The Complete Machine Learning Engineer
Data Engineering for Machine Learning
Inputs and Outputs: The Data Warehouse as a Production Service
Create the flexible and evolvable data ingestion systems required to support streaming data pipelines and ML use cases.
Analyze the tradeoffs between row-oriented and column-oriented data formats for use during data ingestion, analysis, model training, and model serving.
Solve a broad class of common ML problems by building tools for moving large datasets from your data warehouse into a low-latency serving system in your production environment.
Training Machine Learning Models: The Data Engineering Perspective
Compose data from multiple sources and time scales into coherent datasets that are designed to avoid the most common sources of error in model training.
Evolve your data models beyond supporting a single ML use case into a shared knowledge resource that lets your company bring machine learning everywhere it is needed.
Create a data platform for feature evaluation and model training that enables data scientists and ML researchers to easily trade off speed, flexibility, and compute costs.
Data Quality and Monitoring in the Data Warehouse and Production
Create tools for linking data profiling and quality checks from model training into your production model deployments.
Understand the benefits and the limitations of using standard application performance monitoring (APM) tools for data and ML monitoring problems.
Balance the need for comprehensive and thorough data quality checks with the cost and performance overhead required to perform those checks in both the data warehouse and the production environment.
From Batch to Streaming: Experiments and Contextual Bandits
Understand the unique constraints and opportunities for evaluating ML models in an online serving environment beyond normal A/B testing.
Design streaming data pipelines for performing rapid evaluation of models for recommendations, ranking, and classification problems.
Create the data infrastructure required to support reinforcement learning and contextual bandits in order to support ML models that can learn in real time.
Designing State of the Art Recommender Systems
Introduction to Recommender Systems: Basics and Classic Techniques
Develop an understanding of the fundamental components and techniques behind modern recommender systems
Design a basic recommender system using classic approaches such as collaborative filtering or content based
Contrast classic approaches to recommendation by listing their pros and cons (e.g., cold-start)
Learning to Rank and Hybrid Approaches to Recommendation
Develop an intuition of how ranking approaches work in practice.
Design a recommender system based on learning to rank approaches.
Use hybridization techniques to combine any of the approaches introduced so far
Other Advanced Approaches to Recommending Content
Introduce the explore/exploit dilemma in recommender systems and select the appropriate exploration/exploitation tradeoff by understanding multi-armed bandits and similar approaches.
Introduce contextual aspects/features to the recommender system by selecting the appropriate modeling approach.
Use deep learning for complex recommendations such as sequences, image, or text.
Recommender Systems in Industry I
Contrast and differentiate the approaches to recommendation used at LinkedIn and Pinterest
Understand and argue about the importance of ethics and fairness when designing a recommender system
Build an intuition for deep learning approaches to recommender systems that will be described in later sessions
Recommender Systems in Industry II
Contrast and differentiate the approaches to recommendation used at Netflix and Quora
Understand the role of UI components such as explanations in the design of a recommender system
Design an end-to-end recommender system infrastructure, from training to serving model results in real-time
Modern Forecasting in Practice
Should your business problem be solved with forecasting?
Understand which and how business processes can be optimized by incorporating (probabilistic) predictions of future outcomes
Differentiate strategic from operational forecasting problems with examples from Zalando and Amazon
Measure and compare the accuracy of different forecasts
Forecasting solutions using a small set of time series
Case Study: Forecasting top-level energy demand and prices
Understand the underlying business problem and the challenges of the resulting data-constrained forecasting problem
Identify the effects and structural components that make up the data, such as trend(s), seasonality, exogenous shocks, and noise
Identify the appropriate method and tool, such as linear regression, ETS, and ARIMAX
Forecasting solutions with a large set of time series
Case Study: Retail demand forecasting
Build an intuition for the data via visualization of individual time series and aggregate summaries
Obtain co-variates/features and process them
Use and tune global ML-powered methods such as Gradient Boosted Trees and Neural Network-based methods like DeepAR
Forecasting solutions with dependency structures
Case Study: Forecasting with causal inputs
Forecast demand subject to price changes for millions of products
Build what-if analysis using simple and advanced approaches
Evaluate & improve forecasting in counterfactual situations
What best practices help you avoid common pitfalls in production?
Practical tactics for forecasting exemplified by labor planning
Productionize forecasting models including retraining schemes
Handle missing data and the associated perils
Research approaches to outliers/extreme events such as blizzards and pandemics