AWS Machine Learning Blog
In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment. Step 3: Fit the model To start the training process, call the fit method on your AutoMLV2 job object. This method requires specifying the input data’s location in Amazon S3 and whether SageMaker should wait for the job to complete before proceeding further. During this step, AutoMLV2 will automatically pre-process your data, select algorithms, train multiple models, and tune them to find the best solution. automl_sm_job.fit( inputs=[AutoMLDataChannel(s3_data_type='S3Prefix', s3_uri=train_uri, channel_type='training')], wait=True, logs=True ) Please note that model fitting may take several hours, depending on the size of your dataset and compute budget. A larger compute budget allows for more powerful instance types, which can accelerate the training process. In this situation, provided you’re not running this code as part of the provided SageMaker notebook (which handles the order of code cell processing correctly), you will need to implement some custom code that monitors the training status before retrieving and deploying the best model. 3. Deploying a model with AutoMLV2 Deploying a machine learning model into production is a critical step in your machine learning workflow, enabling your applications to make predictions from new data. SageMaker AutoMLV2 not only helps build and tune your models but also provides a seamless deployment experience. In this section, we’ll guide you through deploying your best model from an AutoMLV2 job as a fully managed endpoint in SageMaker. Step 1: Identify the best model and extract name After your AutoMLV2 job completes, the first step in the deployment process is to identify the best performing model, also known as the best candidate. This can be achieved by using the best_candidate method of your AutoML job object. You can either use this method immediately after fitting the AutoML job or specify the job name explicitly if you’re operating on a previously completed AutoML job. # Option 1: Directly after fitting the AutoML job best_candidate = automl_sm_job.best_candidate() # Option 2: Specifying the job name directly best_candidate = automl_sm_job.best_candidate(job_name='your-auto-ml-job-name') best_candidate_name = best_candidate['CandidateName'] Step 2: Create a SageMaker model Before deploying, create a SageMaker model from the best candidate. This model acts as a container for the artifacts and metadata necessary to serve predictions. Use the create_model method of the AutoML job object to complete this step. endpoint_name = f"ep-{best_candidate_name}-automl-ts" # Create a SageMaker model from the best candidate automl_sm_model = automl_sm_job.create_model(name=best_candidate_name, candidate=best_candidate) 4. Inference: Batch, real-time, and asynchronous For deploying the trained model, we explore batch, real-time, and asynchronous inference methods to cater to different use cases. The following figure is a decision tree to help you decide what type of endpoint to use. The diagram outlines a decision-making process for selecting between batch, asynchronous, or real-time inference endpoints. Starting with the need for immediate responses, it guides you through considerations like the size of the payload and the computational complexity of the model. Depending on these factors, you can choose a faster option with lower computational requirements or a slower batch process for larger datasets. Batch inference using SageMaker pipelines Usage: Ideal for generating forecasts in bulk, such as monthly sales predictions across all products and locations. Process: We used SageMaker’s batch transform feature to process a large dataset of historical sales data, outputting forecasts for the specified horizon. The inference pipeline used for batch inference demonstrates a comprehensive approach to deploying, evaluating, and conditionally registering a machine learning model for time series forecasting using SageMaker. This pipeline is structured to ensure a seamless flow from data preprocessing, through model inference, to post-inference evaluation and conditional model registration. Here’s a detailed breakdown of its construction: Batch tranform step Transformer Initialization: A Transformer object is created, specifying the model to use for batch inference, the compute resources to allocate, and the output path for the results. Transform step creation: This step invokes the transformer to perform batch inference on the specified input data. The step is configured to handle data in CSV format, a common choice for structured time series data. Evaluation step Processor setup: Initializes an SKLearn processor with the specified role, framework version, instance count, and type. This processor is used for the evaluation of the model’s performance. Evaluation processing: Configures the processing step to use the SKLearn processor, taking the batch transform output and test data as inputs. The processing script (evaluation.py) is specified here, which will compute evaluation metrics based on the model’s predictions and the true labels. Evaluation strategy: We adopted a comprehensive evaluation approach, using metrics like mean absolute error (MAE) and root-means squared error (RMSE) to quantify the model’s accuracy and adjusting the forecasting configuration based on these insights. Outputs and property files: The evaluation step produces an output file (evaluation_metrics.json) that contains the computed metrics. This file is stored in Amazon S3 and registered as a property file for later access in the pipeline. Conditional model registration Model metrics setup: Defines the model metrics to be associated with the model package, including statistics and explainability reports sourced from specified Amazon S3 URIs. Model registration: Prepares for model registration by specifying content types, inference and transform instance types, model package group name, approval status, and model metrics. Conditional registration step: Implements a condition based on the evaluation metrics (for example, MAE). If the condition (for example, MAE is greater than or equal to threshold) is met, the model is registered; otherwise, the pipeline concludes without model registration. Pipeline creation and runtime Pipeline definition: Assembles the pipeline by naming it and specifying the sequence of steps to run: batch transform, evaluation, and conditional registration. Pipeline upserting and