Amazon SageMaker features and reviews of 2020

Amazon SageMaker Machine Learning software uses a fully managed service that allows developers and data scientists to build, train, and deploy ML models.


Amazon SageMaker Machine Learning software helps industries and organizations to eliminate the heavy lifting involved in the machine learning process and develop high-quality models. The platform provides all the components required for machine learning so that users get to the production aspect faster and at lower costs.

The software offers businesses with integrated tools that label, build, train, tune, deploy, and manage all the ML development steps. Developers use Amazon SageMaker to upload data, create new notebooks, compare results, and more, to improve their company’s productivity levels. All machine learning development activities are performed within the software’s visual interface.

Team members and employees can also share a notebook with others without interrupting anyone’s workflow. The platform allows for faster collaborations among developers and data scientists. Users can inspect raw data, apply feature processors, track performance, and rank models based on said performance with a few clicks. Amazon SageMaker Machine Learning software provides professionals with full visibility and control while building and training ML models. Companies can explore up to 50 different models generated by the platform, making it easy for users to select the best one for a particular occasion.

Amazon SageMaker Machine Learning software works both for experienced developers looking to develop a baseline model, and users without prior machine learning knowledge. The platform allows professionals to generate predictions for real-time or batch data. Amazon SageMaker works for various industries including, e-commerce, healthcare, cybersecurity, transportation, and agricultural sectors.

Product Details

Amazon SageMaker Machine Learning software allows developers to create accurate training datasets for their organizations. With the platform, professionals use machine learning to build datasets and reduce the cost of data labeling by 70 percent. Amazon SageMaker trains ML models to make correct decisions and reduces complexity and cost in the process. The software allows users to increase the accuracy of their data labeling by employing active learning.

Amazon SageMaker Machine Learning software helps professionals to improve teamwork with the collaborative notebook experience. The platform provides companies with one-click notebooks with elastic computing features that can be spun-up quickly. Data scientists and developers can explore and visualize their data before documenting their findings in re-usable workflows. The platform also allows users to import stored data from other Amazon services such as Amazon S3, and Amazon RDS. Amazon SageMaker Machine Learning software enables businesses to save time by using one of the several pre-built notebooks for different occasions. The models can be shared with colleagues to visualize and reproduce the developer’s results.

Amazon SageMaker Machine Learning software enables data scientists to manage their companies’ data processing needs at a scale. The platform helps professionals to overcome the challenge of using self-managed infrastructures that are hard to allocate. It extends the ease, reliability, and scalability of the software. Users can connect to existing file system data sources or storage and spin-up the resources they need to run a job. SageMaker saves the organization’s output to persistent storage and provides analysts with logs and metrics for later use.

Amazon SageMaker Machine Learning software provides businesses with high-performance algorithms that perform training on data sets. Data scientists can select from supervised algorithms during ML training and instruct the model when errors are made. The software allows users to address recommendations and time series prediction issues. SageMaker also supports unsupervised learning by enabling the algorithms to determine correct answers by themselves, with no external inputs. E-commerce sites use Amazon SageMaker Machine Learning software to solve problems like recognizing customer groupings based on their purchasing behavior and other features. The platform also helps organizations to detect malicious users or discover the usage patterns of different IP addresses.

Amazon SageMaker Machine Learning software enables developers to set up popular frameworks for deep learning such as Chainer, TensorFlow, and PyTorch. Users can write scripts and train custom TensorFlow models with the platform. Amazon SageMaker automatically configures and optimizes the frameworks for high performance. Industries that utilize machine learning models can set them up and save them within built-in containers.

Amazon SageMaker Machine Learning software allows professionals to test and prototype their ML models locally. The software provides open-source containers available for use on GitHub. Developers can download them to their organization’s local environment and test the scripts before finally deploying to SageMaker for training and hosting. Amazon SageMaker Machine Learning software offers a single line of code that allows users to switch from local testing to production training when they are ready.

Amazon SageMaker Machine Learning software provides industries with reinforcement learning to complement the supervised and unsupervised ones. The platform allows developers to train models using virtual 3D environments. Users can build models that make sophisticated decisions by using reinforcement learning to supervise machine learning training. Amazon SageMaker also supports unsupervised learning, where the model makes less sophisticated decisions. Organizations use the technology to detect anomalies in data such as signs of network intrusion or abnormal temperature fluctuations. Amazon SageMaker Machine Learning software also helps healthcare sectors to create personalized treatment regimens for their patients and optimizes manufacturing processes.

Amazon SageMaker Machine Learning software helps data scientists to track and organize iterations to their company’s ML models. The platform offers experiments that allow users to evaluate training runs of their projects. They can manage every change made by using SageMaker to capture configurations, input parameters, and results. The software also enables professionals to search for previous experiments or browse existing ones by using their characteristics.

Amazon SageMaker Machine Learning software allows industries to define their ML workflows. Professionals use the platform for data labeling, inference steps, and training. SageMaker supports Kubeflow Pipelines that enables users to build and deploy scalable and portable end-to-end machine learning pipelines. The technology helps businesses improve job scheduling and orchestration.


Amazon SageMaker Machine Learning software helps industries and businesses to create, train, and deploy ML models in one place. The platform eliminates most of the barriers that slow down developers that work with machine learning and improves productivity levels.