Skymind for Engineering Teams | Skymind

Skymind for Engineering Teams

Challenges

Feeling pressure to ship Machine Learning solutions into applications faster, engineering teams are facing challenges in working with or without a data science team.

Getting the Right Data
It is difficult and time-consuming to find, access, clean, transform, and label data sets in sufficient quantities to create accurate ML models.
Pressure to Ship Faster
Engineering teams have very limited time to build and ship ML functionality. There is not enough time to learn ML theory and perform low-level ML programming.
Restricted Environments
Products must comply with security and regulatory requirements, meet scalability demands, and perform on hybrid environments using JVM.
Interoperability
There's a disconnect between what data scientists make and what engineers need, creating the burden of integrating pipelines and models into production on JVM environments.

Solution

Skymind helps engineering leaders and teams solve those challenges with a platform made for the stack and environment they already use.

Ship ML Features Faster

Add text mining, computer vision, analytics, prediction, and other ML capabilities into Java applications, with the help of a built-in, enterprise-grade Deep Learning framework.

Reduce Operational Overhead

Own the ML lifecycle with a platform that guides you through ML development and deployment, and takes care of infrastructure so you can focus on the product.

Automate Business Processes

Take ML out of the research lab and into real business applications. Perform model training and model functions on data from ERP systems, data warehouses, Kafka, Spark, Flink, and other sources.

Make Data Science Work Useful

Seamlessly convert Keras, TensorFlow, and other Python-based models that were developed by data science teams into production-ready models for Java applications. Track and collaborate on models in the process.

Ask an Expert

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