runtime: The pipeline.upsert method is called to create or update the pipeline based on the provided definition, and pipeline.start() runs the pipeline. The following figure is an example of the SageMaker Pipeline directed acyclic graph (DAG). This pipeline effectively integrates several stages of the machine learning lifecycle into a cohesive workflow, showcasing how Amazon SageMaker can be used to automate the process of model deployment, evaluation, and conditional registration based on performance metrics. By encapsulating these steps within a single pipeline, the approach enhances efficiency, ensures consistency in model evaluation, and streamlines the model registration process all while maintaining the flexibility to adapt to different models and evaluation criteria. Inferencing with Amazon SageMaker Endpoint in (near) real-time But what if you want to run inference in real-time or asynchronously? SageMaker real-time endpoint inference offers the capability to deliver immediate predictions from deployed machine learning models, crucial for scenarios demanding quick decision making. When an application sends a request to a SageMaker real-time endpoint, it processes the data in real time and returns the prediction almost immediately. This setup is optimal for use cases that require near-instant responses, such as personalized content delivery, immediate fraud detection, and live anomaly detection. Usage: Suited for on-demand forecasts, such as predicting next week’s sales for a specific product at a particular location. Process: We deployed the model as a SageMaker endpoint, allowing us to make real-time predictions by sending requests with the required input data. Deployment involves specifying the number of instances and the instance type to serve predictions. This step creates an HTTPS endpoint that your applications can invoke to perform real-time predictions. # Deploy the model to a SageMaker endpoint predictor = automl_sm_model.deploy(initial_instance_count=1, endpoint_name=endpoint_name, instance_type='ml.m5.xlarge') The deployment process is asynchronous, and SageMaker takes care of provisioning the necessary infrastructure, deploying your model, and ensuring the endpoint’s availability and scalability. After the model is deployed, your applications can start sending prediction requests to the endpoint URL provided by SageMaker. While real-time inference is suitable for many use cases, there are scenarios where a slightly relaxed latency requirement can be beneficial. SageMaker Asynchronous Inference provides a queue-based system that efficiently handles inference requests, scaling resources as needed to maintain performance. This approach is particularly useful for applications that require processing of larger datasets or complex models, where the immediate response is not as critical. Usage: Examples include generating detailed reports from large datasets, performing complex calculations that require significant computational time, or processing high-resolution images or lengthy audio files. This flexibility makes it a complementary option to real-time inference, especially for businesses that face fluctuating demand and seek to maintain a balance between performance and cost. Process: The process of using asynchronous inference is straightforward yet powerful. Users submit their inference requests to a queue, from which SageMaker processes them sequentially. This queue-based system allows SageMaker to efficiently manage and scale resources according to the current workload, ensuring that each inference request is handled as promptly as possible. Clean up To avoid incurring unnecessary charges and to tidy up resources after completing the experiments or running the demos described in this post, follow these steps to delete all deployed resources: Delete the SageMaker endpoints: To delete any deployed real-time or asynchronous endpoints, use the SageMaker console or the AWS SDK. This step is crucial as endpoints can accrue significant charges if left running. Delete the SageMaker Pipeline: If you have set up a SageMaker Pipeline, delete it to ensure that there are no residual executions that might incur costs. Delete S3 artifacts: Remove all artifacts stored in your S3 buckets that were used for training, storing model artifacts, or logging. Ensure you delete only the resources related to this project to avoid data loss. Clean up any additional resources: Depending on your specific implementation and additional setup modifications, there may be other resources to consider, such as roles or logs. Check your AWS Management Console for any resources that were created and delete them if they are no longer needed. Conclusion This post illustrates the effectiveness of Amazon SageMaker AutoMLV2 for time series forecasting. By carefully preparing the data, thoughtfully configuring the model, and using both batch and real-time inference, we demonstrated a robust methodology for predicting future sales. This approach not only saves time and resources but also empowers businesses to make data-driven decisions with confidence. If you’re inspired by the possibilities of time series forecasting and want to experiment further, consider exploring the SageMaker Canvas UI. SageMaker Canvas provides a user-friendly interface that simplifies the process of building and deploying machine learning models, even if you don’t have extensive coding experience. Visit the SageMaker Canvas page to learn more about its capabilities and how it can help you streamline your forecasting projects. Begin your journey towards more intuitive and accessible machine learning solutions today! About the Authors Nick McCarthy is a Senior Machine Learning Engineer at AWS, based in London. He has worked with AWS clients across various industries including healthcare, finance, sports, telecoms and energy to accelerate their business outcomes through the use of AI/ML. Outside of work he loves to spend time travelling, trying new cuisines and reading about science and technology. Nick has a Bachelors degree in Astrophysics and a Masters degree in Machine Learning. Davide Gallitelli is a Senior Specialist Solutions Architect for AI/ML in the EMEA region. He is based in Brussels and works closely with customers throughout Benelux. He has been a developer since he was very young, starting to code at the age of 7. He started learning AI/ML at university, and has fallen in love with it since then.
Published: 2024-10-08T17:39:14
