In this series of posts, we'll cover how Gretel's synthetic data platform helps you overcome challenges across the data-centric machine learning life cycle to help you successfully build, deploy, maintain, and realize value from your AI projects. The life cycle outlined below is a common framework or workflow process for building machine learning and AI solutions. It's focused on streamlining the stages necessary to develop machine learning models, deploy them to production, and maintain and monitor them. These steps are a collaborative process, often involving data scientists and DevOps engineers. The process below was inspired by the value chains created by The Sequence, Databricks, Google Cloud, and Microsoft.

An application with 95 percent accuracy may not behave much more differently than one with 96 percent accuracy. They are too expensive to train, too big to fit onto consumer devices, and too slow to be useful to users. In the research phase, you often do not care about the size of the model – but in real life you do. On what factors do you choose the baseline and how do you quantify it? Most of the time, in production, they are only useful if their performance is unquestionably superior.

It's time for every product manager, entrepreneur or business leader to get up to speed on machine learning. Even if you're not building the next chatbot or self driving car, you'll probably need to use machine learning in your product sooner rather than later to stay competitive. The good news is you don't need to invent the technology (though kudos if you do), just leverage what already exists. Tech companies have open sourced tools and platforms (Amazon AI, TensorFlow, originally developed by Google, and many others) that make machine learning accessible to virtually any company today. When I started in machine learning I knew next to nothing about it, yet in a relatively short time I was leading the development of products with machine learning at their very core (such as this).

Seattle is poised to be an epicenter for machine learning and artificial intelligence. That's one takeaway from the inaugural Machine Learning / Artificial Intelligence Summit hosted by Madrona Venture Group on Wednesday in downtown Seattle. Thanks to Amazon Web Services, Microsoft Azure and a wide range of startups, the Emerald City is already known as a hub for cloud computing technology development, with Madrona Managing Director Matt McIllwain calling Seattle the "cloud capital of the world" more than two years ago. There is also a long list of Silicon Valley tech companies who have established engineering outposts in the Seattle area, including Google, Facebook, Oracle, Apple, HP, Uber, Lyft, Twitter, Splunk, and many others. But looking beyond the infrastructure and services side of cloud computing, there is also momentum building from both tech giants and small startups in Seattle that are developing technologies related to machine learning, artificial intelligence, natural language processing, algorithms, data analytics, and more.