AWS Config allows security and compliance professionals to assess, audit, and evaluate the configurations of their AWS resources via Config rules, which evaluate the compliance of AWS resources against specified policies.
New Amplify Flutter supports customizable authentication flowsĪWS Security Hub now automatically receives AWS Config managed and custom rule evaluation results as security findings. Getting started takes just a click, eliminating the need to build custom applications or integrate with third-party products. This helps businesses optimize their operations, meet service level goals, and improve agent and customer satisfaction. Machine-learning powered capabilities make it easier for contact center managers to help predict contact volumes and average handle time with high accuracy, determine ideal staffing levels, and optimize agent schedules to ensure they have the right agents at the right time.
Deploy, test, run, maintain, operate, and evolve migrated applications in the runtime environment with no upfront costs.Īmazon Connect forecasting, capacity planning, and scheduling, now available in the Europe (London) AWS RegionĪmazon Connect forecasting, capacity planning, and scheduling (preview) are now available in the Europe (London) AWS Region. You can break up and manage your complete migration with infrastructure, software, and tools to refactor or replatform legacy applications. Use Mainframe Modernization to easily migrate and modernize your mainframe applications, increasing agility and reducing costs. Incident Manager from AWS Systems Manager now streamlines responses to ServiceNow Incidents As a result, evaluating model accuracy on the test set data provides a real-world estimate of model performance.
Evaluating the model on data seen during the training can be biased, thus setting test data aside prior to training is crucial. For example, if you create a random split of your data into a training set and test set, you can train a machine learning model on the training set and then evaluate your machine learning model on the test set. SageMaker Data Wrangler also provides various types of splits including: randomized, ordered, stratified, and key-based splits along with the option to specify how much data should go in each split. With SageMaker Data Wrangler’s new train-test split transform, you can now split your data into train, test, and validation sets for use in downstream model training and validation.
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Previously data scientists had to write code to split their data into train and test sets before training ML models. Starting this week, you can now split your data into train and test sets in just a few clicks with Data Wrangler. With just a few clicks, you can automatically build, train, and tune machine learning models, making it easier to automatically employ state-of-the-art feature engineering techinques, train high quality machine learning models, and gain insights from your data faster. With this unified experience, you can now prepare your data in SageMaker Data Wrangler and easily export to SageMaker Autopilot for model training. Previously, customers used Data Wrangler to prepare their data for machine learning and Autopilot for training machine learning models independently.
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SageMaker Autopilot automatically builds, trains, and tunes the best machine learning models based on your data, while allowing you to maintain full control and visibility. SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes. Starting this week, you can invoke SageMaker Autopilot from SageMaker Data Wrangler to automatically train, tune and build machine learning models.
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