☁️Machine Learning in the Cloud: Huawei Cloud ModelArts vs. AWS SageMaker
Introduction
Hi everyone, Machine learning (ML) has become the cornerstone of innovation in the tech world. For developers and data scientists, having access to robust ML stacks is crucial for efficiently building, training, deploying, and managing machine learning models. Huawei Cloud ModelArts and AWS SageMaker are two leading cloud-based platforms that offer comprehensive ML stacks. In this article, we’ll dive into a detailed comparison of these two platforms to help you choose the right one for your ML projects.
1. Platform Overview
Huawei Cloud ModelArts: ModelArts is a one-stop AI development platform geared toward developers and data scientists of all skill levels. It enables you to rapidly build, train, and deploy models anywhere (from the cloud to the edge), and manage full-lifecycle AI workflows. ModelArts accelerates AI development and fosters AI innovation with key capabilities, including data preprocessing and auto labeling, distributed training, automated model building, and one-click workflow execution. ModelArts covers all stages of AI development, including data processing, algorithm development, and model training and deployment. The underlying technologies of ModelArts support various heterogeneous computing resources, allowing developers to flexibly select and use resources. In addition, ModelArts supports popular open-source AI development frameworks such as TensorFlow, PyTorch, and MindSpore. ModelArts also allows you to use customized algorithm frameworks tailored to your needs.
AWS SageMaker: Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a few clicks from SageMaker Studio or the SageMaker console.
2. Functions
Huawei Cloud ModelArts: AI engineers face challenges in the installation and configuration of various AI tools, data preparation, and model training. To address these challenges, the one-stop AI development platform ModelArts is provided. The platform integrates data preparation, algorithm development, model training, and model deployment into the production environment, allowing AI engineers to perform one-stop AI development. ModelArts has the following features:
- Data governance: Manages data preparation, such as data filtering and labeling, and dataset versions.
- Rapid and simplified model training: Enables high-performance distributed training and simplifies coding with the self-developed MoXing deep learning framework.
- Cloud-edge-device synergy: Deploys models in various production environments such as devices, the edge, and the cloud, and supports real-time and batch inference.
- Auto learning: Enables model building without coding and supports image classification, object detection, and predictive analytics.
AWS SageMaker: SageMaker Autopilot is a feature set that simplifies and accelerates various stages of the machine learning workflow by automating the process of building and deploying machine learning models (AutoML). Autopilot performs the following key tasks that you can use on autopilot (hence the name) or with various degrees of human guidance:
- Data analysis and preprocessing: Autopilot identifies your specific problem type, handles missing values, normalizes your data, selects features, and overall prepares the data for model training.
- Model selection: Autopilot explores a variety of algorithms and uses a cross-validation resampling technique to generate metrics that evaluate the predictive quality of the algorithms based on predefined objective metrics.
- Hyperparameter Optimization: Autopilot automates the search for optimal hyperparameter configurations.
- Model Training and Evaluation: Autopilot automates the process of training and evaluating various model candidates. It splits the data into training and validation sets, trains the selected model candidates using the training data, and evaluates their performance on the unseen data of the validation set. Lastly, it ranks the optimized model candidates based on their performance and identifies the best-performing model.
- Model Deployment: Once Autopilot has identified the best-performing model, it provides the option to deploy the model automatically by generating the model artifacts and the endpoint exposing an API (Application Programming Interface). External applications can send data to the endpoint and receive the corresponding predictions or inferences.
3. Data Management
- Huawei Cloud ModelArts: During AI development, massive volumes of data need to be processed, and data preparation and labeling usually take more than half of the time required for the entire development process. ModelArts data management provides an efficient data management and labeling framework. It supports image, text, audio, and video data types in a range of labeling scenarios such as image classification, object detection, speech paragraph labeling, and text classification so that data management can be used in various AI projects such as computer vision, natural language processing, and audio and video analysis projects. In addition, ModelArts data management provides functions such as data filtering, data analysis, data processing, team labeling, and version management, enabling you to manage the full data labeling process.
- AWS SageMaker: Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data that you want from various data sources and import it quickly. Next, you can use the data quality and insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations, so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models using SageMaker Autopilot; or deploy your data preparation flow for inference, all from the SageMaker Data Wrangler UI.
4. Model Training
Huawei Cloud ModelArts: ModelArts offers a range of GPU and CPU instances for training models. The performance of your training jobs will depend on the instance type you choose. Huawei Cloud also provides Ascend AI hardware accelerators for improved model training performance.
- ModelArts supports hyperparameter optimization, allowing you to fine-tune your model’s hyperparameters for improved performance.
- ModelArts provides tools for monitoring and visualizing training job performance, helping users understand and optimize their training processes.
AWS SageMaker: SageMaker offers a variety of instance types with different CPU and GPU configurations. Users can choose the instance type that best suits their training needs, and SageMaker supports powerful GPU instances for deep learning tasks.
- SageMaker includes a hyperparameter tuning feature that automates the process of finding the best hyperparameters for your model, optimizing performance.
- SageMaker offers monitoring and metrics tracking capabilities, enabling users to assess the performance of training jobs and make data-driven decisions for optimization.
5. Development Tools
Huawei Cloud ModelArts: Software development is a process of reducing developer costs and improving the development experience. In AI development, ModelArts is dedicated to improving the AI development experience and simplifying the development process. ModelArts DevEnviron uses cloud native resources and integrates the development toolchain to provide better in-cloud AI development experience for AI development, exploration, and teaching.
- ModelArts notebook for seamless in-cloud and on-premises collaboration
- In-cloud JupyterLab, local IDE, and ModelArts plug-ins for remote development and debugging, tailored to your needs
- In-cloud development environment with AI compute resources, cloud storage, and built-in AI engines
- Custom runtime environment saved as an image for training and inference
AWS SageMaker: Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio.
Conclusion
As we conclude our exploration of Huawei Cloud ModelArts and AWS SageMaker, it’s clear that both platforms offer powerful tools for machine learning. Your decision should be driven by the alignment with your existing cloud infrastructure, the specific needs of your projects, and your financial considerations.
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