Many programmers utilize the hosted or physical environment to build, train and deploy ML models. As a result, it becomes difficult to adjust the resource levels and scale up or down the necessary processes as per need. AWS SageMaker addresses this problem by providing a cloud-based machine-learning solution that enables data scientists to build and train ML models quickly and affordably.
In this article, we will define Amazon SageMaker, how it works to develop efficient ML models, and its benefits for your enterprise.
AWS SageMaker- Definition
Amazon SageMaker is a cloud-based ML platform (managed by the leading cloud service provider, Amazon Web Service) that enables users to construct, train, tune, and deploy machine-learning models. You can use the platform to design an ML model from scratch or use their built-in algorithms.
Today, many enterprises use this cloud-based AWS platform for multiple purposes- from enhancing data training interfaces and designing accurate data models to accelerating production-ready AI models.
How Does SageMaker AWS Work to Develop Efficient ML Models?
The SageMaker Studio divides ML models into three phases: Build, Train, and Deploy. The phases are discussed in detail below:
Build
The first step for building an ML model involves assembling the data and creating data sets. The cloud-based AWS platform uses Jupyter notebooks to create and share codes and equations under a single file. These cloud-hosted notebooks can share files instantly with one click. Moreover, there is no limit to the extent of data collection and storage using the Amazon Simple Storage Service (S3), making visualization and creation of data sets easier.
Train
After building the data sets, data scientists need to train ML models to analyze and make predictions. For this, the SageMaker studio has in-built ML algorithms, which you can use as a learning algorithm to train your model.
Furthermore, as you continue to optimize your algorithms and make constant changes in the model, tracking the progress becomes tedious. SageMaker stores all the model iterations and organizes the changes made in parameters and data sets to help you monitor progress effectively. This AWS cloud platform also provides a debugger that fixes any standard error in the model by sending warnings during training models, thereby helping data scientists build sophisticated and meticulous models within a short time frame.
Deploy
After training your ML model successfully, it becomes ready for deployment. Deploying an ML model involves making the model available in real-time with the help of Application Program Interfaces (APIs).
The SageMaker consists of a model monitor that detects concept drifts. A concept drift represents the gap between the real-time data and the learning algorithms. Thus, the platform exposes prediction drifts and offers insights with a detailed report which helps to enhance the model in the future.
Benefits of SageMaker AWS
SageMaker has extensive applications across several industries. Learn more about how VW Credit, Inc. (VCI), a financial services arm of Volkswagen Group of America, enhanced data science capabilities with SageMaker studio. Thus, the benefits of this on-demand cloud-based ML platform are indeed far-reaching. Let’s discuss what these advantages are in detail:
- Productivity: The platform boosts the output of a machine learning project by computing instances in the shortest time possible. Thus, reducing the number of delays in model delivery and increasing productivity.
- Scalability: SageMaker provides a highly scalable architecture that enables enterprises to grow with future needs. It offers the ability to scale up or down as per requirements and helps with training models faster.
- Storage: Working with ML models is storage-intensive. But, SageMaker AWS provides suitable cloud storage to help with this problem. Now, you can store necessary data sets and various other model components in one place!
- Cost: Since Amazon SageMaker is a service provided by Amazon Web Service (AWS), it can access other resources provided by AWS. One such resource is Amazon Elastic Interface, which reduces inference costs in building and deploying ML models by up to 70 percent.
- Time-Efficient: SageMaker analyzes raw data sets, creates, trains, and deploys a model automatically in a time-efficient manner while providing open and absolute visibility. It also reduces the overall time for the various data labeling tasks effectively.
Expedite ML Journey for Your Enterprise with Wipro’s Amazon SageMaker Offerings
Thus, SageMaker has terrific value for enterprises that want to achieve an end-to-end machine-learning model solution. The platform has several useful features for quick model deployment while ensuring extreme effectiveness, versatility, and cost efficiency.
Wipro AWS AI Solution team helps enterprises take advantage of SageMaker by providing skilled AWS ML-certified professionals, AI/ML accelerators, a dedicated AWS AI/ML CoE Lab, and MLOPs framework. Our AI experts implement numerous sophisticated ML models for key solution areas (such as customer experience, demand forecasting, and industrial AI) on Sagemaker to help clients cut down costs related to building a robust AI/ML infrastructure.
Leverage Wipro’s AWS cloud consulting services to lower costs and deploy highly scalable server-less machine learning models today!
