Steps of the machine learning pipeline

There are a number of steps that go into building a machine learning pipeline, and every step can be a time-involved process resulting in unpredictable costs when doing it yourself.

1. Data Preprocessing

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

4. Model Selection

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

2. Data Cleaning

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

5. Prediction Generation

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

3. Feature Engineering

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

6. Deployment

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

No more data wrangling

Many businesses might already have the teams 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 can be free to formulate strategies and apply the results.

Fresh retrains are no problem

Retraining models with new data can take a lot of additional effort if the data needs to be pulled together, cleaned, and engineered all over again.  With Cortex, retraining models becomes a snap. As your data is streaming into the platform, pipelines can be scheduled to run on a recurring basis so your results are always of the highest quality.