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"The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations.

Machine learning is everywhere you look, affecting many technologies and products that we use on a daily basis. But who are the product managers leading these products? Who is ensuring that the success metrics are set correctly and ethically? Who is responsible for accurate messaging around such products? Let's go several years back, and look at the Product Manager's role.

This is what makes them dangerous.' Previously, I talked about how AI could change the human-machine relationship. Who should be making the decisions in an autonomous car? Should humans always be able to overrule robot decisions? What if you only have a split second to react?

Part I: Why is managing machine learning products so hard? And why should you care? In my previous article, I talked about the biggest difference that Machine Learning (ML) brings: ML enables a move away from having to program the machine to true autonomy (self-learned). Machines make predictions and improve insights based on patterns they identify in data without humans explicitly telling them what to do. That's why ML is particularly useful for challenging problems that are difficult for people to explain to machines.

As a project manager, you've probably engaged in a number of IT projects throughout your career, spanning complex monolithic structures to SaaS web apps. However, with the advancement of artificial intelligence and machine learning, new projects with different requirements and problems are coming onto the horizon at a rapid speed. With the rise of these technologies, it is becoming less of a "nice to have" and instead essential for technical project managers to have a healthy relationship with these concepts. According to Gartner, by 2020, AI will generate 2.3 million jobs, exceeding the 1.8 million that it will remove--generating $2.9 trillion in business value by 2021. Google's CEO goes so far as to say that "AI is one of the most important things humanity is working on. It is more profound than […] electricity or fire." With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, technical PMs who can grasp this opportunity must understand how AI project management is distinct and how they can best prepare for the changing landscape. Before going deeper, it's important to have a solid understanding of what AI really is. With many different terms often used interchangeably, let's dive into the most common definitions first.

I've spent the last few years applying data science in different aspects of business. Some use cases are internal machine learning (ML) tools, analytics reports, data pipelines, prediction APIs, and more recently, end-to-end ML products. I've had my fair share of successful and unsuccessful ML products. There are even reports of ML product horror stories where the developed solutions ended up failing to address the problems they were supposed to solve. To a large extent, the gap can be filled by properly managing ML products to ensure that it ends up being actually useful to users. Given the difficulties in the ML workflow and our resource constraints (e.g. In this blog post I aim to give an overview of each of these steps, while illustrating some of the foreseeable challenges and the frameworks that I've found to be useful in optimizing the ML workflow.

The field of Machine Learning (ML) has been consistently evolving since Data Science started gaining traction in 2012. However, I believe 2018 was a critical inflection point in the ML industry. After helping Insight Fellows build dozens of ML products to get roles on applied ML teams, and reading through both corporate and academic published research and, I've seen more need for engineering skills than ever before. As a field that has consistently toed the line between its origins in academic research and the need to serve customer needs, it has often been hard to reconcile engineering standards with ML models. As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018's foundation and truly blossom in 2019.

A recent survey revealed that 84% of data pros have used at least one ML framework in the last 5 years while 51% of data pros have used at least one ML product in the last 5 years. The most popular ML frameworks include Scikit-Learn, Tensorflow and Keras. The most popular ML products include SAS, Cloudera and Azure. Machine Learning Frameworks used in last 5 years. The practice of data science requires the use of machine learning products and frameworks to help data professionals automate processes that drive their business forward.

The field of Machine Learning (ML) has been consistently evolving since Data Science started gaining traction in 2012. However, I believe 2018 was a critical inflection point in the ML industry. After helping Insight Fellows build dozens of ML products to get roles on applied ML teams, and reading through both corporate and academic published research and, I've seen more need for engineering skills than ever before. As a field that has consistently toed the line between its origins in academic research and the need to serve customer needs, it has often been hard to reconcile engineering standards with ML models. As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018's foundation and truly blossom in 2019.