Keys to Successful Machine Learning in IoT
Answer the questions in this outline & you will have a clear understanding of the key elements for a successful Machine Learning project.
Learning the key elements for a successful machine learning project can be daunting. There is a lot of confusion about Machine Learning and Artificial Intelligence today. It’s not magic, it’s another form of statistics, computer science, and development that is powerful but can be easily misunderstood. What used to make Machine Learning a very difficult and confusing space to be in, has dramatically changed for the better. So what does successful Machine Learning look likeand how have things changed?
A few short years ago it could take at least a week to get an ML environment set up before you could even write the first line of code. But now, with the advent of high-quality, ready-made products on the market, you set up the environment and begin coding within a matter of minutes! With all the work that has been done on the infrastructure, more types of ML workflows are going to become low-code to no-code options. Developers will be able to use products ‘off the shelf’ without having to do any training or coding to get them up and running. Looking towards the future we may one day see ML becoming as simple as plug-n-play!
Building an ML model takes a clear understanding of the possibilities as well as risks. But, what are the key elements you need in an ML project? In this whitepaper, we outline some of the most important items needed to build a successful ML project.
What should you consider and why or why not? Of course, there are many variables but the basic building blocks still remain in any project. What to look for in an outline and what to avoid. How do you know if your project is ready? That depends… but, if you answer the questions we outline in the readiness assessment, you will have a clear understanding of where you are on this path.
