“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.” ~Mark Cuban Â
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. Let’s figure out why it’s a hot topic and why it’s crucial to manage and collaborate while running ML experiments.
If you think that it would be better to jump into code and work without common things like planning, defining project goals and conditions at your documentation -> please don't do this.
Quite often you will end up wasting time by delaying discussions surrounding the project goals and model evaluation criteria. All team members should be working toward a common goal from the start of the project and have access to all pool of knowledge fast. Â
Machine learning projects are highly iterative; as you progress through the ML lifecycle, you’ll find yourself iterating on a section until reaching a satisfactory level of performance, then proceed forward to the next task (which may be circling back to an even earlier step).Â
Moreover, a project isn’t complete after you ship the first version; you get feedback from real-world interactions and redefine the goals for the next iteration of deployment. Â
An important part of working at ML projects is related to data preparation. The better data you have - the better results you'll get.
Preparing and cleaning data is something that will become more widespread in the project process. This action is often the most labor-intensive part of organizing an AI system, as most firms do not have the data ready in the correct formats—thus, it may take a while for data analysts to perform this essential action.
The dataset should be continuously updated with the new data. Access to different datasets might be the main crucial factor defining which ML product is most successful. It’s critical to stay up to date on this and reach the best possible performance for your ML project, even post-launch.
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