Biggest Bottleneck in Machine Learning and AI
Machine Learning and AI are all the buzz. In the last year, IDC reports that 37.5 billion dollars will be spent on machine learning and AI investments, increasing to close to $100 billion by 2023. Yet organizations still struggle to get value out of their machine learning and AI investments. One possible cause? The time consuming, difficult process of preparing data for machine learning and AI. It’s widely known that 80% of any data science project is spent wrangling the data. To compound this fact, machine learning and AI models require high quality data in order to be effective. The challenge organizations face in wrangling data for machine learning and AI has opened up a huge opportunity for organizations to out compete on differentiated data.
We at Trifacta see this commonly in insurance, retail, IOT, financial services and marketing intelligence where organizations are looking to leverage large volumes and varieties of data to gain insights into customer behavior, price optimization, fraud detection, and more. Data scientists are often working with complex data formats, raw text, sensor data, and various other forms of unstructured and semi-structured data. These datasets require tons of upfront work to get them into a structured state. They also require the additional work of blending data together with other sources, and performing feature engineering in order to create effective training data. Data scientists need to be able to structure this data, address anomalies, remove outliers, and engineer features quickly and efficiently so that more time can be spent on building models, deploying models into production, and gaining valuable insights. They then need to automate this work to drive consistent value out of their models in production.
Trifacta’s visual and machine learning driven guidance create a code-free interface focused on ease of use, instant validation, and powerful transformations. Users of all skill levels can create the transformations, data quality checks and data pipelines they need to decrease the time it takes to deploy models and to drive consistent and continuous value from supported machine learning and AI platforms like Amazon Sagemaker, DataRobot, Tensor Flow, and others.
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