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Medidata Solutions is an American technology company that develops and markets software as a service (SaaS) for clinical trials. These include protocol development, clinical site collaboration and management; randomization and trial supply management; capturing patient data through web forms, mobile health (mHealth) devices, laboratory reports, and imaging systems; quality monitor management; safety event capture; and monitoring.

Learn & Deploy Data Science Web Apps with Streamlit


Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful web apps in minutes. Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. In just a few minutes you can build and deploy powerful data apps. On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.

Artificial Intelligence Trends That Will Dominate In 2022


In 2022, businesses will be using artificial intelligence (AI) in more innovative ways than ever before. Some of the most popular ways that AI will be used in businesses include: Chatbots will be used to communicate with customers. AI will be used to analyze data and make decisions. Robotics will be used to automate tasks. AI will be used to create new products and services. Virtual assistants will be used to manage tasks. AI will be used to improve customer service. Predictive analytics will be used to make decisions. It allows users to interact with digital objects using their smartphones. While mixed reality combines real world elements with virtual ones. These vehicles can drive on highways and through city streets without assistance from a driver or any human behind the wheel. They also protect organizations against malicious attacks. By 2030, this number is projected to grow to 3.1 billion. These people generate more than 70 percent of all greenhouse gas emissions and over 80 percent of global water usage.

Customer churn prediction for SaaS companies


See how we help SaaS companies use machine learning, predictive models and data-driven CX strategies to prevent attrition.

RAMANMETRIX: a delightful way to analyze Raman spectra Machine Learning

Although Raman spectroscopy is widely used for the investigation of biomedical samples and has a high potential for use in clinical applications, it is not common in clinical routines. One of the factors that obstruct the integration of Raman spectroscopic tools into clinical routines is the complexity of the data processing workflow. Software tools that simplify spectroscopic data handling may facilitate such integration by familiarizing clinical experts with the advantages of Raman spectroscopy. Here, RAMANMETRIX is introduced as a user-friendly software with an intuitive web-based graphical user interface (GUI) that incorporates a complete workflow for chemometric analysis of Raman spectra, from raw data pretreatment to a robust validation of machine learning models. The software can be used both for model training and for the application of the pretrained models onto new data sets. Users have full control of the parameters during model training, but the testing data flow is frozen and does not require additional user input. RAMANMETRIX is available in two versions: as standalone software and web application. Due to the modern software architecture, the computational backend part can be executed separately from the GUI and accessed through an application programming interface (API) for applying a preconstructed model to the measured data. This opens up possibilities for using the software as a data processing backend for the measurement devices in real-time. The models preconstructed by more experienced users can be exported and reused for easy one-click data preprocessing and prediction, which requires minimal interaction between the user and the software. The results of such prediction and graphical outputs of the different data processing steps can be exported and saved.

Forecasting: theory and practice Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Big Data Industry Predictions for 2022 - insideBIGDATA


As a result, all major cloud providers are either offering or promising to offer Kubernetes options that run on-premises and in multiple clouds. While Kubernetes is making the cloud more open, cloud providers are trying to become "stickier" with more vertical integration. From database-as-a-service (DBaaS) to AI/ML services, the cloud providers are offering options that make it easier and faster to code. Organizations should not take a "one size fits all" approach to the cloud. For applications and environments that can scale quickly, Kubernetes may be the right option. For stable applications, leveraging DBaaS and built-in AI/ML could be the perfect solution. For infrastructure services, SaaS offerings may be the optimal approach. The number of options will increase, so create basic business guidelines for your teams.



Developed by a team with decades of experience and expertise in the Gaming Industry. It's an AI powered Software-as-a-Service (SaaS) platform which provides both visual and advanced analytics using Deep Learning and Machine Learning. It is a scalable and robust gaming analytics platform which is easy to integrate with data from various gaming systems. It reduces the process of transforming data to information to action in a matter of days and weeks instead of months...know more

WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy Artificial Intelligence

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser's features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection via local differential privacy mechanism. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.

3 ways to use data, analytics, and machine learning in test automation


Just 10 years ago, most application development testing strategies focused on unit testing for validating business logic, manual test cases to certify user experiences, and separate load testing scripts to confirm performance and scalability. The development and release of features were relatively slow compared to today's development capabilities built on cloud infrastructure, microservice architectures, continuous integration and continuous delivery (CI/CD) automations, and continuous testing capabilities. Furthermore, many applications are developed today by configuring software as a service (SaaS) or building low-code and no-code applications that also require testing the underlying business flows and processes. Agile development teams in devops organizations aim to reduce feature cycle time, increase delivery frequencies, and ensure high-quality user experiences. The question is, how can they reduce risks and shift-left testing without creating new testing complexities, deployment bottlenecks, security gaps, or significant cost increases?