Google Cloud’s AI Adoption Framework: Building a Transformative AI Capability

It is well known that AI is one of the 2020's technology and for the years to come too.

AI can help startups, companies, and large organizations improve, scale, and automate decision-making. On the other hand, Machine Learning can create new opportunities so that you can develop new revenue streams to make your business grow.  

AI and ML go hand by hand. If you learn how to combine both and make the best of each one of them, you will be able to develop the best way possible.  

Building an application with an effective AI capability can be challenging yet worthwhile. To achieve your objective you have to support building platforms and solutions. You also have to implement and operate systems, manage data correctly, and govern the required processes.

Questions

When building an AI-powered application, there are a couple of frequently asked questions that executives often ask.

• What skills should we prioritize and how should our teams be?
• What Machine Learning projects should be prioritized?
• How can we develop AI capacities responsibly? 

Engineering teams also have some frequently asked questions.

• How can we share data more efficiently?
• How can we utilize cloud services to scale?
• How can we operationalize data processing?

To answer all these questions Google has created the Google Cloud AI Adoption Framework. The whitepaper explains how this framework can help leverage the power of AI for transformational purposes.                           

The AI framework is built on four main areas: People, Processes, Technology, and Data. The interplay between all of these areas bring six themes that are needed for success. These are Lead, Learn, Access, Scale, Automate, and Secure. Following next, we will describe them all one by one.

• Lead is the extent of leadership that provides support and encouragement for data scientists and engineers to apply machine learning to business use cases. The degree to which they are cross-functional, collaborative and self-motivated.
• Learn is the quality and scale of the learning programs that upskill staff. Includes hiring talented people and increasing the data science and machine learning engineering staff.
• Access is recognizing data management as a key factor to enable AI. The degree to which data scientists and machine learning engineers can share, discover, and reuse data assets and other machine learning artifacts.
• Scale is how you can use cloud-native services to scale with big datasets and including a large number of data processing and machine learning jobs that reduce operational overheads.
• Secure is how you can classify and protect your sensitive data. You can also ensure that you implement AI responsibly.
• Automate is the ability to deploy, execute, and operate technology for data processing and machine learning pipelines in production efficiently, frequently, and reliably.

In conclusion, Google supports AI development by the Google Cloud AI adoption framework. Google has its focus on AI and every tech company too. It's the present and the future, and the most important thing when developing AI is to develop responsibly and consciously. AI has such a big capability and potential that if someone is not using it responsibly, it could harm users in a big way.

There are a couple of questions that executives do when developing AI, "What skills should we prioritize and how should our teams be?", "What Machine Learning projects should be prioritized?", "How can we develop AI capacities responsibly?". The questions are not to be answered openly, but they have very exact answers, the answer is found in the use of the AI Adoption Framework. The AI framework is built on four main areas: People, Processes, Technology, and Data. The interplay between all of these areas bring six themes that are needed for success. These are Lead, Learn, Access, Scale, Automate, and Secure.