Cloud ML:

The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale. The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.

The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science. AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

• The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.
• The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
• The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
• AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

Cloud AI:

The AI cloud, a concept only now starting to be implemented by enterprises, combines artificial intelligence (AI) with cloud computing. An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.

Artificial intelligence (AI) assists in the automation of routine activities within IT infrastructure, which increases productivity. The combination of AI and cloud computing results in an extensive network capable of holding massive volumes of data while continuously learning and improving.

• Data Mining.
• Agile Development.
• Reshaping of IT Infrastructure.
• Seamless Data Access.
• Analytics and Prediction.
• Cloud Security Automation.
• Cost-Effective.

Cloud ML	Cloud AI
The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale.	An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.
The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.	Enterprises use the power of AI-driven cloud computing to be more efficient, strategic, and insight-driven. AI can automate complex and repetitive tasks to boost productivity, as well as perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows.
The pay-per-use model further makes it easy to access more sophisticated capabilities without the need to bring in new advanced hardware.	Cloud AI Platform is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
This storage service provides petabytes of capacity with a maximum unit size of 10 MB per cell and 100 MB per row. 1024 Petabytes of data.	1024 Petabytes of data. The larger the RAM the higher the amount of data it can handle hence faster processing. 16GB RAM and above is recommended for most deep learning tasks.
High Flexibility and Cost Effective.	Seamless Data Access. High Flexibility and Cost Effective.
Cloud ML Engine is used to train machine learning models in TensorFlow and other Python ML libraries (such as scikit-learn) without having to manage any infrastructure.	In Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the data from past decisions.
Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.	Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.
The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.	The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
Google, Amazon, Microsoft, and IBM	Google, Amazon, Microsoft, and IBM
ML’s aim is to improve accuracy without caring for success.	The goal of AI is to increase the chances of success.
ML is the way for the computer program to learn from experience.	AI is a computer program doing smart work.
The ML’s goal is to keep learning from data to maximize the performance.	The future goal of AI is to stimulate intelligence for solving highly complex programs.
ML allows the computer to learn new things from the available information.	AI involves decision-making.
ML looks for the only solution.	AI looks for optimal solutions.

ML and AI:

Even though many differences exist between ML and AI, they are closely connected. AI and ML are often viewed as the body and the brain. The body collects information, the brain processes it. The same is with AI, which accumulates information while ML processes it.

Conclusion:

AI involves a computer executing a task a human could do. Machine learning involves the computer learning from its experience and making decisions based on the information. While the two approaches are different, they are often used together to achieve many goals in different industries.