Machine learning (ML) is gaining momentum across a number of industries and scenarios as enterprises look to drive innovation, increase efficiency, and reduce costs. Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. At Microsoft Build 2020, we announced several advances to Azure Machine Learning across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML. New enhancements provide ML access for all skills. Data scientists and developers can now access an enhanced notebook editor directly inside Azure Machine Learning studio.

I have been asked many times during the past month whether the heightened pressure that enterprises are now facing as a result of the Covid-19 pandemic will cause them to short-cut aspects such as responsible machine learning in order to get pilots into production more quickly. This is certainly a possibility, but in my opinion, people's memories of the actions that enterprises are taking now will run much deeper than many of the better-planned projects that came before the pandemic or have yet to start. More organisations will therefore aim to get artificial intelligence (AI) right during the crisis as well. As practitioners get going in this area, here are a few things to consider. One global bank I spoke to recently has just put in place a policy that no AI model can move into production without some interpretability and bias controls built into the lifecycle of the application.

This week Microsoft hosted its annual Build conference completely online. That meant streaming keynotes, panels, digital breakouts and workshops for developers. The company's investments in cloud and machine learning are starting to deliver real products. Businesses continue to be the main target of its software offerings -- things like Outlook, Office, Teams and SharePoint. Microsoft has been working to make its 365 services more powerful.

Yesterday, Microsoft's Build 2020 developer conference kicked off (remotely), and we saw the first results of Microsoft's billion-dollar investment in OpenAI, a company co-founded by Elon Musk. Microsoft announced it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research focuses on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

Last year, Microsoft invested $1 billion in Open AI, a non-profit co-founded by Elon Musk that focuses on the development of human-friendly artificial intelligence. Microsoft announced that it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research is focused on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.