Data preparation for machine learning still requires humans
Data is at the core of AI and machine learning projects. Even more so than application code, data is crucial in training, testing, validating and supporting the machine learning algorithms at the heart of AI systems. Part of the reason why AI has surged again in popularity is due to the combination of almost limitless cloud computing, the availability of big data to train machine learning models, and the evolution of deep learning algorithms. The last two of those three reasons are data dependent. In fact, the more data you can feed AI algorithms, the better they perform and the more significant the machine learning results.
However, it’s not enough to have a lot of data. Without good quality data, AI systems fail. The root of many machine learning project failures has little to do with the machine learning algorithms or code, or even any particular technology vendor choice. Problems almost always come back to the quality of data. In order for machine learning models to be properly trained and provide the expected accurate results, the data used needs to be clean, accurate, complete and well-labeled. Data preparation for machine learning is a crucial step.
Because of this, the majority of time that companies spend on AI projects goes toward data collection, cleaning, preparation and labeling. Enterprises are finding that they need to invest more in these data prep steps than on the data science, model training and operationalization parts. This has led to a substantial growth in demand for tools and services to assist with data preparation and labeling.
The many steps of data preparation for AI A recent report from AI research and advisory firm Cognilytica finds that over 80% of the time enterprises spend on AI projects goes toward preparing, cleaning and labeling data. Specifically, the report finds that the many steps involved in data collection, aggregation, filtering, cleaning, deduping, enhancing, selecting and labeling data far outnumber the steps on the data science, model building, and deployment sides. Where data scientists and engineers spend most of their machine learning time A new class of data preparation tools has emerged into the market, built to manage big data sets and optimized to address the problems of machine learning projects. According to the report, the market for AI-focused data preparation tools is currently valued at over $500 million and expected to more than double to $1.2 billion by the end of 2023. The market for data prep tools is projected to grow rapidly Most enterprise data is not ready to be used by machine learning applications and requires significant effort in preparation. Tools offering data preparation for machine learning need to be able to perform a long list of tasks, including standardize formats across different data sources, remove or replace invalid and duplicate data, confirm data is accurate and up to date, help enhance and augment data as needed, reduce data noise, anonymize data, normalize data, allow for proper data sampling especially when working with large volumes of data, and allow for feature engineering and extraction. Cognilytica’s report finds that the AI-relevant data preparation tools provide iterative and interactive ways to allow people to quickly view the impact of data prep activities on big data. Some of the key features of these tools allow you to quickly spot anomalies in the data, identify and remove duplicates, resolve data conflicts, normalize data formats, establish pipelines for extracting and collating data from multiple sources, enhance data with additional features required for models, and anonymize data as might be necessary for certain applications. In the past, enterprises relied on a category of tools known as extract, transform, load (ETL) to move data into and out of big data warehouses to facilitate reporting, analytics, business intelligence and other operations.
