Bridge the Gap From Models to Production Systems
Learn to deploy and maintain machine learning models in real-world environments through practical, hands-on experience with MLOps.
Back to HomeWhat This Workshop Delivers
This workshop prepares you for the practical realities of machine learning engineering. You'll gain the skills needed to take models from notebooks to production, understanding how to deploy, monitor, and maintain ML systems that deliver reliable value.
Production Deployment Skills
Master the techniques needed to deploy models that serve predictions reliably in real applications.
Feature Engineering Expertise
Learn to create features that improve model performance and translate raw data into predictive signals.
MLOps Understanding
Understand the practices that keep ML systems running smoothly through monitoring and continuous improvement.
Ethical AI Awareness
Develop sensitivity to bias detection and fairness considerations in production ML systems.
The Challenge of Production Machine Learning
Building models in notebooks feels very different from deploying them in production environments. Many practitioners find that their skills don't translate smoothly when it comes time to create systems that serve real users reliably and continuously.
Deployment Uncertainty
You might have trained successful models but feel unsure about how to actually deploy them. The path from a working model to a production API or service seems unclear, with many technical decisions to navigate.
Maintenance Concerns
Models that perform well initially can degrade over time as data distributions shift. Without monitoring and retraining strategies, you might worry about maintaining model quality after deployment.
Feature Engineering Gaps
Raw data rarely comes in model-ready form. Transforming data into effective features while ensuring consistency between training and serving environments presents practical challenges.
Integration Complexity
ML models need to work within larger systems. Understanding how to integrate predictions into applications, handle errors gracefully, and scale to meet demand requires experience beyond model training.
Learning Production ML Through Practice
This workshop addresses deployment challenges through end-to-end projects that simulate real ML engineering scenarios. You'll work through the complete lifecycle of ML systems, gaining practical experience with each component.
End-to-End Project Experience
You'll work on projects that span the entire ML lifecycle—from data preparation and feature engineering through model training, deployment, and monitoring. These comprehensive exercises help you understand how each piece connects in real systems.
MLOps Practices and Tools
We introduce tools and workflows for model versioning, experiment tracking, and deployment automation. You'll learn containerization basics, API creation, and monitoring setup—skills that translate directly to professional ML engineering roles.
Ethical Considerations in Production
We explore how to identify and address bias in models, considering fairness implications before deployment. Understanding these ethical dimensions helps you build systems that serve users responsibly.
Your Workshop Experience
This workshop emphasizes hands-on work with real scenarios. You'll collaborate with peers on projects that mirror actual ML engineering challenges, with instructors providing guidance as you navigate technical decisions.
Feature Engineering and Data Preparation
Starting with raw data, you'll learn to engineer features that capture predictive patterns. We cover techniques for handling missing data, encoding categories, and creating temporal features while ensuring consistency across environments.
Model Development and Optimization
You'll work on training models with production constraints in mind—considering inference speed, memory usage, and interpretability alongside predictive performance. Experiment tracking helps you compare approaches systematically.
Deployment and Serving
Moving models to production, you'll create APIs for serving predictions and explore containerization for consistent deployment. These practical exercises prepare you for real-world model deployment scenarios.
Monitoring and Maintenance
You'll implement monitoring systems that track model performance and data drift. Learning to identify when retraining is needed and how to update models safely completes your production ML skillset.
Investment in Production ML Skills
Complete Applied ML Workshop Program
Workshop Includes
- 56 hours of intensive hands-on work over 10 weeks
- Three comprehensive end-to-end ML projects
- Access to deployment and monitoring tools
- Collaborative learning with peer review
Professional Capabilities
- Deploy ML models to production environments
- Monitor and maintain production ML systems
- Engineer effective features for model performance
- Apply MLOps best practices confidently
This investment equips you with production ML engineering skills that are directly applicable to professional roles. Payment plans are available to make the workshop accessible while you develop these valuable capabilities.
Demonstrating Your Applied Skills
Progress in this workshop is measured through working systems rather than theoretical assessments. You'll build a portfolio of deployed projects that demonstrate your capabilities to potential employers.
Project-Based Assessment
Each project requires you to deploy a working system. Your implementations are evaluated on functionality, code quality, and adherence to production ML best practices.
Performance Monitoring
You'll demonstrate your ability to monitor deployed models and identify issues. This includes setting up dashboards and alerting systems that track model health over time.
Peer Review Process
Collaborative reviews help you learn from others' approaches and receive constructive feedback. This mirrors professional ML engineering where code review is standard practice.
Workshop Timeline
Across 10 intensive weeks, you'll complete three major projects that build on each other. Most participants finish with a portfolio demonstrating their ability to deploy and maintain production ML systems.
Feature engineering and data pipeline development
Model deployment and API creation
Monitoring, maintenance, and ethical considerations
Our Commitment to Your Success
This workshop requires dedication and active participation. We provide the structure and support you need to develop production ML skills through focused, hands-on work.
Early Program Evaluation
After completing the first project (approximately 3 weeks), if you feel the workshop doesn't meet your expectations, we'll work to address concerns or provide a full refund. We want you to feel confident about the learning experience.
Technical Support Access
When deployment issues arise or monitoring systems behave unexpectedly, our instructors help troubleshoot. This ongoing support ensures technical challenges don't block your progress through the material.
Pre-Workshop Consultation
Before enrolling, discuss your experience level and goals with our team. This workshop assumes ML foundations—we'll help determine if you're ready or suggest preparatory steps if needed.
Collaborative Environment
Work alongside other ML practitioners tackling similar challenges. This collaborative setting provides opportunities to learn from diverse approaches and share insights throughout the workshop.
Start Your Production ML Journey
Taking the step toward production ML skills is straightforward. Here's how to get started with the Applied ML Workshop.
Express Your Interest
Fill out the contact form to let us know you're interested in the Applied ML Workshop. Share any relevant background information that helps us understand your current experience.
Discuss Your Background
We'll schedule a conversation to understand your current ML skills and goals. This helps us ensure the workshop matches your experience level and that you're prepared for the hands-on nature of the program.
Review and Decide
After our discussion, you'll understand what the workshop entails and whether it fits your learning objectives. Take time to consider—we want you to feel certain this is the right step for your development.
Begin the Workshop
Once enrolled, you'll receive project specifications and access to necessary tools. We'll orient you to the workshop structure and introduce you to your cohort before the first session.
Build Production-Ready ML Skills
Take the next step in your ML journey. Learn to deploy, monitor, and maintain models in real-world environments through hands-on practice with experienced guidance.
Connect With Our TeamReady to discuss whether this workshop matches your experience level? We're here to help you determine the right path.
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