AWS Machine Learning & AI Consulting Services

You can think of AI as being the broadest topic, which contains the others, because it is generally about making computers perform tasks that require human-like thinking. AI can be a lot of things by that definition. Machine learning is a subset of AI, in which you train models using data to optimize for a specific outcome. This is why ML models often have a predictive element to their solution. Deep learning is itself a subset of ML and uses neural networks with many layers to analyze various factors of data with the aim of improving performance on a given task.

It depends. There are a lot of factors, including the scale of your solution, the instance type on which it will best perform, how long you plan to run the solution, and what the supporting data architecture will be. In general, the most reliable way to estimate a cost is to work with an AWS-certified Solutions Architect to work on scoping out the size of what you want accomplished and the most efficient way to engineer it. Since we know this is often on customers’ minds, we offer a free hour with our Data, Analytics, and Machine Learning team to do just that, walking through each part of your solution and even working backward, if necessary, from your desired business outcomes.

In terms of collection, you want to find data that’s representative of the real-world scenarios where your ML model will be applied. If your data isn’t really relevant to your domain or representative of its characteristics, you may optimize a model that cannot handle the real-world task. You’ll also need to prepare the data, using services like AWS Data Wrangler, or building a full ETL pipeline through services like AWS Glue. Are you doing supervised or unsupervised training? Supervised training will require labeled data, which can represent its own challenge—consider AWS SageMaker Ground Truth for this. And when you get to engineering your models, consider how you will select features, validate results, experiment, iterate, and reproduce as you train a model. ML engineering is an iterative process, often requiring you to test and adjust frequently until you arrive at a solution.

First, do you have sufficient quality data to train an algorithm? One of the hardest problems can simply be having the scale of data that makes for accurate training. Also, if your problem will shift according to environmental conditions, like macroeconomics, for example, this can be a better fit for ML’s adaptability than other ways of solving. In general, prediction-oriented problems with a large historical data set are a good fit. And problems which involve hard-to-identify patterns are where ML shines, by detecting a pattern that would otherwise be difficult to pin down with other analytic methods.

It depends on your business but there are many well-established use cases for machine learning. Identifying consumer sentiment, trend analysis and forecasting, recommendation engines, fraud detection, healthcare diagnostics, manufacturing monitoring—all of these have seen considerable advances from machine learning solutions. A healthy dose of skepticism is warranted—the media often has trouble distinguishing between real use cases and companies looking to brand themselves—but if your problem has a strong fit for ML techniques there can be large upside to a well-architected solution.

This depends on your problem, goals, and the availability of data for your solution. Generative AI is strong for creating novel content, like prototyping, or the synthesis of several data sources, or even a simulation of some real world phenomena. Machine learning is more narrowly about solving problems by absorbing and iterating over a large amount of data for solutions that need to predict outcomes, identify patterns, or adapt to a shifting environment. Traditional AI is rule-based and best when the problem has clearly definable logic but presents too many variables for other programmatic solutions.