In production recommender systems, feature preprocessing must be faithfully replicated across training and inference environments. This often requires duplicating logic between offline and online environments, increasing engineering effort and introducing risks of dataset shift. We present Kamae, an open-source Python library that bridges this gap by translating PySpark preprocessing pipelines into equivalent Keras models. Kamae provides a suite of configurable Spark transformers and estimators, each mapped to a corresponding Keras layer, enabling consistent, end-to-end preprocessing across the ML lifecycle. Framework's utility is illustrated on real-world use cases, including MovieLens dataset and Expedia's Learning-to-Rank pipelines. The code is available at https://github.com/ExpediaGroup/kamae.

This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select the optimal variants and learns quickly from every interaction they make. It offers a powerful solution to improve user experiences while minimizing the costs and time associated with traditional testing methods. The platform unlocks the ability to iterate towards optimal product solutions quickly, even in ever-changing content and continuous "cold start" situations gracefully.

I am Heather Laurence and I am a Director of Program Management for AI & Machine Learning. I've been with Expedia Group for 4 years in AI & ML and before that was a Program Manager for software development at companies for a total of 15 years. I live in Austin, Texas with my husband and two children and we love to go on adventures in our RV! I love working in the AI & Machine Learning space at Expedia Group! When I came aboard, I knew nothing about machine learning and was given the task to transform then HomeAway's data science organization into an agile shop.

At Expedia Group, we strive to improve our customer satisfaction by providing a frictionless shopping and support experience. A core part of this are machine learning models that power Expedia Group's virtual agents, that are available 24x7 to guide customers through any changes in their travel plans, pull-up information about their upcoming trips, book new trips and much more. Each capability has an associated'intent', which is identified by an intent classification model. However, natural language is messy, and not all customer utterances are actionable -- perhaps not even relevant. Therefore, the predicted probabilities of a classification model -- intent classifier in this case -- should be reliable: a misclassification should not have a high probability.

AIOps can help IT Ops manage today's considerable challenges of scaling operational practices and exceeding business expectations. Digital transformation is a strategic priority, and that almost always means launching new cloud-native applications, scaling data platforms, and improving employee experiences. At the same time, IT must address more complexities such as supporting legacy enterprise systems, establishing multicloud capabilities, addressing increasing security issues, and responding to global regulations. In other words, business leaders expect IT operations to deliver more capabilities faster, more reliably, and without dramatically adding costs, technical debt, or complicated operating procedures. At BigPanda's Resolve '21 and Pandapalooza, I heard from several leaders paving a new IT operating model based on automation, machine learning, and integrated ITSM workflows while empowering people to make smarter and faster decisions.

AI Singapore (AISG) was launched in June 2017 as an integrated, impact-driven, research and innovation program in artificial intelligence (AI) for the entire country. As a national initiative, AISG brings together the strength of Singaporean research bodies in Singapore's Autonomous Universities (AUs) and research institutes, together with the vibrant ecosystem of AI start-ups and companies developing AI products, to perform use-inspired research, create innovative AI solution, and develop the talent to power Singapore's AI efforts. To achieve Singapore's national mission, AISG's activities are anchored around four key pillars: An organization can propose a problem statement where no commercial-off-the-shelf AI solution exists, but can potentially be solved through AISG's ecosystem of researchers and research IPs within nine to 18 months. AISG will assemble a team of AI researchers and engineers from Singapore's research and development ecosystem to work on an organization's problem statement. Through a collaborative process, a company's existing technical manpower will work alongside a team of AI researchers and engineering assembled by AISG to develop AI solutions while helping the company build up its internal AI capabilities.

As a part of Expedia Group's partnership with AWS we recently took an amazing opportunity to host a DeepRacer competition in our Brisbane office. DeepRacer is designed to introduce people of all backgrounds to Machine Learning. The goal of the competition is to engineer a control loop for an autonomous toy racing car that enables the car to complete a full circuit of a physical race track in the shortest amount of time. This control loop is constructed using a Machine Learning technique called Reinforcement Learning. Reinforcement Learning encourages an autonomous machine to perform certain actions.

AI and machine learning are changing global consumption habits, and companies are playing catch-up. Good salespersonship is a species of street smarts. It's about quickly sizing up your customers and pitching your wares in terms that reverberate with their unspoken needs and desires. As AI and machine learning increasingly intersect with e-commerce, these priceless human skills are finding algorithmic analogues – not just at point of sale, but throughout the customer journey. The results will be familiar to online shoppers everywhere.

Expedia Group has announced a collaboration with AI Singapore (AISG) – an inter-agency unit tasked to catalyse and grow the country's artificial intelligence (AI) capabilities – under its flagship 100 Experiments (100E) programme to develop an AI solution to transform the online search experience for Asian travellers. The first online travel platform to collaborate with AISG for 100E, Expedia Group will provide a team of experienced engineers, data scientists and marketers to work with the AISG's project lead, project managers and AI apprentices to enhance travel search query understanding and improve the accuracy of search query resolution in Asian languages. Today's search engines are efficient in understanding travel search queries and providing query resolutions in English, as English is the dominant language used online by 25 per cent of all Internet users. However, when dealing with travel search queries conducted in Asian languages such as Japanese, Korean, simplified Chinese and traditional Chinese, the performance of the search engines declines significantly and the accuracy of query resolution dips. For a start, the Expedia Group and AI Singapore project team will leverage natural language processing and machine learning to develop an AI-based model to enhance search query understanding and resolution in the Japanese language, before extending the model to other Asian languages to enhance online search efficiency.

Would you like to solve real-world problems using advanced machine learning for the largest online travel company worldwide? The Expedia Group team in Geneva helps our customers and partners to find and post the best accommodation supply on our travel platform, by transforming raw data into insights that power external and internal services. This role is for a Data Scientist to join our fast-growing Data Science team. Our mission is to help the organization understand, shape and optimize its hotel and vacation rental portfolio across the world. We create state of the art machine learning models to make our travel platform more efficient and increase its relevance for travelers and hotel partners worldwide, for example by optimizing our lodging supply and forecasting demand.