The Five Major Platforms For Machine Learning Model Development Posted on : Dec 14 - 2020

Over the past two decades, the biggest evolution of Artificial Intelligence has been the maturation of deep learning as an approach for machine learning, the expansion of big data and the knowledge of how to effectively manage big data systems, and affordable and accessible compute power that can handle some of the most challenging machine learning model development. Today’s data scientists and machine learning engineers now have a wide range of choices for how they build models to address the various patterns of AI for their particular needs.

However, The diversity in options is actually part of the challenge for those looking to build machine learning models. There are just too many choices. This, compounded by the fact that there are different ways you can go about developing a machine learning model, is the issue that many AI software vendors do a particularly poor job of explaining what their products actually do. Marketing skills and websites that actually explain what the product and/or company actually does are clearly lacking for many AI vendors. This makes it difficult for those looking to implement machine learning models to choose the best vendor option for them.

Untangling the complexity of machine learning model development

Machine learning is the cornerstone of AI. Without a way for systems to learn from experience and example, they aren’t able to achieve higher order cognitive tasks that require learning patterns from data. Without machine learning, machines need instructions and rules programmed and developed by humans to tell them what to do which isn’t intelligence - that’s just programming.

There are three primary approaches to machine learning: supervised learning in which machines learn from human-tagged examples, unsupervised learning in which machines discover patterns in the data, and reinforcement learning where machines learn from trial-and-error with a reward-based system. Each of these approaches are applicable and appropriate for different learning scenarios.

Furthermore, there are a wide range of algorithms that machine learning practitioners can use to implement those various learning approaches. These algorithms have different tradeoffs and performance characteristics. In addition, the end result of training a particular algorithm on particular training data is a machine learning model. The model represents what the machine has learned for a particular task. People seem to often confuse the machine learning algorithm, which tells machines the approach they should use to encode learning, and the machine learning model, which is the outcome of that learning. New algorithms are not frequently developed as new approaches to learning are few and far between. New models, however, are developed all the time since each new learning is encoded in a model, which can happen an infinite amount of times. View More