Summary
Even though ML is not new, recent advancements in relatively cheap computing power have allowed many companies to start investing in it. This widespread availability of hardware comes with its own challenges. Often, teams do not put the focus on the big picture, and that may result in ML initiatives not delivering the value they promise.
In this chapter, we have discussed two common challenges that enterprises face while going through their ML journey. The challenges span from the technology adoption to the teams and how they collaborate. Being successful with your ML journey will require time, effort, and practice. Expect it to be more than just a technology change. It will require changing and improving the way you collaborate and use technology. Make your team autonomous and prepare it to adapt to changes, enable a fail-fast culture, invest in technology, and always keep an eye on the business outcome.
We have also discussed some of the important attributes of an E2E ML platform. We will talk about this topic in-depth in the later parts of this book.
In the next chapter, we will introduce an emerging concept in ML projects, ML operations (MLOps). Through this, the industry is trying to bring the benefits of software engineering practices to ML projects. Let's dig in.
