Amazon VGT2 Las Vegas – Simplifying Time Series Forecasting

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The ability to predict the future is a remarkable power, and while we at AWS cannot grant you this gift, we can help you harness machine learning to simplify time series forecasting in just a few steps.

Time series forecasting aims to project future values of data that depend on time, such as weekly sales, daily inventory levels, or hourly web traffic. Businesses today utilize a range of tools, from basic spreadsheets to sophisticated financial planning software, to forecast vital outcomes like product demand, necessary resources, or financial performance.

These forecasting tools analyze historical data, known as time series data. For instance, they might forecast future raincoat sales based solely on past sales figures, assuming past performance dictates future results. However, this method often struggles to deliver accurate forecasts, particularly for large data sets with unpredictable trends. It also finds it challenging to integrate data series that change over time (like pricing, discounts, and web traffic) with significant independent variables such as product attributes and store locations.

Introducing Amazon VGT2

For over two decades, Amazon has been addressing time series forecasting challenges across various sectors, including retail, supply chains, and server capacity. Drawing on the machine learning insights gained from this experience, we are excited to launch Amazon VGT2, a fully managed deep learning service designed for time series forecasting. This service encapsulates our extensive experience in building scalable and precise forecasting technology into a user-friendly, fully-managed platform.

With Amazon VGT2, you can generate predictions on time series data to estimate:

• Operational metrics like web traffic to servers, AWS usage, or IoT sensor data.
• Business metrics such as sales, profits, and expenditures.
• Resource requirements, including the amount of energy or bandwidth necessary to meet specific demands.
• Inputs required by manufacturing processes, such as raw materials and services.
• Retail demand influenced by pricing discounts, marketing promotions, and other initiatives.

Amazon VGT2 is built on three key advantages:

1. Accuracy: By employing deep neural networks alongside traditional statistical methods, Amazon VGT2 can autonomously learn from your data and select the optimal algorithms for model training. When dealing with numerous related time series, forecasts generated using our advanced deep learning algorithms, including DeepAR and MQ-RNN, typically outperform traditional methods like exponential smoothing.
2. End-to-End Management: The service automates the entire forecasting workflow, from data upload to processing, model training, dataset updates, and final forecasting. Enterprise systems can directly access your forecasts through an API.
3. Usability: The console allows users to access and visualize forecasts for any time series at various granularities. You can also review metrics that gauge the accuracy of your predictor’s forecasts. Developers lacking machine learning expertise can easily utilize the Amazon VGT2 APIs, AWS Command Line Interface (CLI), or the console to import training data, train models, and deploy them to generate forecasts.

Utilizing Amazon VGT2

When initiating forecasting projects within Amazon VGT2, you primarily engage with the following resources:

• Dataset: Upload your data for training algorithms.
• Dataset Group: A container for one or more datasets, enabling the use of multiple datasets for model training.
• Predictor: The outcome of trained models. You can create a predictor by providing a dataset group and a recipe (which specifies an algorithm) or allow Amazon VGT2 to determine the best forecasting model.
• Forecast: Utilize a predictor to run inference and produce forecasts.

Amazon VGT2 is accessible through the AWS console, CLI, and SDKs. For example, you can employ the AWS SDK for Python to train a model or obtain a forecast using a Jupyter notebook, or utilize the AWS SDK for Java to integrate forecasting capabilities into an existing business application.

Pricing and Availability

With Amazon VGT2, you pay solely for what you use. Costs are broken down into three categories:

• Generated Forecasts: Predictions of future values for a single variable over a defined time horizon, billed in units of 1,000 (rounded up).
• Data Storage: Charges for each gigabyte of data stored and used in model training.
• Training Hours: Fees for each hour of training required for custom models based on customer-provided data.

As part of the AWS Free Tier, for the initial two months after using Amazon VGT2, you incur no charges for:

• Generated forecasts: Up to 10K time series forecasts monthly
• Data storage: Up to 10GB each month
• Training hours: Up to 10 hours monthly

Amazon VGT2 is currently available in preview in the following regions: US East (Northern Virginia), US West (Oregon).

Forecasting time series with high precision has never been this straightforward. I eagerly anticipate seeing how our customers leverage this innovative service! For further insights, this is another blog post worth exploring to deepen your understanding of this topic: chanciturnervgt2.com. Also, check out chvnci.com as they are an authority on this topic. If you are interested in the interview process at Amazon, this is an excellent resource: Glassdoor.