Artificial Intelligence (AI) is constantly present in our daily activities. For example, it is with the help of AI that we receive content recommendations on social networks and streaming apps, interact with chatbots and virtual assistants, and so on.
As a result, the trend is for more and more businesses to turn to AI solutions, both to manage internal processes and to enhance the services and/or products offered to customers.
According to IBM’s Global AI Adoption Index survey, 41% of companies in Brazil report using AI in their business operations, and 34% of companies report exploring the use of AI in other sectors.
However, developing corporate AI solutions involves a great deal of work, often requiring the ‘training’ of the application on specific data, typically through an approach called Machine Learning. What is involved in this process? How can Amazon SageMaker assist in this process? What are the advantages of this tool? Find out as you read!”
What is Machine Learning?
Machine Learning (ML) is a subfield within Artificial Intelligence that aims to train machines to interpret different types of data and make decisions based on it.
Therefore, the idea is to create technological systems that can perform actions independently, with as little human intervention as possible.
Just to illustrate, one of the cases where Machine Learning is used is in the development of autonomous cars, which navigate routes on their own, automatically detecting obstacles on the road.
So, the role of ML is to provide the appropriate training for machines to recognize patterns and perform specific tasks, simulating the behavior of the human brain.
To achieve this, learning models are developed. In simple terms, these are programs that follow specific rules and use data to recognize patterns or make predictions.
In this regard, the use of Machine Learning tools can streamline this process and make model creation more efficient. This is the case, for example, with Amazon SageMaker.
What is Amazon SageMaker?
Amazon SageMaker is a service offered and managed by AWS (Amazon Web Services) that enables the creation, training, and deployment of Machine Learning models.
In this way, the entire process of model development and enhancement becomes simpler, as the platform provides the complete infrastructure for working with machine learning.
Furthermore, SageMaker also supports various common Machine Learning tools, including many libraries and frameworks, such as:
- Hugging Face;
- Apache MXNet;
- among others.
Therefore, Amazon SageMaker is of great assistance in creating scalable ML models. In other words, the resources it provides enable companies to work efficiently even when dealing with large volumes of data and processing demands.
However, in practice, how does the application of SageMaker work in the routine of developing Machine Learning models? Check it out!
How does SageMaker work?
The operation of creating Machine Learning models in Amazon SageMaker can be divided into 3 stages: preparation, training, and deployment. Learn more details about what is involved in each of these stages.
Before starting to train an ML model, it’s important to prepare the data to be used. In this regard, SageMaker provides access to tools for data storage and processing.
Just to illustrate, you can use Jupyter Notebooks, which can be described as a kind of digital notebooks that allow you to write and execute code, as well as add explanations and create visualizations. Therefore, they are very helpful for data analysis and preprocessing.
Furthermore, through the platform, you can also use Amazon S3 for cloud storage of both raw and clean data without worrying about size limits.
After data preparation, it’s time to move on to training. In this stage, one of the tasks is selecting the Machine Learning algorithms that will be used in the model.
In this case, it’s worth noting that SageMaker offers a variety of ready-to-use algorithms optimized to make training much faster and more efficient. Among these, you can mention:
- Linear Learner: helps find solutions for classification and regression problems.
- Random Cut Forest (RCF): an unsupervised algorithm that aids in identifying anomalies in a dataset.
- K-Means: organizes data into groups based on similar or distinctive characteristics.
- And so on.
However, the platform is highly flexible and also allows you to create your own custom algorithms if needed.
Furthermore, before starting the training, it’s necessary to specify what the model’s hyperparameters will be. In other words, you need to define the characteristics of the settings that will control the training of the ML model.
In this regard, one of the features offered by SageMaker is Automatic Model Tuning (AMT), which automatically tests different model configurations, selecting the best set of hyperparameters based on performance.
Finally, after training, the model can be deployed to an Amazon SageMaker endpoint, making real-time predictions based on new input data.
It’s worth noting that the platform allows integration with other AWS services, such as Amazon EC2, making it possible to scale computational processing capacity as needed, ensuring high performance.
Additionally, through SageMaker, you have the ability to run A/B tests, comparing the performance and effectiveness between an old model and a new one in the face of different variations. This way, replacements are made with greater confidence.
What are the advantages of this tool?
Is it really worth using Amazon SageMaker in building ML models? In this regard, check out 4 advantages of using this tool!
1. Time optimization
First and foremost, Amazon SageMaker reduces the time required to complete the creation and deployment of solutions involving the use of Machine Learning. This is because the platform provides tools that allow much of the process to be automated.
As a result, teams can focus more on improving the quality of models and analyzing how to use them strategically to achieve the company’s objectives.
2. Cost reduction
Another benefit provided by SageMaker is cost reduction for ML projects. In this case, you will only pay for the resources you use, meaning the cost is proportional to your business demands.
Furthermore, it’s important to highlight that since it’s integrated with AWS, there’s no need to worry about setting up a complete physical infrastructure to work with it, as the service operates in the cloud. Not to mention that it’s a great alternative for those who are already using Amazon’s cloud services.
3. Multidisciplinary teams
Amazon SageMaker is a tool that makes it easier to involve professionals from various fields, such as business analysts, in the development of a Machine Learning project.
The platform has a user-friendly interface and provides integrated tools that simplify the development of ML models. For example, one of the features it offers is AutoML, which automates some steps during creation and allows for deployment with just a few clicks.
Finally, one of the major advantages is that SageMaker helps the company develop innovative products and services through the use of Machine Learning within a shorter time frame.
This way, it’s possible to adapt more quickly to market trends and consumer preferences, enhancing the brand’s competitive edge against the competition.
Amazon SageMaker use cases
It’s worth remembering that Amazon SageMaker allows for efficient creation and deployment of ML models. Therefore, the tool becomes a highly versatile resource for businesses in various situations. Here are some examples of use cases:
- Detecting anomalies: SageMaker helps create training models to identify abnormalities within patterns. This is a feature widely used in the medical and financial sectors, for example, to assist in disease diagnosis and detect irregular transactions.
- Data processing: Some business projects deal with extremely complex and voluminous data sets. Therefore, Machine Learning solutions are necessary to streamline the analysis of these data, sometimes providing real-time insights.
- Natural Language Processing (NLP): Amazon SageMaker also aids in the development of NLP applications, where a machine can understand and use human language. Just to illustrate, this is the case with chatbots and virtual assistants.
- Among others.
In an increasingly technological world, investing in digital transformation is not something that can be viewed as a luxury but rather a necessity. In this context, machine learning plays a very important role in the development of Artificial Intelligence solutions.
However, creating Machine Learning models involves some challenges, such as the choice of algorithms and the definition of hyperparameters. Therefore, to streamline this process, you can rely on the assistance of ML tools.
This is the case with Amazon SageMaker, developed specifically to assist in the creation, training, and deployment of ML models, allowing access to resources that expedite this process. Among the advantages of using the platform, you can mention:
- Cost reduction;
- Innovation of products and/or services;
- Time optimization.
However, it’s important to emphasize that, to fully leverage the potential of SageMaker and other AI solutions, it’s beneficial to have the assistance of a specialized partner company in this field. This way, you can ensure that the organization achieves the best results for the business.
So, how about relying on the expertise of BRQ’s specialists? Get to know our services and solutions!