Building Data Pipelines that Fuel Long-Term Success

    By this point, it’s well known that data is as good as gold to modern businesses. With the growing adoption of machine learning for business solutions, organizations are leveraging large datasets to develop functionality and services with predictive capabilities that provide their customers with a high-quality user experience.

    With that said, leveraging data for machine learning purposes is not without its challenges—not least of which is building effective datasets to support the analysis, development, and training of these models. Below, I will discuss the importance of building data pipelines that fuel success in this realm.

    Mining the Value of Data with Machine Learning

    Machine learning solutions are dependent upon two things: code and data. From the perspective of code, machine learning algorithms are utilized to develop solutions that can ingest and evaluate data, thereby detecting patterns that enable accurate predictive capabilities.

    With that said, the quality of a machine learning model (i.e. the accuracy of its predictive capabilities) is dependent upon the quality of its training—and this training is accomplished by utilizing structured training datasets. The model establishes and refines the rules that it uses as the basis of its predictive analysis through its evaluation of this training data. Furthermore, test data is used to ensure that a solution is performing at a high level and in a manner consistent with business requirements. In other words, the data used throughout the design and development process is crucial for ensuring the model’s effectiveness when evaluating input data in a production setting.

    Machine learning solutions are prevalent in many of the applications and services that are frequently and heavily used on a daily basis. For example, banking institutions utilize machine learning to detect instances of fraud, and online video streaming platforms use machine learning to make personalized and targeted content recommendations. Machine learning solutions enable organizations to provide increased value to their customers by significantly improving the user experience.

    What Is a Data Pipeline?

    Since data has such a significant impact on the quality of a machine learning model's predictive capabilities, the processes of collecting and preparing this data are extremely important. To ensure effective collection, preparation, transformation, and availability of this data, data science teams build data pipelines.

    At a high level, the process of building a data pipeline involves several steps, the first of which is collection. During collection, raw data is gathered from one or more sources. Next, the data is prepared, cleaned, and transformed. During the preparation process, which typically involves a series of operations, the newly collected raw data is taken in, and well-structured data that is more suitable for use by data scientists is churned out.

    Finally, the newly cleaned, well-structured data is made available to the data science team. This cleaned data must adhere to the post-transformation data model that will support the machine learning development process. In other words, the data is stored in a state that is readily available and usable by data science personnel. This facilitates analysis as well as training and testing the machine learning model.

    Building Effective Datasets for Machine Learning

    It’s easy to see how critical data pipelines are in the process of building machine learning solutions. Without them, designing, training, and testing a machine learning model would be impossible. It is important to ensure that you have high-quality datasets early in the machine learning development process.

    The Importance of Clearly Defined and Achievable Objectives

    All too often, data science teams begin collecting data without a clear idea of how it will be used to power a machine learning model—and there’s a big difference between having data and being able to use it. To avoid this issue, data science teams need to set clear and achievable objectives for their data prior to beginning the collection process.

    This means that data science teams must have a clear understanding of the problems that need to be solved, and they must determine how they can fulfill their particular business’ requirements from the outset. Preliminary data analysis enables data engineers to build collection processes that collect the correct data as well as preparation processes that effectively transform the data into a format that will be useful for training the machine learning model.

    If the collection process is implemented before teams fully understand what they will need to train the model, there’s a much higher likelihood that the data being collected won’t be exactly what the model requires. The same goes for data cleaning. One of the central functions of a data pipeline is to transform raw data into usable output. If the preparation process is flawed, the output datasets will be flawed as well, even if the proper data is collected—and this will lead to an equally flawed machine learning model.

    Machine Learning Is Iterative: Make the Early Iterations Count

    As is the case with any software development process, developing a machine learning solution is iterative in nature. By building data pipelines that produce high-quality datasets, data science teams can facilitate the development of machine learning models with high levels of accuracy.

    It’s especially important to accomplish this early on, so that the data scientists can begin to build their models based on a solid data foundation. Making the appropriate investments in data analysis and preparation can lead to higher quality datasets earlier in the machine learning lifecycle. This will help ensure that this iterative process is smoother and more efficient, allowing data scientists to refine and tweak their models rather than having to overhaul their products due to flawed training and testing datasets.

    Automating Data Pipelines to Improve Efficiency

    Supporting machine learning means making data readily available to data scientists. Traditionally, this has been more difficult than an organization would like. As is the case with many development processes today, proper automation can help remove pain points—and the implementation of a data pipeline is no exception. By introducing end-to-end automation within data pipelines, organizations can speed up workflows and eliminate human error in processing, thereby providing data science personnel with the data they need in a more efficient manner.

    Wrapping Up

    A machine learning model can’t magically train itself, and its level of effectiveness will only be as good as the source data that’s used in the training process. If the data used to train the machine learning model is flawed, then the predictive capabilities of the model will be equally flawed.

    The goal of building data pipelines for machine learning is to produce datasets that effectively support the processes of machine learning design and development. This requires a thorough understanding of your business’s requirements as well as effective preliminary data analysis from the outset of the project (prior to data collection). Creating clear and achievable objectives for data engineers ensures that they will build data pipelines that collect useful raw data and transform it into a format that can be utilized to effectively train and test a machine learning solution.

    Like any development process, building a machine learning solution is iterative—and these iterations are made more efficient by a well-constructed data pipeline. When a pipeline is built upon incorrect assumptions and lackluster analysis, the future iterations will require major changes in the core collection and preparation processes. On the other hand, proper planning and a full understanding of the objectives at the outset will result in more accurate initial output datasets. This will ensure that later iterations will center on tweaking the data model and supporting functionality rather than having to do a major overhaul.