After ChatGPT took off, the AI/ML market suddenly became attractive to everyone. But is it that easy to kickstart a project? More importantly, what do you need to scale an AI initiative? MLOps or machine learning operations is the answer when it comes to automating machine learning workflows.
Adopting MLOps is like adopting DevOps, you need to embrace a different mindset and way of working. However, the return on investment that this kind of initiative generates is worth the effort. When looking at the big picture, there are two key aspects to consider. On one hand, MLOps is a practice that is relatively new, so it is completely normal to face challenges along the way. On the other hand, MLOps is evolving very fast, so solutions are appearing every day. What do companies typically struggle with and how can they overcome the most common MLOps challenges? We’ll dig into these questions in this blog.
MLOps Challenge #1: Lack of talent
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