The sheer volume of data generated by consumer internet companies is vast but lacks the label that a robust model needs to extract insights. The time associated with data prep and model training also add up and slow down the data science process. As a result, data scientists face challenges in their workflow to train and deploy their models for predictive services like fraud detection and content syndication on social media.

Join experts from Appen and NVIDIA to learn about two new solutions that accelerate workflows and solve bottlenecks.

1. Learn how Appen’s data labeling solutions improve model accuracy by employing quality control measures to correct a false signal as the model is retrained and deployed.
2. Learn how RAPIDS accelerates your existing data science toolchain in GPUs. RAPIDS accelerates Python workloads in a comfortable Jupyter notebook environment. This means being able to explore your data and train your model in a familiar environment at a significantly faster speed.

• How to build an end-to-end data science pipeline and model.
• How RAPIDS empowers teams to do more with a Python stack.
• How Appen’s solutions yield the most accurate data labels.
• How to do more with data, in the shortest amount of time, at the lowest cost.

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