If you are solving a problem using machine learning, no matter if the problem is a very small business problem or a super large one, the end-to-end machine learning life cycle will be the same. The internal steps at each life cycle stage could be more or less however there will always be 4 of the above stages in any machine learning life cycle.

The enterprise infrastructure team is facing a challenge. AI machine learning (ML) and deep learning (DL) are making a transition from tools just for consumer internet companies, to tools for mainstream enterprise organizations. This evolution calls for a new type of infrastructure and workflow that expanded beyond a single application. In the enterprise, infrastructure, IT & DevOps teams are seeing an increasing number of business groups adopt AI for product recommendations, forecasting, customer interactions, financial risk assessment, manufacturing defect detection, retail loss prevention, and more. For these AI applications, GPU-accelerated servers have a proven history to provide orders of magnitude higher performance than CPUs.

It's time to welcome the new year with a splash of machine learning sprinkled into our brand new resolutions. Machine learning will continue to be at the heart of what we do and how we do it. What a year it has been! The sheer amount of developments we saw in Natural Language Processing (NLP) blew us away. It was the year of fine-tuning language models and frameworks like Google's BERT and OpenAI's GPT-2 (more of all of this later!).

Whether you are a newcomer to machine learning, a newbie to specific algorithms or concepts, or a seasoned ML vet looking for a once-over of an algorithm you haven't seen or used in a while, these short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept. Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality. A wide array of clustering techniques are in use today.