To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game. What's transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic "you can only have two of cost, quality, or speed" triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What's truly transformative is that: There hasn't been a technology this revolutionary for gaming since real-time 3D. Spend any time at all talking to game creators, and the sense of excitement and wonder is palpable. So where is this technology going? And how will it transform gaming?

When you say'MLOps', what do you mean? As the technology ecosystem around ML evolves, 'MLOps' now seems to have (at least) two very different meanings: The typical journey of an organization with a data science use case and a small team is to start from what they perceive to be the logical beginning: building AI models. A business idea based on data science is selected, and budget is allocated for the data scientists to start the work of building and training machine or deep learning models. They get access to data extractions, search for patterns, and build models that work in the lab. For veterans of this space, it's remarkable to observe how the industry has changed.

"I was a happy data scientist until we decided it was time for deploying our models." It is common among many DS/ML teams that when the time for productionizing the model comes, they are caught off guard due to poor planning. Of course, thinking solely about the end is far from enough, the stages beforehand are equally as important. To reach the end of any endeavor we need to be strategic, the same applies for succeeding with MLOps. One such strategy is to take on a less challenging problem or part of it in the beginning and find the easiest way it can be solved.

The primary mission of DevOps is to help the teams to resolve various Tech Ops infrastructure, tools and pipeline issues. At the other hand, as mentioned in the conceptual review by Forbes in November 2016, the industrial analytics is no more going to be driven by data scientists alone. It requires an investment in DevOps skills, practices and supporting technology to move analytics out of the lab and into the business. There are even voices calling Data Scientists to concentrate on agile methodology and DevOps if they like to retain their jobs in business in the long run. The eternal dream of almost every Data Scientist today is to spend all (well, almost all) the time in the office exploring new datasets, engineering decisive new features, inventing and validating cool new algorithms and strategies.