Predictive ML

With Azure Machine Learning & AutoML
Smarter Forecasting, Better Planning

At Digital Bricks, we help organizations turn historical data into forward-looking insights using Predictive Machine Learning. By leveraging the power of Azure Machine Learning and AutoML, we design and deploy custom predictive models tailored to your business goals — whether it's revenue forecasting, demand planning, or resource allocation.

Why Predictive ML?

Forecast the Future with Confidence
Predict upcoming trends and performance by analyzing past behavior using advanced statistical and machine learning models.

Improve Planning & Decision-Making
Make smarter operational, financial, and strategic decisions using forecasts grounded in real data — not guesswork.

Custom Models for Your Use Case
From time series analysis to regression-based forecasting, we tailor the right model architecture for your specific scenario and forecast horizon.

AI, Explained Simply
We combine cutting-edge ML algorithms with real business language and logic so your team understands what the models are doing and why.

How We Build Predictive Models

We use Azure Machine Learning’s library of state-of-the-art algorithms — including scalable boosted decision trees, Bayesian recommendation systems, deep neural networks, and decision jungles — to build predictive models capable of solving high-impact business challenges.

AutoML: Smarter Time Series & Regression Forecasting

AutoML simplifies the process of selecting and optimizing the best forecasting approach for your data. We apply two primary model types:

Time Series Forecasting

Models that rely on historical target values to predict future outcomes:
Example:
Let yₜ represent the daily demand for a product. Time series models predict yₜ₊₁ using:

yₜ₊₁ = f(yₜ, yₜ₋₁, ..., yₜ₋ₛ)
Where f is a function trained on past data, and s is how much historical data is used.

Regression Forecasting

Models that use known future predictors to forecast the target value.
Example:
Predict orange juice demand based on known features like:

price, day of the week, and holidays
y = g(price, day, holiday)

These models assume that predictor variables are available at least up to the desired forecast horizon.

Hybrid Models: The Best of Both Worlds

AutoML also supports hybrid forecasting, blending time series lags with regression inputs — for more accurate results and less error compounding over time.

Key benefits:

• Direct Forecasting: Predict entire time windows at once, not just one step at a time
• Reduced Error Propagation: Avoid recursive feedback loops that degrade performance
• Lagged Features: Include historical values of both the target and predictors for context-aware forecasts

Where Predictive ML Delivers Value

• Demand Forecasting: Inventory and supply chain optimization
• Revenue Forecasting: Financial modeling and target tracking
• Workforce Planning: Resource and schedule optimization
• Sales & Promotions: Campaign planning based on likely impact
• Healthcare & Education: Utilization prediction and strategic planning

Digital Bricks can help you build, deploy, and maintain predictive ML models that drive better business decisions across your organization. Whether you're starting with raw data or looking to scale existing forecasting, we’ll bring clarity, accuracy, and automation to the process.

→ Contact us to begin your Predictive ML journey.

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