward techniques such as merging, appending, or collapsing can give an inaccurate picture of the data’s contents. Data engineers can use connectors and APIs to combine data from different sources. They can also use tools like AWS and Informatica for seamless integration.
• Security: Data pipelines may process sensitive data, such as a customer’s personally identifiable information (PII). Ensuring no leakage occurs at any stage of the process is difficult due to the high complexity of data and lack of appropriate anonymization methods. However, robust access controls and encryption techniques can help prevent security-related incidents.

Strategies for Streamlining AI Data Pipelines

With organizations rapidly moving toward AI-based technologies, the need for streamlining data pipelines is rising. This is because companies want to extract maximum value from their data assets to optimize business functions and customer experience.

However, the exact steps to streamlining data pipelines can vary from case to case. Here are some general guidelines that you can follow to jumpstart your data initiatives.

Identify Goals, Objectives, and Purpose of Pipelines

Whatever initiative you plan to undertake, your starting point should include defining its purpose and the objectives you wish to achieve. 

Clarifying your goals will help you design the pipelines more efficiently and allow you to set realistic performance expectations.

Choose Reliable Data Sources

Choosing suitable data sources can solve many data integration issues. Start by identifying what sources are relevant to your use case. 

You can then determine the type of data these sources generate and whether they can reliably deliver data without disrupting ongoing operations.

Implement Data Governance

An overarching data governance framework can eliminate many data management challenges. Such a framework defines data quality standards, security protocols, data-sharing procedures, and metadata practices. 

Codifying these elements in a pipeline can significantly reduce data inconsistencies and allow for data usability across different domains.

Implement a Modular Architecture

Modularizing each component is critical to building scalable pipelines. Building separate and independent modules can help organizations allocate specific teams to manage each component. 

This method will ensure data flows smoothly between stages, with each team taking care of upgrades and maintenance without disrupting other teams’ operations.

Automate

The entire process of streamlining AI data pipelines rests upon the idea of automation. Automating repetitive and tedious tasks such as data ingestion, cleaning, and transformation can significantly reduce model failures due to inaccurate data. 

Also, automating integration tasks so that the pipelines process new data as soon as it becomes available paves the way for a more scalable infrastructure.

Use Scalable Data Storage Solutions

With almost 50% of organizations becoming cloud-native, moving to cloud-based storage solutions is inevitable. 

Additionally, with advancements in security compliance by the major cloud players such as AWS, Azure, and Google Cloud, migrating to cloud-based data lakes is a no-brainer for companies that want to build scalable AI data pipelines.

Implement Monitoring Workflows

In addition to monitoring production data using pipelines, you must also monitor the pipelines themselves to prevent sudden failures. 

You can set up logging for each component in the pipeline and generate real-time reports showing how the pipelines perform in each run. 

An effective way is to use large language models (LLMs) to read logs and have them generate relevant summaries to highlight errors and recommend fixes.

Opt for Real-time Data Processing

Wherever possible, prioritize real-time data processing over batch processing. This is because modern data is no longer static. Applications operate in highly dynamic environments, calling for instant insights and immediate action. Real-time processing can address all these needs by ingesting and analyzing data on the fly.

Establish Recovery Techniques

Even the most potent pipelines will likely fail, leading to severe data loss. To continuously maintain high service availability, it is always pragmatic to implement clear backup procedures and failover mechanisms.

Encord for Augmenting Computer Vision Data Pipelines

Encord is a data development platform for annotating, curating, and monitoring large-scale datasets for computer vision (CV) tasks. 

• Encord Annotate: Leverage SOTA model-assisted customizable labeling workflows and flexibly setup complex ontologies to efficiently and accurately label computer vision data for any use case.
• Encord Active: Supports active learning pipelines for evaluating data quality and model performance.
• Index: Manage and curate  computer vision data at scale to prepare high quality datasets for training and fine-tuning AI models.

Encord

Key Features

• Scalability: Encord can help you scale AI data pipelines by ingesting petabytes of  data. You can create multiple datasets to manage larger projects and upload up to 200,000 frames per video at a time.
• Functionality: The platform consists of multiple features to filter and slice datasets in a consolidated visual explorer and export for labeling in one click. It also supports deep search, filtering, and metadata analysis. You can also build nested relationship structures in your data schema to improve the quality of your model output.
• Ease-of-Use: Encord offers an easy-to-use, no-code UI with self-explanatory menu options and powerful search functionality for quick data discovery. Users can query large scale datasets in everyday language to search for images and use relevant filters for efficient data retrieval.
• Data Security: The platform is compliant with major regulatory frameworks, such as the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2 Type 1), AICPA SOC, and Health Insurance Portability and Accountability Act (HIPAA) standards. It also uses advanced encryption protocols to protect data privacy.
• Integrations: You can connect Encord with your native cloud storage platforms and programmatically control workflows using its Python SDK.

G2 Review

Encord has a rating of 4.8/5 based on 60 reviews. Users commend the tool’s simplicity, intuitive interface, and several annotation options. However, they feel a few additional customization options for tool settings, can improve user experience.

Streamlining AI Data Pipelines: Key Takeaways

As organizations focus more on using unstructured data to better understand their business and customers, modern AI data pipelines must align with evolving data needs to handle integration complexity and scalability challenges.

The list below summarizes the key points for streamlining AI data pipelines.

1. Data Pipeline Components: AI data pipelines comprise ingestion, cleaning, preprocessing, feature engineering, storage, utilization, and monitoring components.
2. Techniques to Streamline Data Pipelines: Organizations must identify the pipeline’s goals, select reliable data sources, establish data governance, implement a modular architecture, automate wherever possible, use scalable storage solutions, create monitoring workflows, and define recovery techniques.
3. Augmenting Pipelines with Encord: You can integrate Encord in your pipeline to streamline the ingestion, annotation, and curation process for image and video data.