Training Jobs

Welcome to the Education & Training remote, part-time, freelance, and flexible jobs page! Education and Training is an exciting career choice for those who enjoy working with people and helping them learn new things. Education and Training professionals act as facilitators to learning by using classroom or virtual presentations or... More

training jobs

Welcome to the Education & Training remote, part-time, freelance, and flexible jobs page! Education and Training is an exciting career choice for those who enjoy working with people and helping them learn new things. Education and Training professionals act as facilitators to learning by using classroom or virtual presentations or individual instruction to help students learn complex subjects. Educators and Trainers plan, develop, and assign lessons, then evaluate students on their grasp of the lessons. Trainers can work in a corporate or private environment or virtually, helping others achieve their goals. Some job titles for Educator and Trainer are Teacher, Instructor, Coach, Tutor, Faculty, Curriculum Developer, Paraprofessional, and Program Director. If a career in Education and Training is in your future, be sure to check out the many contract, freelance, part and full-time opportunities found here.

To create a CustomJob, follow the instructions in one of the following tabs,depending on what tool you want to use. If you use the gcloud CLI,you can use a single command to autopackage training code on your local machineinto a Docker container image, push the container image toContainer Registry, and create a CustomJob. Other options assume you havealready created a Python training application or custom container image.

If your training code is on your local computer, we recommend that you followthe With autopackaging section. Alternatively, if you havealready created a Python training application or custom container image,then skip ahead to the Without autopackaging section.

SCRIPT_PATH: The path, relative to WORKING_DIRECTORY on your local filesystem, to the script that is the entry point for your training code. This can be a Python script(ending in .py) or a Bash script.

You can optionally replace script=SCRIPT_PATH withpython-module=PYTHON_MODULE to specify the name of aPython module in WORKING_DIRECTORY to run as the entry pointfor training. For example, instead of script=trainer/task.py, you mightspecify python-module=trainer.task.

If you don't use autopackaging, you can create a CustomJob with a commandsimilar to one of the following. Depending on whether you havecreated a Python training application or a custom container image, chooseone of the following tabs:

If you are using autopackaging, then you must only specify local-package-path,script, and other options related to autopackaging in the first worker pool.Omit fields related to your training code in subsequent worker pools, which willall use the same training container built by autopackaging.

The following instructions describe how to create a TrainingPipeline thatcreates a CustomJob and doesn't do anything else. If you want to useadditional TrainingPipeline features, like training with a managed dataset orcreating a Model resource at the end of training, read Creating trainingpipelines.

Optional: In the Arguments field, you can specify arguments forVertex AI to use when it starts running your training code.The maximum length for all arguments combined is 100,000 characters.The behavior of these arguments differs depending on what type ofcontainer you are using:

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Overall employment in education, training, and library occupations is projected to grow 7 percent from 2021 to 2031, about as fast as the average for all occupations; this increase is expected to result in about 658,200 new jobs over the decade. In addition to new jobs from growth, opportunities arise from the need to replace workers who leave their occupations permanently. About 929,900 openings each year, on average, are projected to come from growth and replacement needs.

When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.

First, 100 trainings jobs with any status, including those other than InProgress , are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.

list-training-jobs is a paginated operation. Multiple API calls may be issued in order to retrieve the entire data set of results. You can disable pagination by providing the --no-paginate argument.When using --output text and the --query argument on a paginated response, the --query argument must extract data from the results of the following query expressions: TrainingJobSummaries

Multiply ResourceRetainedBillableTimeInSeconds by the number of instances (InstanceCount ) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount .

An Amazon SageMaker training job is an iterative process that teaches a model to make predictions by presenting examples from a training dataset. Typically, a training algorithm computes several metrics, such as training error and prediction accuracy. These metrics help diagnose whether the model is learning well and will generalize well for making predictions on unseen data. The training algorithm writes the values of these metrics to logs, which SageMaker monitors and sends to Amazon CloudWatch in real time. To analyze the performance of your training job, you can view graphs of these metrics in CloudWatch. When a training job has completed, you can also get a list of the metric values that it computes in its final iteration by calling the DescribeTrainingJob operation.

If you want to profile your training job with a finer resolution down to 100-millisecond (0.1 second) granularity and store the training metrics indefinitely in Amazon S3 for custom analysis at any time, consider using Amazon SageMaker Debugger. SageMaker Debugger provides built-in rules to automatically detect common training issues; it detects hardware resource utilization issues (such as CPU, GPU, and I/O bottlenecks) and non-converging model issues (such as overfit, vanishing gradients, and exploding tensors). SageMaker Debugger also provides visualizations through Studio and its profiling report. To explore the Debugger visualizations, see SageMaker Debugger Insights Dashboard Walkthrough, Debugger Profiling Report Walkthrough, and Analyze Data Using the SMDebug Client Library.

SageMaker automatically parses training job logs and sends training metrics to CloudWatch. By default, SageMaker sends system resource utilization metrics listed in SageMaker Jobs and Endpoint Metrics. If you want SageMaker to parse logs and send custom metrics from a training job of your own algorithm to CloudWatch, you need to specify metrics definitions by passing the name of metrics and regular expressions when you configure a SageMaker training job request.

If you choose the Your own algorithm container in ECR option as your algorithm source in the SageMaker console when you create a training job, add the metric definitions in the Metrics section. The following screenshot shows how it should look after you add the example metric names and the corresponding regular expressions.

Typically, you split the data on which you train your model into training and validation datasets. You use the training set to train the model parameters that are used to make predictions on the training dataset. Then you test how well the model makes predictions by calculating predictions for the validation set. To analyze the performance of a training job, you commonly plot a training curve against a validation curve.

Viewing a graph that shows the accuracy for both the training and validation sets over time can help you to improve the performance of your model. For example, if training accuracy continues to increase over time, but, at some point, validation accuracy starts to decrease, you are likely overfitting your model. To address this, you can make adjustments to your model, such as increasing regularization.

For this example, you can use the Image-classification-full-training example in the Example notebooks section of your SageMaker notebook instance. If you don't have a SageMaker notebook instance, create one by following the instructions at Step 1: Create an Amazon SageMaker Notebook Instance. If you prefer, you can follow along with the End-to-End Multiclass Image Classification Example in the example notebook on GitHub. You also need an Amazon S3 bucket to store the training data and for the model output.

The U.S. Department of Labor's Employment and Training Administration (ETA) provides information on training programs and other services that are available to assist workers who have been laid off or are about to be laid off. For a list of programs nearest you, contact an American Job Center or call ETA's toll-free help line at 1-877-US-2JOBS (TTY: 1-877-889-5267). Services are designed to meet local needs and may vary from state to state. Some services for dislocated workers have eligibility requirements. Check with your State Dislocated Worker Unit for details.

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