Data assimilation (DA), as an indispensable component within contemporary Numerical Weather Prediction (NWP) systems, plays a crucial role in generating the analysis that significantly impacts forecast performance. Nevertheless, the development of an efficient DA system poses significant challenges, particularly in establishing intricate relationships between the background data and the vast amount of multi-source observation data within limited time windows in operational settings. To address these challenges, researchers design complex pre-processing methods for each observation type, leveraging approximate modeling and the power of super-computing clusters to expedite solutions. The emergence of deep learning (DL) models has been a game-changer, offering unified multi-modal modeling, enhanced nonlinear representation capabilities, and superior parallelization. These advantages have spurred efforts to integrate DL models into various domains of weather modeling. Remarkably, DL models have shown promise in matching, even surpassing, the forecast accuracy of leading operational NWP models worldwide. This success motivates the exploration of DL-based DA frameworks tailored for weather forecasting models. In this study, we introduces FuxiDA, a generalized DL-based DA framework for assimilating satellite observations. By assimilating data from Advanced Geosynchronous Radiation Imager (AGRI) aboard Fengyun-4B, FuXi-DA consistently mitigates analysis errors and significantly improves forecast performance. Furthermore, through a series of single-observation experiments, Fuxi-DA has been validated against established atmospheric physics, demonstrating its consistency and reliability.

Nintendo is riding high off the runaway success of its new, hybrid Nintendo Switch game console. The Japanese gaming giant dominated industry sales charts for both hardware and software in October thanks to high demand for the Switch and the release of the hotly anticipated Super Mario Odyssey, a flagship game for the system. Nintendo's brands are so hot right now that the best you can hope for on Black Friday is the Switch being in stock at all. The company is also using this success as an opportunity to break one of its longstanding traditions of not doing much outside the realm of video games. Back in June, Nintendo announced a Super Nintendo World theme park addition to Universal Studios Japan, set to open in 2020.

Data Scientists live and breathe data. We choose the best tools for that task from a vast array of ever changing tools. We turn data into information, information into discovered knowledge, and through wisdom the discovered knowledge is turned into intelligent action. Our tool suites include many open source software products. In this session we will review the foundations for today's Data Scientist's skill set, introducing the concepts behind machine learning, data mining, analytics and data science and the open source tool suite that has served us well over the past two decades.

Hi Tim - No, I haven't tried that. To be clear, are you thinking of images as "sequences" of pixels? If that's the case, I suppose one could use some sequence-related algorithms, like RNN/LSTM, but with two dimensions. Typically, CNNs are used for images since they encode the proximity of neighboring pixels. To your second point, one could submit the image to the model before it's fully loaded, and get less-than-optimal results until the image is fully loaded.

In our ongoing series showcasing the awesome community contributed content in the Cortana Intelligence GaIlery, I have with me Val Fontama. Val Fontama is a Principal Data Scientist Manager on the Azure team. Today he is chatting with us about the Predictive Maintenance model in the Gallery that predicts yield failure in a semiconductor manufacturing process. Predictive maintenance helps you deal with a problem even before it occurs saving you time and money. You can access the model used in this conversation and follow along.

Data analysis seeks to learn from experience. Better inferences require better thinking and better tools. Practical advice about how to make more credible conclusions based on data. What we can expect in the future, and what we should aspire to in the future.

By integrating scalable human judgment into your analytics solution, you can augment your lower-confidence machine learning predictions with accurate labels, and these labels can be used as additional training data to help improve your model. In this session, we'll demonstrate how to build a real-world text analytics workflow using machine learning with humans in the loop.

Join us to hear from thought leaders and Microsoft engineers on the latest Big Data, Machine Learning, Artificial Intelligence, and Open Source techniques and technologies. Join Big Data engineers, Data Scientists, Machine Learning practitioners and managers to share best practices. Up-level your technical foundation with product sessions, hands on labs and access to Microsoft Ignite's expo hall to learn about the latest Open Source (including R, Hadoop, Spark and more) and Microsoft technologies for Big Data, Advanced Analytics, Machine Learning & AI. Get inspired by what data driven solutions can enable. Learn about real-world examples directly from leading customers sharing use cases, architectural guidance and practical tips to help you accelerate sponsorship and adoption of your solutions.

With the growing focus on renewable energy, there is a need to accurately forecast energy production. In this video, we explore a typical work flow when forecasting wind energy and wrap up the conversation with possible predictive maintenance use cases for the wind turbines. Although the discussion focuses on wind energy domain, this work can be easily reused with minor tweaks for other renewable energy sources.

Cognitive Services and the Bot Framework are two new exciting technologies that Microsoft is offering to devs; in this chat Erica talks about how she combined the two to create a bot that can help you monitor all the exciting events at the Olympics.