Establish a baseline performance for State-of-the-Art (SoTA) computer vision models with ease and use tuning to take them a step further. Today's world of Computer Vision offers different models for tasks like Image Classification, Object Detection, and Instance Segmentation. Each of these models has its strengths like some being faster, some being more accurate or some being good at specifics like identifying small objects, detecting anomalies, etc. The optimal model to go for depends on the user scenario at hand. With Azure AutoML for Images, users can explore and select from a variety of SoTA algorithms for a computer vision task and optionally tune the hyperparameters to optimize model performance with ease.

Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search advertising systems) that assign ads to pages should satisfy objectives such as plenty of audience for branding advertisers, clicks or conversions for performance-based advertisers, at the same time try to maximize overall revenue of the platform. In this paper, we propose an approach based on linear programming subjects to constraints in order to optimize the revenue and improve different performance goals simultaneously. We have validated our algorithm by implementing an offline simulation system in Alibaba E-commerce platform and running the auctions from online requests which takes system performance, ranking and pricing schemas into account. We have also compared our algorithm with related work, and the results show that our algorithm can effectively improve campaign performance and revenue of the platform.

Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learnt and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base {\em reasoner}, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the meta-reasoning module has to analyze the reasoning trace of multiple components with potentially different learning paradigms. Our approach works by distributing the generation of learning strategies among the different modules instead of centralizing it in the meta-reasoner. We implemented our technique in the GILA system, that works in the airspace task orders domain, showing an increase in performance.