How is machine learning different from your analytics and BI service?
Analytics and business intelligence measure and explain what has happened so a person can decide what to do. Machine learning predicts what has not happened yet, or automates a decision at a volume no person could handle. If your question is why did this change, that belongs on the analytics page. If it is what will happen next, or decide this automatically for every case, it belongs here. Most organizations get more value from doing the first well before attempting the second.
How is this different from your generative AI service?
This page covers predictive machine learning on your own structured data: forecasting, classification, scoring, ranking, and anomaly detection. Generative AI covers language and content: assistants, retrieval over documents, drafting, and extraction from unstructured text. They use different techniques, need different data, and fail in different ways, which is why they are separate pages.
Do we have enough data for machine learning?
It depends on how varied the problem is and how strong the signal is, not on a universal row count. What matters more is whether you have historical examples with labels that reflect the real outcome, whether the conditions that produced them still hold, and whether there is any leakage of the answer into the inputs. We assess this in the feasibility phase and will tell you if the answer is no.
How long does a machine learning project take?
The modeling is usually a small share of the elapsed time. Data preparation, deployment, and monitoring dominate, and their duration depends on the state of your data platform and the system the prediction must reach. We scope after feasibility rather than quoting a standard duration, because a figure given before profiling the data would be a guess.
What accuracy can you achieve?
We do not quote accuracy figures in advance, and we would treat any supplier who does with caution. Achievable performance depends entirely on your data, your problem, and the baseline being improved on, and an accuracy number stated without those is unmeasurable. What we commit to is establishing an honest baseline, reporting performance against it on a held-out set, and telling you plainly if the improvement does not justify the system.
What happens when the model degrades over time?
Every model degrades as the world moves away from its training data. We monitor input drift, prediction distributions, and outcome quality, define the thresholds that trigger retraining, and hand over the runbook. Detecting degradation through monitoring rather than through a complaint is the difference between a maintained system and an abandoned one.
Can you explain individual predictions?
To varying degrees, depending on the model. Simpler models are directly interpretable; complex ones require attribution techniques that indicate influence rather than provide a definitive reason. If you must justify a decision to a regulator, a customer, or an auditor, tell us at the start, because it constrains the model choice and cannot be added afterwards.
Do we need data engineering first?
Usually yes, at least for the data the model consumes. Machine learning needs reproducible datasets and features computed identically in training and production, and neither survives being assembled by hand. Our data engineering page covers that work; how much is required depends on the state of your current platform.
Do you build recommendation engines?
Yes. Recommendation is a ranking problem and is well suited to machine learning where you have sufficient interaction history. For retail specifically, our recommendation engines page under the retail practice covers the applied case, including the catalog and behavioral data it depends on.
How do we get started?
Describe a decision your organization makes repeatedly where being right more often would be worth something measurable. We will assess feasibility, including whether the data supports it and whether a simpler method would serve, and tell you honestly if machine learning is not the right answer.
