As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption. This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case \& solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention. To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible.

Recent developments in sequential experimental design look to construct a policy that can efficiently navigate the design space, in a way that maximises the expected information gain. Whilst there is work on achieving tractable policies for experimental design problems, there is significantly less work on obtaining policies that are able to generalise well - i.e. able to give good performance despite a change in the underlying statistical properties of the experiments. Conducting experiments sequentially has recently brought about the use of reinforcement learning, where an agent is trained to navigate the design space to select the most informative designs for experimentation. However, there is still a lack of understanding about the benefits and drawbacks of using certain reinforcement learning algorithms to train these agents. In our work, we investigate several reinforcement learning algorithms and their efficacy in producing agents that take maximally informative design decisions in sequential experimental design scenarios. We find that agent performance is impacted depending on the algorithm used for training, and that particular algorithms, using dropout or ensemble approaches, empirically showcase attractive generalisation properties.

The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scenarios seen during training as well as novel out-of-distribution scenarios. In this work, we present a framework for dynamics generalization in deep reinforcement learning that unifies these two distinct types of generalization within a single architecture. We introduce a robust adaptation module that provides a mechanism for identifying and reacting to both in-distribution and outof-distribution environment dynamics, along with a joint training pipeline that combines the goals of in-distribution adaptation and out-of-distribution robustness. Our algorithm GRAM achieves strong generalization performance across indistribution and out-of-distribution scenarios upon deployment, which we demonstrate on a variety of realistic simulated locomotion tasks with a quadruped robot. Due to the diverse and uncertain nature of real-world settings, generalization is an important capability for the reliable deployment of data-driven, learning-based frameworks such as deep reinforcement learning (RL). Policies trained with deep RL must be capable of generalizing to a variety of different environment dynamics at deployment time, including both familiar training conditions and novel unseen scenarios, as the complex nature of real-world environments makes it difficult to capture all possible variations in the training process. Existing approaches to zero-shot dynamics generalization in deep RL have focused on two complementary concepts: adaptation and robustness. Contextual RL techniques (Hallak et al., 2015) learn to identify and adapt to the current environment dynamics to achieve the best performance, but this adaptation is only reliable for the range of in-distribution (ID) scenarios seen during training. Robust RL methods (Nilim & Ghaoui, 2005; Iyengar, 2005), on the other hand, maximize the worst-case performance across a range of possible environment dynamics, providing generalization to out-of-distribution (OOD) scenarios at the cost of conservative performance in ID environments.

As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount. A key component of reliability is the ability to estimate uncertainty, which enables the identification of areas of high and low confidence and helps to minimize the risk of error. In this study, we propose a machine learning pipeline called U-PASS tailored for clinical applications that incorporates uncertainty estimation at every stage of the process, including data acquisition, training, and model deployment. The training process is divided into a supervised pre-training step and a semi-supervised finetuning step. We apply our uncertainty-guided deep learning pipeline to the challenging problem of sleep staging and demonstrate that it systematically improves performance at every stage. By optimizing the training dataset, actively seeking informative samples, and deferring the most uncertain samples to an expert, we achieve an expert-level accuracy of 85% on a challenging clinical dataset of elderly sleep apnea patients, representing a significant improvement over the baseline accuracy of 75%. U-PASS represents a promising approach to incorporating uncertainty estimation into machine learning pipelines, thereby improving their reliability and unlocking their potential in clinical settings.

We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED). Traditional sequential BOED approaches require substantial computational time at each stage of the experiment. This makes them unsuitable for most real-world applications, where decisions must typically be made quickly. DAD addresses this restriction by learning an amortized design network upfront and then using this to rapidly run (multiple) adaptive experiments at deployment time. This network takes as input the data from previous steps, and outputs the next design using a single forward pass; these design decisions can be made in milliseconds during the live experiment. To train the network, we introduce contrastive information bounds that are suitable objectives for the sequential setting, and propose a customized network architecture that exploits key symmetries. We demonstrate that DAD successfully amortizes the process of experimental design, outperforming alternative strategies on a number of problems.

