2 – 4 weeks
MLOps & AIOps to monitor post-production results, looking for model drift, bias, and performance.
10 – 12 weeks
Model deployment and enterprise deployment support, requires different skillsets than algorithm development.
1 – 2 weeks
Cross-validation of predictive/prescriptive algorithm to make sure the insights are providing business value.
4 – 8 weeks
Algorithm prototyping to match best predictive/prescriptive approach for the data, processes with an eye towards balancing explainability, performance, and accuracy.
1 – 3 weeks
Exploratory Data Analysis to develop trends, summarization, and visualizations, prep for predictive analytics.
2 – 3 days
Meet with stakeholders, users, and decision-makers to understand data & decision-making processes.
2 – 3 days
Identify use case that will deliver most value to the business.