Open-source technologies have had a profound impact on the world of AI and machine learning, enabling developers, data scientists, and organizations to collaborate, innovate, and build better AI solutions. As large AI models like GPT-3.5 and DALL-E become more prevalent, organizations are also exploring ways to leverage existing open-source models and tools without needing to put a tremendous amount of effort into building them from scratch. Microsoft Azure AI is leading this effort by working closely with GitHub and data science communities, and providing organizations with access to a rich set of open-source technologies for building and deploying cutting-edge AI solutions. At Azure Open Source Day, we highlighted Microsoft's commitment to open source and how to build intelligent apps faster and with more flexibility using the latest open-source technologies that are available in Azure AI. Recent advancements in AI propelled the rise of large foundation models that are trained on a vast quantity of data and can be easily adapted to a wide variety of applications across various industries.

This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. This Professional Certificate teaches learners how to create end-to-end solutions in Microsoft Azure. They will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

Azure AI is driving innovation and improving experiences for employees, users, and customers in a variety of ways, from increasing workday productivity to promoting inclusion and accessibility. The success of Azure AI--featuring Azure Cognitive Services, Azure Machine Learning, and Azure OpenAI Service--is built on a foundation of Microsoft Research, a wide range of Azure products that have been tested at scale within Microsoft apps, and Azure customers who use these services for the benefit of their end users. As 2023 begins, we are excited to highlight 10 use cases where Azure AI is utilized within Microsoft and beyond. Speech transcription and captioning in Microsoft Teams is powered by Azure Cognitive Services for Speech. Microsoft achieved human parity in conversational speech recognition when it reached an error rate of 5.9 percent.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Countless hours of organized effort are required to bring a model to the production stage. The efforts which were spent on all the previous steps would turn out to be fruitful only if the model is successfully deployed.

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals.

A fundamental shift is currently taking place in how AI applications are built and deployed. AI applications are becoming more sophisticated and applied to broader use cases. This requires end-to-end AI lifecycle management--from data preparation, to model development and training, to deployment and management of AI apps. This approach can lower upfront costs, improve scalability, and decrease risk for customers using AI applications. While the cloud-native approach to app development can be appealing to developers, machine learning (ML) projects are notoriously time-intensive and cost-intensive, as they require a team with a varied skill set to build and maintain.

In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads in the cloud. This is the fourth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurec ertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam.

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the DP-100 Azure Data Scientist Associate certification exam. You will refresh your knowledge of how to plan and create a suitable working environment for data science workloads on Azure, run data experiments, and train predictive models. In addition, you will recap on how to manage, optimize, and deploy machine learning models into production. You will test your knowledge in a practice exam mapped to all the main topics covered in the DP-100 exam, ensuring you're well prepared for certification success.

Data scientists and model evaluators – At training time to help them to understand their model predictions and assess the fairness of their AI systems, enhancing their ability to debug and improve models. Model performance tab: With the predefined female and male cohorts, we can observe the different prediction distributions between males and female cohorts, with females experiencing higher probability rates of being rejected for a loan. We sort our top feature importances by the Female cohort, which indicates that while the feature for "Sex" is the second most important feature to contribute towards the model's predictions for individuals in the female cohort, they do not influence how the model makes predictions for individuals in the male cohort. The dependence plot for the feature "Sex" also shows that only the female group has positive feature importance towards the prediction of being rejected for a loan, whereas the model does not look at the feature "Sex" for males when making predictions. The original fairness dashboard also enables the comparison of multiple models, such as the models produced by different learning algorithms and different mitigation approaches.

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.