Make Your Data Ready for Machine Learning
Before you can get started with Machine Learning, you need to look at your data. Because not just any data will work. For machine learning you need clean, purpose-built data.
Unfortunately, most of the time you’re dealing with legacy data in legacy systems, neither of which were architected with ML in mind. You will almost certainly spend weeks or months to transform the schema of the data so the machine learning model can use it, potentially breaking the legacy systems that rely on it.
Neither is tenable when your boss is asking you to add “AI” to your product, preferably yesterday, and your operational data stores need to stay intact, lest you incur the wrath of your sales team.
Fortunately, there are several strategies to get your existing data ML-ready. In this webinar, we will highlight these best practices as applied to real-world use cases, and demonstrate how Skafos, our ML operationalization and delivery platform, can help you quickly achieve data that is built for ML.