Data scientists need tools that give them access to previously siloed data, eliminate time wasted on data searches, increase cooperation, and reduce bottlenecks.
A robust data pipeline is at the heart of modern data solutions. Whether it is training or inference, any enterprise-level AI model must become part of the data analytics pipeline for production deployment. And the integration of the model into the data pipeline must be able to work in multiple deployment models.
Data scientists may start with simple prototyping, but working with enterprise boundaries requires scale. Operationalization is complicated, causing the bottleneck and eventual death of AI deployment in all but the most straightforward cases. That is not a scenario most companies can withstand. Increasingly, AI is viewed as a competitive differentiator that will allow one company to succeed versus another.
So, what do companies do? Businesses that manage to build modern data-driven applications will survive. Here’s how a business can work to build that elusive production-grade, enterprise-ready, end-to-end solution to harness real data.
One problem when developing and deploying AI within a business is that there are different data criteria for different pipeline and deployment stages. Ensuring the right data is used, is in the correct format, and properly secured at each step in a data pipeline or analysis workflow are time-consuming tasks. These tasks divert data scientists away from their main objective of turning data into insights.
Data scientists must understand each stage
The raw data is far from ready for AI model training or use in inference analysis for many businesses. It must be cleaned and transformed in batch or streaming mode, depending on the application. Unless the AI solutions are designed with an understanding of all the stages of the data pipeline, starting from the source of the original data, it will be challenging to deploy in production.
Developers need transparent tools
Corporations need ethical and explainable AI as compliance issues and regulations are requiring transparent solutions. While a black box may have been the solution in the past, now, companies must answer for the insights they give.
It’s no longer acceptable to take results without some form of explanation. Data can skew in several ways, and it’s crucial that companies develop a true understanding of implicit biases and how visualizations can hide answers.
Traceability and good documentation are required. Data science tools must accommodate such transparency, helping companies ensure that their solutions are above board. If something goes wrong, companies can find the source of the issue.
The most significant component? Validation
Even with explainability and stage-to-stage oversight, the most important piece of an efficient pipeline is validation. Data goes through cleaning and transforming at multiple stages and through multiple, often complex steps. Since the AI model is only as good as the data itself, validation should be built in to ensure consistency.
Data science projects start with raw data and must perform a series of transformations to select the relevant subset of data, fix data collection or archival inconsistencies, and finally use these “cleaned up” data to create a model. At each of these transformation steps, there is the possibility of introducing an error that will propagate through the rest of the analysis, invalidating the results. Sometimes these errors are not even the result of programming mistakes but can be caused by new data that violates assumptions used to create the model in the first place. Validation at each stage of the data processing pipeline can catch these sorts of problems before they have a chance to wreck a model and cost stakeholders both time and money.
For example, a commonly undertaken analysis is to use years of U.S. census data with an ML regression model to predict income by using people’s education information. The simplest approach is to run the model on the raw data with no changes. What really needs to be done once the census data is ingested is to clean up the data. For example, data without an income value should be dropped, yearly income numbers should be normalized for inflation, and other consistency checks should be applied. When new data is added in the future, robust consistency checks will flag issues that need to be addressed before the model is updated. Taking such steps would significantly improve the veracity of the predicted results.
Enterprise, open-source tools are possible
Intel and Anaconda have partnered to bring enterprise-ready open-source tools to businesses. These tools are optimized for scale-up and scale-out performance.
The tools are available to address requirements for building end-to-end, security-first AI solutions. They allow businesses to create data pipelines and AI workflows that deliver reliable results. The solutions can incorporate cutting-edge analytics technologies and run on a wide range of enterprise platforms.
All stakeholders and data owners using the tools will be able to access previously siloed data, increasing cooperation and providing a unified programming model. The immediate benefit of using the Anaconda and Intel solutions is reduced bottlenecks and less time wasted with data searches.
Read the other blogs in this series: