Workcloud Modeling Studio

**Overview**
Zebra Workcloud Modeling studio is a low-code / no-code Data Science Machine Learning (DSML) platform designed for retailers and CPG companies to predict future demand with speed and accuracy. It provides a collaborative environment for data scientists and ML engineers to train, test, and deploy highly scalable machine learning forecasting pipelines to predict future demand.

The platform blends domain expertise with machine intelligence to deliver highly accurate forecasts, leveraging state-of-the-art AI and machine learning algorithms specifically fine-tuned for retail time series.

Our platform helps companies stay ahead of demand volatility, leading to tangible business gains. According to McKinsey, a 10-20% improvement in forecast accuracy can reduce inventory costs by ~5% and boost revenues by 2-3%.

Workcloud Modeling Studio focuses on demand forecasting as the primary use case, yet its flexible architecture makes it extensible to other predictive analytics scenarios across Retail and CPG enterprises.

**Key Features**
1. Model Experimentation Workspace

Provides an interactive sandbox for data scientists to explore various forecasting techniques (ARIMA, ETS, Machine Learning Algorithms, Neural Network Architectures like RNN, Autoencoder, etc.) and Foundational Models for forecasting. Teams can easily run model bake-offs to identify the best algorithm with automated tracking of experiments, results, and artifacts.

2. Managed Infrastructure

Workcloud Modeling Studio is built on top of Databricks, which means no additional infrastructure to manage. Users simply pass their Databricks developer token into the studio, and the platform accesses their Databricks environment securely and seamlessly. There’s no new compute to provision or services to configure. Your data stays entirely within your own environment, and you only pay for the compute you use. With Modeling Studio leveraging your existing Databricks infrastructure, you get the power of a fully managed experience without the operational burden.

3. Automated Pipelines

End-to-end features to streamline all steps between experimentation and deployment. The Modeling Studio offers configurable, automated workflows for data preparation, model training, performance evaluation, and explainaibility. This allows users to go from raw dataset to high quality and intelligent forecasts within hours without any technical debt.

4. Scalability and Performance

Built on top of Apache Spark and Databricks, Workcloud Modeling Studio effortlessly scales to handle thousands of product-location combinations and years of history and any type of external data. Distributed computing ensures even fine-grained forecasts (e.g. store-SKU level) execute within tight windows. The platform’s architecture and modeling option supports analyzing data at any granularity – blending forecasts from store/item to brand/week – to provide a unified view across all channels and product hierarchies.

5. MLOps

Transitioning from experimentation to production is extremely simple and efficient. Workcloud Modeling Studio offers one-click deployment of ML pipelines, allowing data scientists to go from a model experiment to a production-grade deployment without rewriting code. All the MLOps functionality – including scheduling, monitoring, pipeline orchestration, artifact management, and version control – is fully managed within the platform itself. This reduces time-to-value and removes barriers to model operationalization.

6. Extensibility

While demand forecasting is the core focus, Workcloud Modeling Studio is extensible to a wide range of time-series and predictive use-cases. Users can integrate custom Python scripts or integrate external data sources (economic indicators, weather events, industry reports, etc.) to enrich the forecasts.

**Target Audience**

Workcloud Modeling Studio is built for enterprise data science and analytics teams in the retail and CPG sectors. Its primary users are data scientists, ML engineers, and analytics professionals who develop and maintain forecasting models. These users will appreciate collaborative workspace, advanced modeling capabilities, and integration with existing Databricks workflows. Additionally, the output of Modeling Studio benefits demand planners, supply chain managers, and business analysts who rely on accurate forecasts to make decisions on inventory, procurement, and marketing.

**Use cases**

1. Demand Planning and Inventory Optimization

Generate precise forecasts for product demand at various levels (store, region, channel) to ensure optimal inventory levels. By forecasting with high accuracy, retailers can reduce excess stock and avoid stockouts, directly improving working capital and customer satisfaction.

2. Promotional Forecasting

Predict the uplift in demand for products during promotions or holiday campaigns. Data Scientists can model different scenarios (with promotion, without promotion) to help marketing and sales teams understand the impact of campaigns on demand and plan accordingly.

3. New Product Demand Forecasting

Leverage advanced machine learning techniques and algorithms to forecast demand for new product launches or new store openings where historical data is limited.

4. Supply Chain and Replenishment

Extend demand forecasts to upstream supply chain planning. For manufacturers and CPG companies, Workcloud Modeling Studio can forecast product demand at distributions centers or wholesalers, helping to streamline production scheduling and replenishment. This ensures better alignment between consumer demand and supply plans.

5. Retail Sales and Revenue Forecasting

Leverage the platform to project sales figures for financial planning and budgeting (FP&A) processes. The platform’s ability to model at aggregate levels while preserving the fine detail makes it useful for both granular operational forecasts and high-level revenue forecasts for executives.

**Databricks Integration**
Workcloud Modeling Studio is natively integrated with your existing Databricks infrastructure. The platform does not require any additional setup or data migration on your part. Users simply create developer’s API token in their Databricks Workspace and securely passes it in Modeling Studio UI. All experimentation, model training, and production jobs are executed within the customers own Databricks environment, ensuring complete control, security, and compliance.

Because the platform runs on top of Databricks, it works seamlessly with your Lakehouse architecture, Delta Lake tables, and existing workflows. Modeling Studio does not move or copy data. This tight and secure integration enables frictionless experience, from loading data and experimentation with models to deploying forecasts and scheduling jobs. The result is a native, secure, and efficient forecasting platform that fits directly into your existing ecosystem without additional complexity.

**Getting Started**
Request a Demo or Try Now