For the European Union, lifelong learning has become a means of achieving both competitiveness and social cohesion in an increasingly knowledge-based and globalised economy. Though the concept of lifelong learning is not new, it now coincides with a period of rapid EU expansion. The research project the book is based on examines how lifelong learning is understood and operationalised, especially in countries within the area of the EU's expansion. Europe, its policy-makers and peoples, need to know whether lifelong learning can contribute to the construction of a European identity - and if so, how. The research points to the importance of diverse national contexts, which suggests a single model of lifelong learning across the EU is unlikely to be achieved. While the EU may encourage a common policy, and this may generate significant national policy developments, these will be strongly influenced by national context: institutional, political, social, ideological. Many countries will continue - consciously or unconsciously - to "pick and choose" between different EU priorities.

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly