Innovation is the instrument of entrepreneurship, and one area that is recording tremendous growth is machine learning.
Research reveals that the global machine learning industry will reach $20.83 billion in 2024, increasing from $1.58 billion in 2017.
Among the reasons for the huge growth of machine learning in different sectors of the economy is the availability of services that make it easy to create high-quality models, such as Amazon SageMaker.
Here are five frequently asked questions about Amazon SageMaker.
1. What is Amazon SageMaker?
Amazon SageMaker is a powerful service that allows developers and data scientists to easily and swiftly create, train, and utilize machine learning (ML) models within a single platform.
Traditional ML development is a complicated, costly, and iterative process. Lack of integrated tools for the ML workflow makes the process even harder. As such, you have to combine multiple tools and workflows that are time-consuming and error-prone.
Fortunately, SageMaker tackles the challenges of traditional ML development. It offers the components necessary for ML in a single toolset. Subsequently, your models are produced faster, using less effort and at a lower cost.
2. How does Amazon SageMaker Work?
In a nutshell, SageMaker lets you build, train, and implement ML models without the complexities of traditional ML development. It gets rid of the heavy lifting.
- Building models – You can create ML models seamlessly and prepare them for training. SageMaker has tools that allow you to connect to your training data and select and optimize the appropriate algorithms for the application. You don’t have to struggle to choose your preferred algorithm because the service has a list of some of the most popular and commonly used ML algorithms.
- Training models – With SageMaker, you can train your models easily and quickly. The service has the necessary infrastructure to train and to scale.
- Deploying models – After building and training your models, it is time to deploy them. SageMaker helps you implement ML models swiftly and with ease. You can start generating predictions on fresh data and those predictions are available to any production platform via an accessible API.
3. Is Amazon SageMaker Good?
Amazon SageMaker is excellent for ML. It stands out from the crowd because it makes ML less costly, less laborious, and less time-consuming. Typically, the machine learning process without tools like SageMaker is slow and complex. You have to undertake all the tasks below:
- Collect and prepare training data for creating ML models.
- Identify the ideal algorithm to train your models.
- Employ expensive computers to train models for predictions
- Adjust and fine-tune models to enhance prediction accuracy.
- Incorporate trained ML models into business applications
As you might have guessed, all the above activities are easier said than done. They require extensive expertise, broad knowledge in computing, and massive resources and time.
As such, SageMaker becomes an appealing alternative to using a disparate set of tools to build your ML models and eradicate the pains associated with traditional ML workflows. Here are more benefits of Amazon SageMaker.
Developing machine learning models is not a cheap endeavor. It requires high-quality data to train models, which is complex, time-intensive, and expensive. Consequently, any opportunity to reduce these costs is welcomed.
SageMaker provides several cost reduction opportunities. First, SageMaker cuts data labeling costs by up to 70% using Amazon SageMaker Ground Truth. Good machine learning models require large volumes of quality training data. While creating training data is often expensive, complicated, and time-consuming, Amazon SageMaker Ground Truth helps create and manage accurate training datasets.
Second, SageMaker lowers training costs by 90% using Managed Spot Training. Third, Amazon Elastic Inference decreases machine learning inference costs by 75%. It does this by aligning trained models with the right-sized instance.
Central Location for all ML Components
One primary pain point for traditional ML development is the huge number of tools needed to build and train models. You have to switch between numerous ML tools from different vendors.
SageMaker addresses this problem by offering an Integrated Development Environment (IDE) for machine learning.
Using the Amazon SageMaker Studio feature, you access a broad toolset for building, training, and deploying models in one interface. Hence, you can manage the ML process in SageMaker’s unified experience, which improves your productivity. If there is a model that you’ve already developed, SageMaker supports models from tools such as TensorFlow and Apache MXNet.
4. How Much Does Amazon SageMaker Cost?
Like many AWS products, SageMaker charges you for what you use, without minimum and upfront commitments. You incur costs for building, training, and deploying ML models by the second. You pay for the ML compute, storage, instances, and data processing resources you utilize.
Among the most remarkable things about Amazon SageMaker is that you can get started for free. New SageMaker users can try it for the first two months for free. The free tier includes;
- Two hundred fifty hours of t2.medium or t3.medium Jupyter Notebook usage for creating models.
- Fifty hours of m4.xlarge or m5.xlarge for model training
- One hundred twenty-five hours of m4.xlarge or m5.xlarge for deploying ML models
Notably, the free tier does not include storage volume usage. Also, it starts the first month you create your first resource.
5. What are the Use Cases for Amazon SageMaker?
SageMaker can be used in a range of industries to create, train, and implement ML models swiftly. It helps you move from concept to production with a few clicks. The three common use cases for SageMaker are industrial, commercial, and consumer.
In the industrial sector, SageMaker can predict equipment failure and take measures to prevent them. Also, it is useful for demand forecasting to predict demand and streamline supply-demand decisions.
SageMaker is valuable for fraud detection, demand forecasting, and explaining credit decisions in the commercial sector. The consumer sector can use SageMaker for demand forecasting, fraud detection, and reinforcement learning.
Machine learning is among the fastest-growing areas of technology. However, over time, developers and data scientists have struggled to create and implement high-quality models. This is primarily because of the complex, time-consuming, and costly processes of machine learning.
Fortunately, with Amazon SageMaker, much of the heavy lifting of the machine learning process is now in the past.
You can easily and quickly build, train, and deploy models faster, with less effort and lower costs. Also, you can access all of the necessary ML tools and workflows on a single platform.
Are you ready to cut data labeling costs by 70%, training costs by 90%, and inference costs by 75%? Then it’s time to try SageMaker for all your ML model needs.
More articles from our blog you may be interested in: