Establishing an AI Data Pipeline

What is a Data Pipeline?

A pipeline also referred to as a data pipeline, is a series of data processing devices connected in computing, where the result of one is the input of the next. Pipeline components are frequently processed in parallel or in a time-sliced way.

The adoption of AI is increasing, as per Forrester Research. AI is being deployed, implemented, or expanded by 63 percent of enterprise IT decision-makers. By making businesses, procedures, and products more sophisticated, AI promises to help businesses accurately foresee changing economic dynamics, improve offer quality, improve the consumer experience and minimize operational risks. Such competitive advantages are a strong incentive to implement AI sooner than expected.

From AI-driven suggestions to autonomous cars, chatbots, predictive modeling and products that adjust to the requirements and tastes of consumers, AI is making its way into a wide range of applications. But, as diverse as AI-enabled apps are, they ultimately have the same underlying goal: to consume data from various sources and extract valuable intelligence or insight from it.

Despite the potential benefits of AI to speed innovations, increase corporate flexibility, improve the consumer experience and a slew of other advantages, some organizations are moving ahead of the pack. Some people are unsure because AI appears too intricate, and moving from here to there, particularly from intake to insights, seems too difficult. That could be because no other company or IT effort guarantees more in terms of results or places more demands on the system it runs on.

Building a Data Pipeline

From the outside, AI that is done well appears to be simple. However, behind every great AI-enabled app is a data pipeline that moves data. This data pipeline is the essential building block of AI— from ingesting throughout many data categorization levels, classification, machine learning, deep learning model training, analytics and retraining through inference to generate growingly effective decisions or deep insight.

Even to experienced onlookers, the AI data flow is neither linear nor constant and production-grade AI can appear cluttered and challenging. As companies progress from experimenting and prototyping to putting AI in production, the first hurdle they face is integrating AI into their preexisting analytics and data pipeline and creating a data pipeline that can use old data sources. Concerns regarding integrating complexity may emerge as one of the significant barriers to AI adoption in their enterprises amid this demand. But this does not have to be the case.

Different phases of the data pipeline have various I/O features and benefits from complimentary storage systems. Ingest or data collection, for example, makes use of the versatility of software-defined memory at the edge and requires high throughput. Aggregating, standardizing, categorizing and enriching data with meaningful metadata demand exceptionally high performance, with little and massive I/O. Learning algorithm necessitates a productivity tier capable of supporting the high-throughput, low-latency activities involved in training deep learning and machine learning models.

Model retraining with inference does not necessitate as much performance, but it does necessitate incredibly low delay. And, for cold and live archive data, a massively scalable capability layer that is performance-oriented and enables huge I/O, streaming and sequential writes are required. Depending on the needs, they could happen on-premises or in private or public clouds.

Scalability, efficiency, installation versatility and compatibility are all challenging needs. However, the efficacy of the complete data pipeline, not only the speed of the system that enables the ML/DL workloads, determines data science productivity. It necessitates a set of systems and software technologies to meet these demands across the data pipeline.

Pillars of an AI Data Pipeline

Scaling AI from a pilot study to a production setting necessitates a complicated system. The architecture must, among many other things, be able to analyze large data volumes at incredible speeds and ingest information from both unstructured and structured sources.

To deploy AI in a real-world production setting, you’ll need an end-to-end pipeline that includes the following pillars:

Ingest: The data from the sources are sent into the training program at this stage. The data is saved in its natural form, and the raw data is not erased even when annotations are applied to enhance the data in the following stage.

Clean and transform: The data is transformed and saved in a framework that allows for extracting features and model analysis, including connecting the sample data and its related label. The data is not stored up in a second version because it can be recomputed if necessary. This is commonly done on servers with ordinary CPUs.

Exploration: Data analysts enumerate quickly during the analysis phase, starting single GPU operations to create the model and test their hypothesis. This is a highly iterative method and an area where the analytics team is underperforming.

Training: To construct a correct model over substantial data sets, training phases choose batches of data input, comprising both fresh and old samples, and feed them into large-scale production GPU servers for computations. The final results are fed into the inferencing program, then executed on new or live data. After these steps are done, you’ll have an AI solution that can be used in automobiles or phones.

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