Antigenic drift is an important reason why people can get flu multiple times over the course of their lives. Antigenic drift is also a primary reason why the composition of flu vaccines for use in the Northern and Southern Hemispheres is reviewed annually and updated as needed to keep up with evolving flu viruses.
While flu viruses evolve genetically all the time and often undergo antigenic drift, antigenic shift happens infrequently. Flu pandemics occur rarely; there have been four flu pandemics in the past 100 years. For more information, see pandemic flu. Type A viruses undergo both antigenic drift and shift and are the only flu viruses known to cause pandemics, while flu type B viruses change only by the more gradual process of antigenic drift.
Pesticide drift is the airborne movement of pesticides from an area of application to any unintended site. Drift can happen during pesticide application, when droplets or dust travel away from the target site. It can also happen after the application, when some chemicals become vapors that can move off-site. Pesticide drift can cause accidental exposure to people, animals, plants and property.
You might think of pesticide drift as the movement of spray droplets during application. This is called 'particle drift.' But, some pesticides are more likely to drift in the form of vapor. This can happen after an application even when the pesticide was applied as a solid or liquid. This is called 'vapor drift,' and an important factor is the pesticide's vapor pressure.
Pesticide drift can pose health risks to people and pets when sprays and dusts drift to nearby areas such as homes, schools, and playgrounds. Wildlife and plants are also at risk when drift affects natural areas and water sources. Herbicide drift can damage other nearby crops or make them unsellable if the active ingredient is not registered for a particular crop. Pesticide drift also results in wasted pesticide product. EPA estimates up to 70 million pounds of pesticides are lost to drift each year.
Reading the label is the first and most important way to minimize risk and exposure. Understanding the approved use instructions will help reduce the risk of drift. There are four main drift factors the EPA focuses on when reviewing pesticide product registrations:
Drift is closely related to nautical travel and the waters. In boat travel, for example, drift refers to the movement of a ship, its speed, the distance of a rope, the distance of cargo, and measurements of the ship itself. This is because the currents of the water and the wind cause things to float and drift along the water.
By the time of Chicago, Drift had indeed switched sides and he was annoyed to be cleaning up Ark debris on the Moon instead of tracking down remaining Decepticons. Optimus was bemused that Drift was so eager to fight his former allies. While grousing that the Ark was their last memories of Cybertron laid waste, the samurai was horrified to find Knucklehead's corpse: recently killed by Lockdown without Megatron to stop him. Drift made a grave for his friend and Prime told him that they'd stand against Lockdown together. Adrift in Space and Time
Terraform Drift Detection preemptively detects when a resource has changed from what Terraform shows in the state file. Terraform Cloud displays the current state of drift and provides additional information such as the last time drift was checked, the resources detected as being in a state of drift, and a visualization of what attributes have changed.
You can view data drift metrics with the Python SDK or in Azure Machine Learning studio. Other metrics and insights are available through the Azure Application Insights resource associated with the Azure Machine Learning workspace.
Data drift is one of the top reasons model accuracy degrades over time. For machine learning models, data drift is the change in model input data that leads to model performance degradation. Monitoring data drift helps detect these model performance issues.
Azure Machine Learning simplifies drift detection by computing a single metric abstracting the complexity of datasets being compared. These datasets may have hundreds of features and tens of thousands of rows. Once drift is detected, you drill down into which features are causing the drift. You then inspect feature level metrics to debug and isolate the root cause for the drift.
The data drift algorithm provides an overall measure of change in data and indication of which features are responsible for further investigation. Dataset monitors produce a number of other metrics by profiling new data in the timeseries dataset.
Select target dataset. The target dataset is a tabular dataset with timestamp column specified which will be analyzed for data drift. The target dataset must have features in common with the baseline dataset, and should be a timeseries dataset, which new data is appended to. Historical data in the target dataset can be analyzed, or new data can be monitored.
The target dataset is also profiled over time. The statistical distance between the baseline distribution of each feature is compared with the target dataset's over time. Conceptually, this is similar to the data drift magnitude. However this statistical distance is for an individual feature rather than all features. Min, max, and mean are also available.
Columns, or features, in the dataset are classified as categorical or numeric based on the conditions in the following table. If the feature does not meet these conditions - for instance, a column of type string with >100 unique values - the feature is dropped from our data drift algorithm, but is still profiled.
Even as you manage your resources through CloudFormation, users can change those resources outside of CloudFormation. Users can edit resources directly by using the underlying service that created the resource. For example, you can use the Amazon EC2 console to update a server instance that was created as part of a CloudFormation stack. Some changes may be accidental, and some may be made intentionally to respond to time-sensitive operational events. Regardless, changes made outside of CloudFormation can complicate stack update or deletion operations. You can use drift detection to identify stack resources to which configuration changes have been made outside of CloudFormation management. You can then take corrective action so that your stack resources are again in sync with their definitions in the stack template, such as updating the drifted resources directly so that they agree with their template definition. Resolving drift helps to ensure configuration consistency and successful stack operations.
Drift detection enables you to detect whether a stack's actual configuration differs, or has drifted, from its expected configuration. Use CloudFormation to detect drift on an entire stack, or on individual resources within the stack. A resource is considered to have drifted if any of its actual property values differ from the expected property values. This includes if the property or resource has been deleted. A stack is considered to have drifted if one or more of its resources have drifted.
To determine whether a resource has drifted, CloudFormation determines the expected resource property values, as defined in the stack template and any values specified as template parameters. CloudFormation then compares those expected values with the actual values of those resource properties as they currently exist in the stack. A resource is considered to have drifted if one or more of its properties have been deleted, or had their value changed.
CloudFormation detects drift on those AWS resources that support drift detection. Resources that don't support drift detection are assigned a drift status of NOT_CHECKED. For a list of AWS resources that support drift detection, see Resources that support import and drift detection operations.
In addition, CloudFormation supports drift detection on private resource types that are provisionable; that's, whose provisioning type is either FULLY_MUTABLE or IMMUTABLE. To perform drift detection on a resource of a private resource type, the default version of the resource type that you have registered in your account must be provisionable. For more information on resource provision type, see the ProvisioningType parameter of the DescribeType action in the AWS CloudFormation API Reference and of the DescribeType command in the AWS CLI Command Reference. For more information on private resources, see Using the CloudFormation registry.
When detecting drift on a stack, CloudFormation does not detect drift on any nested stacks that belong to that stack. Instead, you can initiate a drift detection operation directly on the nested stack.
CloudFormation only determines drift for property values that are explicitly set, either through the stack template or by specifying template parameters. This doesn't include default values for resource properties. To have CloudFormation track a resource property for purposes of determining drift, explicitly set the property value, even if you are setting it to the default value.
The current configuration of each supported resource matches its expected template configuration. A stack, stack set, or stack instance with no resources that support drift detection will also have a status of IN_SYNC.
Read permission for each resource that supports drift detection included in the stack. For example, if the stack includes an AWS::EC2::Instance resource, you must have ec2:DescribeInstances permission to perform drift detection on the stack.
In certain cases, objects contained in property arrays will be reported as drift, when in actuality they're default values supplied to the property from the underlying service responsible for the resource. 041b061a72