However, most of these are heuristics in nature (Twala et al., 2008), tailored towards some specific tree induction algorithm Decision trees are a popular family of models (Chen & Guestrin, 2016; Prokhorenkova et al., 2018), due to their attractive properties such as interpretability or make strong distributional assumptions about the data, and ability to handle heterogeneous such as the feature distribution factorizing completely (e.g., data. Concurrently, missing data is a prevalent mean, median imputation (Rubin, 1976)) or according to the occurrence that hinders performance of machine tree structure (Quinlan, 1993). As many works have compared learning models. As such, handling missing data the most prominent ones in empirical studies (Batista in decision trees is a well studied problem. In & Monard, 2003; Saar-Tsechansky & Provost, 2007), there this paper, we tackle this problem by taking a is no clear winner and ultimately, the adoption of a particular probabilistic approach. At deployment time, we strategy in practice boils down to its availability in the use tractable density estimators to compute the ML libraries employed. "expected prediction" of our models. At learning time, we fine-tune parameters of already learned In this work, we tackle handling missing data in trees at trees by minimizing their "expected prediction both learning and deployment time from a principled probabilistic loss" w.r.t.

Netflix's data-science team has open-sourced its Metaflow Python library, a key part of the'human-centered' machine-learning infrastructure it uses for building and deploying data-science workflows. The video-streaming giant uses machine learning across all aspects of its business, from screenplay analysis, to optimizing production schedules, predicting churn, pricing, translation, and optimizing its giant content distribution network. According to Netflix software engineers, Metaflow was built to help boost the productivity of its data scientists who like to express business logic through Python code but don't want to spend too much time thinking about engineering issues, such as object hierarchies, packaging issues, or dealing with obscure APIs unrelated to their work. The idea behind Metaflow was to give Netflix data scientists the ability to see early on whether a prototyped model would fail in production, allowing them to fix whatever the issue was and ideally speed up deployment times. Netflix in February revealed that Metaflow had helped reduce median deployment times from four months to just seven days.

Avaya and one of its A.I.Connect development partners, Afiniti, today announced a strategic partnership designed to improve contact center performance using artificial intelligence (AI). The partnership involves the use of Afiniti's behavioral pairing AI algorithm, which optimizes contact center outcomes by pairing customers with the contact center agents most likely to result in a desired outcome, as I wrote in a No Jitter article published last December. In that post I provided a high-level description of how Afiniti's AI-based customer-agent pairing works, showing where its machine learning algorithms fit in the pairing process. What's new with today's announcement is the depth of the integration between the Afiniti technology and Avaya's contact center software, along with an easier mechanism for Avaya contact center customers to purchase Afiniti's advanced routing capabilities. As part of this partnership agreement, Avaya will create an "AI edition" of Avaya Aura Contact Center Elite.

Many performance metrics have been introduced for the evaluation of classification performance, with different origins and niches of application: accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the absolute error, and the Brier score (with its decomposition into refinement and calibration). One way of understanding the relation among some of these metrics is the use of variable operating conditions (either in the form of misclassification costs or class proportions). Thus, a metric may correspond to some expected loss over a range of operating conditions. One dimension for the analysis has been precisely the distribution we take for this range of operating conditions, leading to some important connections in the area of proper scoring rules. However, we show that there is another dimension which has not received attention in the analysis of performance metrics. This new dimension is given by the decision rule, which is typically implemented as a threshold choice method when using scoring models. In this paper, we explore many old and new threshold choice methods: fixed, score-uniform, score-driven, rate-driven and optimal, among others. By calculating the loss of these methods for a uniform range of operating conditions we get the 0-1 loss, the absolute error, the Brier score (mean squared error), the AUC and the refinement loss respectively. This provides a comprehensive view of performance metrics as well as a systematic approach to loss minimisation, namely: take a model, apply several threshold choice methods consistent with the information which is (and will be) available about the operating condition, and compare their expected losses. In order to assist in this procedure we also derive several connections between the aforementioned performance metrics, and we highlight the role of calibration in choosing the threshold choice method.