Steps of The Machine Learning Pipeline


There are a number of steps that go into building a machine learning pipeline, and each step can be a laborious and time-consuming process with highly unpredictable costs when attempting it yourself. Cortex takes the guesswork out of the ML pipelines by automating it from start to finish.

1. Data Preprocessing


Raw data is preprocessed at huge scales, from disparate sources, and in various formats.

2. Data Cleaning


Data must be cleaned by removing outliers, detecting missing values, duplicates, class imbalances and more.

3. Feature Engineering


Meaningful features are derived from your data that can serve as predictive inputs to your models.

4. Model Selection


Many algorithms with various input combinations are tested against one another to determine the best performing model.

5. Prediction Generation


Unlabeled data is passed through the winning model, yielding the most accurate predictions possible.

6. Deployment


Predictions must be integrated directly into business efforts whether through a file or a scalable API.

Say Goodbye to Data Wrangling


Some businesses might already have a team to tackle data wrangling. However, it typically accounts for 90-95% of the effort in building a machine learning pipeline, and progress is often slowed by the unforeseen challenges it presents. With these steps automated for you in Cortex, your team is free to focus on strategy and apply the results.

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Repeatable, Re-trainable ML Pipelines - in Seconds


Retraining models with new data takes a lot of additional effort when the data needs to be pulled together, cleaned and engineered all over again. With Cortex, retraining models is just one click away. As your data streams into the platform, pipelines can be scheduled to run on a recurring basis so you always have the highest quality results at the tips of your fingers.

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