MLA 016 AWS SageMaker MLOps 2
Machine Learning Guide
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Using U K P Lab Sentence Transformers, I'm Using a Sage Maker Pipe Line to Transform Journal Entry Into a Feature
One step of the feature engineering process is to transform those journal entries into an embedding using u k p lab sentence transformers. And that job is going to be running as a sage maker batch transform job. The next step of the pipe line, which gets kicked off by some other notification during this process, we'll take that embeddings and compare those embeddings using cosin similarity to other embeddings. Now you'll note nothing here is training yet. So far, everything i'm talking about in my tool chain are pre trained. Well, like i said, nothe will look at your fields and see how they impact each other. Consider this table data. Eventually
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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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