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-Recent advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. ML is impacting almost all domains of science or industry but determining the right ML workflow for a given problem remains a key question. To allow not only experts in the field to benefit from ML potential, last years have seen an increasing effort from the big data companies (Amazon AWS, Microsoft Azure, Google AutoML...) to provide any user with simple platforms for designing their own ML workflow. However, none of these solutions consider the design of ML workflow as a generic process intending to capture common processing patterns between workflows (even through workflows targeting different application contexts). These platforms either propose a set of dedicated solutions for given classes of problem (i.e. AutoML Vision, AutoML natural language , AutoML Translation...) or propose a recipe to build your own ML workflow from scratch (i.e. MS  Azure Machine Learning studio, RapidMiner).
+Recent advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. ML is impacting almost all domains of science or industry but determining the right ML workflow for a given problem remains a key question. To allow not only experts in the field to benefit from ML potential, last years have seen an increasing effort from the big data companies (Amazon AWS, Microsoft Azure, Google AutoML...) to provide any user with simple platforms for designing their own ML workflow. However, none of these solutions consider the design of ML workflow as a generic process intending to capture common processing patterns between workflows (even through workflows targeting different application contexts). These platforms either propose a set of dedicated solutions for given classes of problem (i.e. AutoML Vision, AutoML natural language , AutoML Translation...) or propose a recipe to build your own ML workflow from scratch (i.e. MS  Azure Machine Learning studio, RapidMiner).\\
 Is it then possible to envision the meta-learning process of designing ML workflow as a systematic approach analyzing past experiences to identify, explain and predict the right choices?
 This PhD thesis will address this issue by correlating research on software architectures (including product lines) and meta-learning, to bring ML workflow design to the next level by producing explanation on algorithm choices and by cutting portfolio exploration  complexity identifying common patterns between workflows.
 ===== Context =====
- Advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. To determine the right ML workflow for a given problem, numerous parameters have to be taken in account: the kind of data, expected predictions (error, accuracy, time, memory space), the choice of the algorithms and their judicious composition [14,11].
+ Advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. To determine the right ML workflow for a given problem, numerous parameters have to be taken in account: the kind of data, expected predictions (error, accuracy, time, memory space), the choice of the algorithms and their judicious composition [14,11]. \\
-To help with this task, Microsoft Azure Machine Learning, Amazon AWS, and RapidMiner Auto Model[12]  provide ML component assembly tools. However, faced with the complexity of choosing the "right" assembly, meta-learning offers an attractive solution, learning from the problems of the past.  The algorithm selection problem is one of its applications [5]: given a dataset, identify which learning algorithm (and which hyperparameter setting) performs best on it.
+To help with this task, Microsoft Azure Machine Learning, Amazon AWS, and RapidMiner Auto Model[12]  provide ML component assembly tools. However, faced with the complexity of choosing the "right" assembly, meta-learning offers an attractive solution, learning from the problems of the past.  The algorithm selection problem is one of its applications [5]: given a dataset, identify which learning algorithm (and which hyperparameter setting) performs best on it.\\
  Algorithm Portfolio generalizes the problem and automates the construction of selection models [8]. The immediate goal is the same: to predict the results of the algorithms on a given problem without executing them. Even if, in the portfolio, some selection models are built by meta-learning [1], the purpose is different. The portfolio is based on the systematic acquisition of knowledge about the algorithms it contains. The research then focuses on the quality and the return of knowledge, the acquisition process itself and the construction of selection models over time.