Lead the GenAI revolution. Future-proof your skills by understanding AI basics, machine learning, algorithms, ethical practices, and real-world applications. Stay ahead in tech leadership.

In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.
 
Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.
 
You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You'll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Grokking AI for Engineering & Product Managers

# Machine learning as a service (MLaaS)

As we learned in the previous lesson, machine learning as a service (MLaaS) refers to **cloud-based platforms that solve infrastructure needs** like data pre-processing, model training, and model evaluation. They allow businesses to easily consume results of predictions on new data by calling REST APIs and to gain valuable insights from the data via predictive analytics.

**Amazon Machine Learning services, Azure Machine Learning and Google Cloud AI** are the top three cloud MLaaS services. This means that you can basically enjoy the **benefits** of AI by simply training your existing developers and skip hiring expensive AI talent. For example, **Azure Machine Learning** provides even a very **simple drag-and-drop interface for developing models**, without much expertise in the field, as shown in the image below.

First, let's get an overview of what the MLaaS platforms by Amazon, Google, and Microsoft give us, and then let's look at their machine learning APIs comparatively. 

This lesson discusses the top cloud services for creating AI solutions. 

The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

AI Infrastructure: Cloud Services to Create AI Solutions II

Machine learning as a service (MLaaS)