Using Azure Machine Learning, create a machine learning model to identify which clients are most likely to leave. Gather and prepare client data from SQL databases or Azure Blob Storage. Conduct exploratory data analysis and feature engineering. Use custom ML pipelines or AutoML to train classification models. Set up the model as a real-time endpoint and keep an eye out for drift in its performance.

Create a time series forecasting program to estimate product sales. Utilize past sales information that is kept in Azure Data Lake. Use Python or Azure ML Designer to clean and transform data for training. Use Azure AutoML to create custom regression models or forecasts. For continuous predictions, implement a batch inference pipeline and connect it to Power BI for visualization.

Use Azure Machine Learning and Azure Cognitive Services to develop an NLP solution for analyzing customer sentiment from reviews. Take in text data from Cosmos DB or Azure Blob Storage. Tokenization, stop-word removal, and embeddings are examples of preprocessing text. To classify reviews as neutral, negative, or positive, train and assess classification models. Install as a real-time API to connect to a dashboard or web application.