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Artificial intelligence (AI) and machine learning (ML) are not recent concepts. Similarly, the use of the cloud for AI/ML workloads is not particularly recent; For example, Amazon SageMaker was launched in 2017. However, with the current buzz around Generative Artificial Intelligence (GenAI), there has been a renewed focus on services that leverage AI in its various forms.
GenAI has been getting a lot of attention recently, and rightly so. It has enormous potential to change the way corporations and their employees operate. A statesman research published in 2023 found that 35% of people in the tech industry used GenAI to help with work-related tasks.
There are use cases that might be applied to almost any industry. The use of GenAI-based tools is not limited to tech-savvy people. Leveraging the cloud for these tools reduces the barrier to entry and accelerates potential innovation.
Understanding the basics
AI, ML, deep learning (DL) and GenAI? So many terms – what’s the difference?
Artificial intelligence might be turned into a computer program designed to mimic human intelligence. It doesn’t have to be complicated; it will possibly be so simple as an if/else statement or a decision tree. ML goes a step further by building models that use algorithms to learn from patterns in data without direct programming.
DL models try to replicate the same structure of the human brain, which is made up of many layers of neurons, and are great for identifying complex patterns equivalent to hierarchical relationships. GenAI is a subset of DL and is characterised by its ability to generate recent content based on patterns learned from huge datasets.
As these methods turn out to be more efficient, additionally they turn out to be more complex. Greater complexity brings greater computational and data requirements. This is where cloud offerings turn out to be invaluable.
Cloud offerings generally fall into one of three categories: infrastructure, platforms, and managed services. They may also be found as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
IaaS offerings offer you complete control over the way you train, deploy, and monitor your AI solutions. This level typically involves writing custom code and requires experience in data science.
PaaS offerings proceed to provide reasonable control and enable the use of AI without requiring detailed understanding. Examples in this space include services like Amazon Bedrock.
SaaS offerings typically solve a specific problem using AI without revealing the underlying technology. Examples include Amazon Rekognition for image recognition, Amazon Q Developer for improving software engineering efficiency, or Amazon Comprehend for natural language processing.
Practical applications
Companies around the world have been using artificial intelligence for years, if not many years. To illustrate the diversity of use cases across industries, take a look at these three examples from The path of the law, Be careful AND Nasdaq.
Challenges and reflections
While there are plenty of opportunities, harnessing the power of artificial intelligence and machine learning comes with some challenges. There is a lot of industry commentary around ethics and responsible AI – it is vital to consider these properly when moving an AI solution into production.
Generally speaking, as AI solutions turn out to be more complex, their explainability decreases. This implies that it becomes increasingly difficult for a business to understand why a given input results in a given output. This is more problematic in some industries than others – something to keep in mind when planning your use of AI. Appropriate levels of explainability are a large part of the responsible use of AI.
It is equally vital to consider the ethics of artificial intelligence. When does it not make sense to use AI? A superb rule of thumb is to consider whether the decisions your model makes can be unethical or immoral if the same decision were made by a human. For example, if the model rejected all loans to applicants that had a certain characteristic, it might be considered unethical.
Getting began
So where should corporations start implementing AI/ML in the cloud? We covered the basics, some examples of how other organizations have applied AI to their problems, and touched on the challenges and considerations of supporting AI.
The start line of any company’s roadmap to successfully implementing AI is opportunity identification. Look for areas of the business where repetitive tasks are performed, especially those involving decision-making tasks based on data interpretation. Additionally, look at areas where people perform manual evaluation or text generation.
Once opportunities are identified, goals and success criteria might be defined. These have to be clear and make it easy to quantify whether the use of AI is responsible and useful.
Only once this is defined can construction begin. Start small and prove the concept. Of the solutions listed, those at the SaaS and PaaS end of the spectrum will get you up and running faster due to their shorter learning curve. However, there might be some more complex use cases where more control might be required.
When assessing the success of a PoC exercise, be critical and do not look at it through rose-colored glasses. Regardless of whether you, your management or your investors might want to use AI, if it is not the right tool for the job, it is best not to use it. Some people tout GenAI as a silver bullet that can solve all problems – this is not the case. It has enormous potential and will disrupt many industries, but it is not a solution for all the things.
After a successful assessment, it is time to operationalize the capabilities. Here, think about features equivalent to monitoring and observability. How do you make sure your solution doesn’t produce bad predictions? What if the characteristics of the data used to train a machine learning model now not represent the real world? Building and training an AI solution is only half the story.
Artificial intelligence and machine learning are established technologies that are here to stay. Harnessing them using the power of the cloud will define future businesses.
GenAI is at its peak and the best use cases will soon emerge from this madness. To find these use cases, organizations need to think innovatively and experiment.
Learn from this text, discover opportunities, prove feasibility, and then put them into motion. There is significant value to be realized, but it requires due care and attention.