Coming to grips with machine learning needn't require vast amounts of labeled data, a team of data scientists, and a lot of compute time. The state of the art in modern artificial intelligence has reached a point where there are now models that are sufficiently general purpose (within their own domains, of course) that they can be dropped into your applications without additional training and customization. We've seen some of this with the evolution from Project Adam to Azure Cognitive Services. Now Microsoft is taking the next step, using that foundation to deliver a set of machine learning models that provide assistance with common tasks: Azure Applied AI Services. We've already seen some of this with the Power Platform's new document automation tool in Power Automate.

Google today announced the beta launch of Cloud AI Platform Pipelines, a service designed to deploy robust, repeatable AI pipelines along with monitoring, auditing, version tracking, and reproducibility in the cloud. Google's pitching it as a way to deliver an "easy to install" secure execution environment for machine learning workflows, which could reduce the amount of time enterprises spend bringing products to production. "When you're just prototyping a machine learning model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make a [machine learning] workflow sustainable and scalable, things become more complex," wrote Google product manager Anusha Ramesh and staff developer advocate Amy Unruh in a blog post. "A machine learning workflow can involve many steps with dependencies on each other, from data preparation and analysis, to training, to evaluation, to deployment, and more. It's hard to compose and track these processes in an ad-hoc manner -- for example, in a set of notebooks or scripts -- and things like auditing and reproducibility become increasingly problematic."

You don't see a startup get a $50 million seed round all that often, but such was the case with Vianai, an early-stage startup launched by Vishal Sikka, former Infosys managing director and SAP executive. The company launched recently with a big check and a vision to transform machine learning. Just this week, the startup had a coming out party at Oracle Open World, where Sikka delivered one of the keynotes and demoed the product for attendees. Over the last couple of years, since he left Infosys, Sikka has been thinking about the impact of AI and machine learning on society and the way it is being delivered today. He didn't much like what he saw.

Ahead of its Build conference, Microsoft today released a slew of new machine learning products and tweaks to some of its existing services. These range from no-code tools to hosted notebooks, with a number of new APIs and other services in-between. The core theme, here, though, is that Microsoft is continuing its strategy of democratizing access to AI. Ahead of the release, I sat down with Microsoft's Eric Boyd, the company's corporate vice president of its AI platform, to discuss Microsoft's take on this space, where it competes heavily with the likes of Google and AWS, as well as numerous, often more specialized startups. And to some degree, the actual machine learning technologies have become table stakes. Everybody now offers pre-trained models, open-source tools and the platforms to train, build and deploy models.

Text (word) analysis and tokenized text modeling always give a chill air around ears, specially when you are new to machine learning. Thanks to Python and its extended libraries for its warm support around text analytics and machine learning. Scikit-learn is a savior and excellent support in text processing when you also understand some of the concept like "Bag of word", "Clustering" and "vectorization". Vectorization is must-to-know technique for all machine leaning learners, text miner and algorithm implementor. I personally consider it as a revolution in the analytical calculations.

Text (word) analysis and tokenized text modeling always give a chill air around ears, specially when you are new to machine learning. Thanks to Python and its extended libraries for its warm support around text analytics and machine learning. Scikit-learn is a savior and excellent support in text processing when you also understand some of the concept like "Bag of word", "Clustering" and "vectorization". Vectorization is must-to-know technique for all machine leaning learners, text miner and algorithm implementor. I personally consider it as a revolution in the analytical calculations.