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How can you apply data engineering principles and techniques to support artificial intelligence analyses?

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Data engineering is the foundation of artificial intelligence (AI) analyses, as it involves collecting, processing, transforming, and storing data in a way that enables AI models to learn and perform tasks. Data engineering principles and techniques can help you design, build, and maintain scalable and reliable data pipelines, platforms, and architectures that support AI analyses. In this article, you will learn how to apply data engineering principles and techniques to support artificial intelligence analyses in six steps.

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Tony Pericle

1 Understand the AI problem

The first step is to understand the AI problem that you want to solve, and the data requirements that it entails. You need to define the goals, scope, and expected outcomes of the AI analysis, and identify the data sources, formats, quality, volume, and velocity that are relevant and available. You also need to understand the AI techniques and tools that you will use, such as machine learning, deep learning, natural language processing, computer vision, or speech recognition, and their data dependencies and specifications.

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Tony Pericle
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Our ability to dream of possibilities is directly correlated to our understanding of the business domain AND AI’s capabilities.

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2 Design the data pipeline

The second step is to design the data pipeline that will ingest, process, transform, and store the data for the AI analysis. You need to choose the appropriate data engineering tools and technologies that suit your data sources, formats, quality, volume, and velocity, as well as your AI techniques and tools. You also need to consider the data governance, security, privacy, and compliance aspects of your data pipeline, and ensure that you follow the best practices and standards for data engineering.

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Tony Pericle
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Don’t obsess about the data pipeline when you are in the ideation phase. Understand what data you have access to, sketch out your design, construct a round-trip of the proposed solution, and then design and build your data pipeline.

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3 Implement the data pipeline

The third step is to implement the data pipeline that you designed, using the data engineering tools and technologies that you selected. You need to write, test, and deploy code that will perform the data ingestion, processing, transformation, and storage tasks for your data pipeline. You also need to monitor, troubleshoot, and optimize the performance, reliability, and efficiency of your data pipeline, and handle any errors, failures, or anomalies that may occur.

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4 Prepare the data for AI analysis

The fourth step is to prepare the data for AI analysis, using the data pipeline that you implemented. You need to perform data cleaning, validation, integration, and enrichment tasks to ensure that your data is accurate, consistent, complete, and relevant for your AI problem. You also need to perform data analysis, exploration, and visualization tasks to understand your data, discover patterns, insights, and anomalies, and generate hypotheses and questions for your AI analysis.

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5 Feed the data to the AI model

The fifth step is to feed the data to the AI model that you want to use for your AI analysis, using the data pipeline that you implemented. You need to format, structure, and label your data according to the requirements and specifications of your AI model, and split your data into training, validation, and testing sets. You also need to train, tune, and evaluate your AI model using your data, and measure its performance, accuracy, and reliability.

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6 Deliver the AI results

The sixth step is to deliver the AI results that you obtained from your AI analysis, using the data pipeline that you implemented. You need to store, organize, and document your AI results in a way that is accessible, understandable, and actionable for your stakeholders. You also need to present, communicate, and explain your AI results using data visualization, storytelling, and reporting techniques, and provide recommendations, solutions, or actions based on your AI results.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Tony Pericle
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The AI model is never “done.” To continually improve, you need to have the ability to run simulations. A simulation is a comparison of one output using a version of the AI model, compared to another output using a modified version of the AI model. The purpose of this comparison is to isolate and analyze variances. Variances can be adjusted in two ways. The first is with better input, which is difficult to do, but we still should strive to do, within reason. The second is modifying the AI model. Once this AI model is adjusted, which can be done in a variety of ways, re-run the simulation, comparing the output to the baseline output. Did the adjustments reduce the variances? If yes, this is your model (baseline) moving forward.

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Mauricio M.

Master of Mathematics & Industrial Computing. Big Data, Business Intelligence & Analytics.
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Besides data collection and preparation for the AI model and serving results, a robust ai data pipeline involves continuous learning, model evaluation, and a feedback mechanism: Continuous Learning: The data pipeline constantly monitors for new data, automatically ingests, processes, and updates the AI model. This loop ensures the model stays current. Model Evaluation: Regularly assessing the AI model's performance is vital. If it underperforms, it triggers retraining with new data. Feedback Mechanism: To adapt to novel scenarios, the model uses feedback from incorrect predictions. The data pipeline captures and integrates this feedback into the training process.

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How can you apply data engineering principles and techniques to support artificial intelligence analyses?

1

2

3

4

5

6

7

1 Understand the AI problem

2 Design the data pipeline

3 Implement the data pipeline

4 Prepare the data for AI analysis

5 Feed the data to the AI model

6 Deliver the AI results

7 Here’s what else to consider

Data Engineering

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How can you apply data engineering principles and techniques to support artificial intelligence analyses?

1

2

3

4

5

6

7

1 Understand the AI problem

2 Design the data pipeline

3 Implement the data pipeline

4 Prepare the data for AI analysis

5 Feed the data to the AI model

6 Deliver the AI results

7 Here’s what else to consider

Data Engineering

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