For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.
ODMs can choose to harden the platform through Hardware Security Modules (HSM). Microsoft has made it easy to run machine learning models at the edge. Each model responsible for inferencing can be packaged and deployed as a standard module. Developers can train their models on Azure through Data Science VMs or Azure ML Studio. Azure IoT Edge also supports running models exported from Azure's AutoML services such as custom vision. Since each model is just a container/module, new models can be quickly pushed to the edge. With Microsoft's investment in ONNX, ML models built using different frameworks may be exported to a standard format before using them for inference. Azure IoT Edge plays a crucial role in Microsoft's vision of delivering Intelligent Cloud and Intelligent Edge.
Microsoft is embedding Anaconda's Python distribution into its Azure Machine Learning products, the latest move by the software vendor to expand its capabilities in the fast-growing artificial intelligence space and an example of Anaconda extending its reach beyond high performance computing and into AI. The two companies announced the partnership this week at the Strata Data Conference in New York City, with the news dovetailing with other announcements around AI that Microsoft officials made this week at its own Ignite 2017 show. The vendors said they will offer Anaconda for Microsoft, which they described as a subset of the Anaconda Python distribution that is now available on Windows as well as MacOS and Linux. There will be a range of options within the Anaconda for Microsoft offering, they said. Initially, Anaconda for Microsoft will be included in Microsoft's Azure Machine Learning, Machine Learning Server, Visual Studio, and SQL Server, but there also will be additional advantages to developers.
Software developers are quickly adopting Artificial Intelligence (AI) technologies, such as natural language understanding, sentiment analysis, speech recognition, image understanding and machine learning (ML). Across a broad range of industries and sectors, AI-infused software applications and cloud services drive innovative customer experiences, augment human capabilities and transform how we live, work and play. New tools, cloud-hosted APIs and platforms make it even easier to build such applications. Modern AI applications live at the intersection of cloud computing, data platforms and AI tools. The cloud provides a powerful foundation for elastic compute and storage, while supporting special-purpose hardware such as graphics processing units (GPUs) that accelerate demanding calculations.
Microsoft CEO Satya Nadella keeps saying that Microsoft's Azure cloud platform makes it easier for firms to exploit machine learning (ML). But how far is this marketing message borne out by the services available on Azure? Azure's suite of machine-learning offerings is fairly comprehensive, targeting everything from companies seeking simple, on-demand services through to those looking to train their own models using in-house data scientists. Every platform-as-a-service (PaaS) machine learning-related product and service that Microsoft offers is part of the Cortana Intelligence Suite. This bundles Microsoft's analytics and ML-focused offerings with Microsoft cloud-based data stores, capable of holding the vast amount of data needed to train machine learning models.