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-title: "Introduction"
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-
-Welcome to **Building Machine Learning Systems That Don't Suck!**
-
-## Program Structure
-
-#### Session 1 - How To Start (Almost) Any Project
-
-* What makes production machine learning different from what you've learned.
-* The strategy to solve the right problem using the right solution.
-* Critical questions to ask before starting any project.
-* Problem framing, inversion, and the haystack principle for building successful applications.
-* The first rule of machine learning engineering and how to start building.
-* Data collection strategies. A technique to determine how much data you need.
-* The problem of selection bias and how to deal with it.
-* Labeling data. Human annotations, natural labels and weak supervision.
-* Active learning using the uncertainty and diversity sampling strategies.
-
-#### Session 2 - Building Models
-
-* The role of data cleaning and feature engineering to build better models.
-* Turning data into numbers using vectorization techniques. 
-* Producing homogeneous features using normalization and standardization.
-* Handling and interpreting missing values using imputation techniques. 
-* The approach to choosing the best model to solve any problem. 
-* Random baselines and the zero-rule algorithm.
-* How to use overfitting to build models that don’t suck.
-* Hyperparameter tuning and experiment tracking.
-* Measuring the quality of your holdout set.
-* An introduction to distributed training using data parallelism and model parallelism.
-
-#### Session 3 - Offline Evaluation
-
-* The role of a baseline to contextualize the evaluation process.
-* Framing evaluation metrics in the context of business performance.
-* Evaluating models using holdout sets and cross-validation.
-* Introduction to data leakages and leaky validation strategies.
-* Invariance tests and model fairness.
-* The problems with metric summarization.
-* Testing models on specific slices of data.
-* Error analysis and measuring the impact of potential improvements.
-* Introduction to backtesting and time-based evaluation strategies. 
-* Dealing with disproportionally important examples and rare cases.
-
-#### Session 4 - Serving Predictions
-
-* Introduction to model versioning and the model registry.
-* The trade-offs when serving predictions. An introduction to latency and throughput.
-* Dynamic serving and static serving predictions.
-* Two-phase predictions and deploying models on the edge.
-* Enforced modularization and inference pipelines.
-* Evaluating individual pipeline models.
-* Introduction to human-in-the-loop workflows.
-* Cost-sensitive deployment architectures.
-* Using test-time augmentations to improve predictions.
-* Model compression using pruning, quantization, and knowledge distillation.
-
-#### Session 5 - Drift and Monitoring
-
-* Dealing with catastrophic predictions and the problem with edge cases.
-* The problem with feedback loops and how to fix them.
-* Data distribution shifts. Understanding concept drift and data drift.
-* Using adversarial validation to detect distribution shifts.
-* An introduction to monitoring model behavior in production systems.
-* Monitoring model inputs, operational metrics, predictions, and user feedback.
-* How to keep your models working in the face of distribution shifts.
-
-#### Session 6 - Continual Learning
-
-* Introduction to continual learning.
-* Techniques to determine how frequently to retrain your models.
-* Techniques to determine what data to use to retrain a model.
-* Common triggers to initiate a retraining process.
-* Understanding stateless training and stateful training.
-* Catastrophic forgetting and how to prevent it.
-* The importance of testing in production.
-* Testing models using A/B testing, shadow deployments, canary releases, and interleaving experiments.