As artificial intelligence (AI) becomes more prevalent and every industry races to develop AI solutions to advance their use cases, practical challenges have arisen around production deployment. In my previous blog post [1], I described a process for taking machine learning (ML) experiments to production deployments. In this follow up post, I outline seven AI industry trends that help users simplify and scale the overall ML lifecycle. We describe each trend, discuss why it matters for operational ML and what factors should be considered as a business decides to exploit a trend to accelerate or improve their operational ML practice. Figure 1 shows a typical machine learning (ML) lifecycle.

Put together, your MLOps should have all the elements in Figure 2 above, with all elements working together to form a cohesive whole for a successful ML operation.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value. One company found the approach so successful that a manager said it was like "printing money"--a reliable, production-based approach to generating revenue.

It provides an agnostic introduction to operational ML with open source and cloud platforms. It is the first ML workshop to go all the way from data preparation to the integration of predictive models in real-world applications and their deployment in production. Participants will learn to use Python open source libraries scikit-learn, Pandas and SKLL, and cloud platforms Microsoft Azure ML, Amazon ML, BigML and Indico (along with their APIs).

The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles. Each workshop is 2 day long and comprises 8 modules of 3 blocks of 30' each--including time for questions. Blocks are either Theory or Exercise, with at least one Exercise per module. The goal is to make you operational with machine learning at the end of the workshop.

Machine learning (ML) is the unsung hero that powers many applications, systems, sensors, devices, and products. Machine learning is so pervasive that we can often assume its presence in most of the applications and systems without having to specifically call it out. In simple terms, machine learning is a computer's ability to learn from data, and it is one of the most useful tools we have to develop intelligent systems and applications. Machine learning is used widely today for all kinds of tasks, from churn prediction in large companies, to web search, to medical diagnostics, to robotics. It's hard to find a field that cannot benefit from machine learning in one way or another.