Chelsea AI Ventures
Technical AI Training Developer Pathway 8 Weeks (Online, approx. 4-5 hours/week) or 3 Day Intensive (In-Person, London)

Building Time Series Projects for Production: From Forecasting to MLOps

Master the end-to-end process of building, deploying, and maintaining robust time series forecasting models in real-world environments

Target Audience

The Data Scientist/ML Engineer

Core Value

Master the end-to-end process of building, deploying, and maintaining time series forecasting models

Key Differentiator

Specialized focus on production challenges, including feature engineering and MLOps for temporal data

Learning Objectives

  • Perform exploratory data analysis specific to time series data, identifying trends, seasonality, and stationarity
  • Implement and compare classic statistical models (ARIMA, SARIMA) and modern ML models (XGBoost, Prophet)
  • Engineer robust features for time series data including lags, rolling statistics, and calendar features
  • Build and train deep learning models (LSTMs, GRUs) for complex forecasting tasks
  • Design a production architecture for forecasting systems including data pipelines and model serving
  • Implement monitoring strategies including forecast accuracy tracking and concept drift detection

Prerequisites

Foundational understanding of machine learning and Python programming.

Course Structure

Weeks 1-2: Foundations of Time Series Analysis & Feature Engineering

Time series EDA, stationarity testing, decomposition. Engineering lag features, rolling statistics, calendar features.

Activities:

  • Complete time series EDA notebook
  • Build feature engineering pipeline

Weeks 3-4: Statistical and Machine Learning Forecasting Models

Week 3: ARIMA, SARIMA, exponential smoothing. Week 4: Tree-based models, Prophet, ensemble methods.

Activities:

  • Implement multiple forecasting approaches
  • Build model comparison framework

Weeks 5-6: Deep Learning for Time Series Forecasting

Week 5: RNNs, LSTMs, GRUs fundamentals. Week 6: Advanced architectures, attention mechanisms.

Activities:

  • Build LSTM forecasting model
  • Experiment with different architectures

Weeks 7-8: MLOps for Time Series: Deployment and Monitoring

Production pipelines, model serving, performance monitoring, drift detection, automated retraining.

Activities:

  • Design production architecture
  • Implement monitoring dashboard

Topics Covered

Time series fundamentals and EDA
Stationarity and decomposition
Feature engineering for temporal data
Classical statistical models (ARIMA, SARIMA)
Machine learning for time series
Facebook Prophet
Deep learning architectures (LSTM, GRU)
Attention mechanisms for sequences
Production data pipelines
Model serving and API design
Forecast accuracy metrics
Drift detection and monitoring

Capstone Project

Build end-to-end forecasting system: preprocess data, compare models, select best performer, and create deployment plan with monitoring strategy.

Why This Course Matters

Forecasting drives critical business decisions across every industry. Sales predictions determine inventory. Demand forecasts guide staffing. Energy consumption models prevent blackouts. Yet most data scientists struggle to move from notebook experiments to production systems that stakeholders can actually rely on.

The gap between a model that works in development and one that performs reliably in production is massive. This course bridges that gap, focusing on the unique challenges that temporal data presents for deployment, monitoring, and maintenance.

What Makes This Course Different

While most time series courses end with model training, that’s where our focus begins. We tackle the hard questions: How do you handle late-arriving data? How do you detect when your model’s assumptions no longer hold? How do you build feature pipelines that update correctly in real-time?

Every technique is taught in the context of production requirements. You’ll learn not just how to build an LSTM, but how to deploy it in a way that handles missing data gracefully. Not just how to calculate MAPE, but how to build monitoring systems that alert you before forecast quality degrades.

Course Philosophy

We believe that production time series systems require a fundamentally different mindset than experimental modeling. It’s not about finding the most sophisticated algorithm—it’s about building robust systems that handle edge cases, adapt to changing patterns, and provide reliable forecasts day after day.

Throughout this course, we emphasize engineering excellence over algorithmic complexity. A simple model that reliably updates and monitors itself beats a complex model that breaks in production every time.

Who Should Take This Course

This course is perfect if you:

  • Have ML experience but struggle with time series specifics
  • Need to move forecasting models from notebook to production
  • Want to specialize in one of the most in-demand ML skills
  • Face real-world challenges like irregular timestamps or missing data
  • Are responsible for forecasts that drive business decisions
  • Want to build reliable systems, not just accurate models

If you’re ready to master production time series, this comprehensive course provides the specialized training you need.

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