Personal Guide on AWS Machine Learning Roadmap
As a Machine Learning (ML) Engineer, you develop systems where algorithms can learn and improve themselves by analyzing and interpreting enormous volumes of data. You use publicly available implementation in TensorFlow, Pytorch, Keras, or Theano usually (but not always) in the cloud. Machine Learning Engineering is considered a top rank career in the job market with a 344% market growth and an average base salary range from $80,000 to $165,000.
Even the market is still new and rapidly evolving, AWS AI Solutions emerges the leader for AI Engineering Services, surpassing Google, IBM and Microsoft. AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure. There are plethora of Machine Learning resources trainings and certification programs. You should focus on Amazon Web Services (AWS) Training and Certification because there are battle-tested and trusted and that is what the market has vouched for. You will need to ask and learn about the 5 W’s for building such skills:
🔹Why is building machine learning skills a golden opportunity for developers and data scientists?
🔹Where is the best place to develop machine learning skills?
🔹What skills do developers and data scientists need for machine learning?
🔹Who are the Machine Learning Heroes?
🔹When should developers and data scientists get started?
How can you build a formidable AWS Machine Learning Career?
1. Join AWS Community Builders
The AWS Machine Learning (ML) community is a vibrant group of ML developers who evangelize AWS AI Services, AWS ML Services, and AWS AI Devices by showcasing their work through blogs, online events, reaching like-minded developers, and building scalable communities.