MLA 016 AWS SageMaker MLOps 2
Machine Learning Guide
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Using Batch Transform to Scale Your Machine Learning Models
You don't have to deploy your model. You can use something called batch transform. Let's say, if you're not using a whole lot of cpu or a ram, you can use elastic inference to attach a gpu or an i n f chip to your instance. So you can have more fine grained control over the type of environment that you set up. It's very similar to using a w s batch. A w s batch is a dedicated service for running one off jobs using a docker container on whatever e c two instance you want. But it's all tied in to the sage tooling so you get all the other features that i've mentioned before.
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SageMaker streamlines machine learning workflows by enabling integrated model training, tuning, deployment, monitoring, and pipeline automation within the AWS ecosystem, offering scalable compute options and flexible development environments. Cloud-native AWS machine learning services such as Comprehend and Poly provide off-the-shelf solutions for NLP, time series, recommendations, and more, reducing the need for custom model implementation and deployment.
Links- Notes and resources at ocdevel.com/mlg/mla-16
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- SageMaker enables model training within integrated data and ML pipelines, drawing from components such as Data Wrangler and Feature Store for a seamless workflow.
- Using SageMaker for training eliminates the need for manual transitions from local environments to the cloud, as models remain deployable within the AWS stack.
- SageMaker Studio offers a browser-based IDE environment with iPython notebook support, providing collaborative editing, sharing, and development without the need for complex local setup.
- Distributed, parallel training is supported with scalable EC2 instances, including AWS-proprietary chips for optimized model training and inference.
- SageMaker's Model Debugger and monitoring tools aid in tracking performance metrics, model drift, and bias, offering alerts via CloudWatch and accessible graphical interfaces.
- SageMaker supports various model creation approaches, including default AWS environments with pre-installed data science libraries, bring-your-own Docker containers, and hybrid customizations via requirements files.
- SageMaker JumpStart provides quick-start options for common ML tasks, such as computer vision or NLP, with curated pre-trained models and environment setups optimized for SageMaker hardware and operations.
- Users can leverage Autopilot for end-to-end model training and deployment with minimal manual configuration or start from JumpStart templates to streamline typical workflows.
- SageMaker Experiments supports automated hyperparameter search and optimization, using Bayesian optimization to evaluate and select the best performing configurations.
- Experiments and training runs are tracked, logged, and stored for future reference, allowing efficient continuation of experimentation and reuse of successful configurations as new data is incorporated.
- Trained models can be deployed as scalable REST endpoints, where users specify required EC2 instance types, including inference-optimized chips.
- Elastic Inference allows attachment of specialized hardware to reduce costs and tailor inference environments.
- Batch Transform is available for non-continuous, ad-hoc, or large batch inference jobs, enabling on-demand scaling and integration with data pipelines or serverless orchestration.
- SageMaker Pipelines manages the orchestration of ML workflows, supporting CI/CD by triggering retraining and deployments based on code changes or new data arrivals.
- CI/CD automation includes not only code unit tests but also automated monitoring of metrics such as accuracy, drift, and bias thresholds to qualify models for deployment.
- Monitoring features (like Model Monitor) provide ongoing performance assessments, alerting stakeholders to significant changes or issues.
- SageMaker supports integration with Kubernetes via EKS, allowing teams to leverage universal orchestration for containerized ML workloads across cloud providers or hybrid environments.
- The SageMaker Neo service optimizes and packages trained models for deployment to edge devices, mobile hardware, and AWS Lambda, reducing runtime footprint and syncing updates as new models become available.
- AWS offers a variety of cloud-native services for common ML tasks, accessible via REST or SDK calls and managed by AWS, eliminating custom model development and operations overhead.
- Comprehend for document clustering, sentiment analysis, and other NLP tasks.
- Forecast for time series prediction.
- Fraud Detector for transaction monitoring.
- Lex for chatbot workflows.
- Personalize for recommendation systems.
- Poly for text-to-speech conversion.
- Textract for OCR and data extraction from complex documents.
- Translate for machine translation.
- Panorama for computer vision on edge devices.
- These services continuously improve as AWS retrains and updates their underlying models, transferring benefits directly to customers without manual intervention.
- When building features such as document clustering, question answering, or recommendations, first review whether cloud-native services like Comprehend can fulfill requirements prior to investing in custom ML models.
- For custom NLP tasks not available in AWS services, use SageMaker to manage model deployment (e.g., deploying pre-trained Hugging Face Transformers for summarization or embeddings).
- Batch inference and feature extraction jobs can be triggered using SageMaker automation and event notifications, supporting modular, scalable, and microservices-friendly architectures.
- Tabular prediction and feature importance can be handled by pipe-lining data from relational stores through SageMaker Autopilot or traditional algorithms such as XGBoost.
- Recommendation workflows can combine embeddings, neural networks, and event triggers, with SageMaker handling monitoring, scaling, and retraining in response to user feedback and data drift.
- Employ AWS cloud-native services where possible to minimize infrastructure management and accelerate feature delivery.
- Use SageMaker JumpStart and Autopilot to jump ahead in common ML scenarios, falling back to custom code and containers only when unique use cases demand.
- Leverage SageMaker tools for pipeline orchestration, monitoring, retraining, and model deployment to ensure scalable, maintainable, and up-to-date ML workflows.
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