Monthly Archives: January 2025

This is a guest post authored by Asaf Fried, Daniel Pienica, Sergey Volkovich from Cato Networks.

Cato Networks is a leading provider of secure access service edge (SASE), an enterprise networking and security unified cloud-centered service that converges SD-WAN, a cloud network, and security service edge (SSE) functions, including firewall as a service (FWaaS), a secure web gateway, zero trust network access, and more.

On our SASE management console, the central events page provides a comprehensive view of the events occurring on a specific account. With potentially millions of events over a selected time range, the goal is to refine these events using various filters until a manageable number of relevant events are identified for analysis. Users can review different types of events such as security, connectivity, system, and management, each categorized by specific criteria like threat protection, LAN monitoring, and firmware updates. However, the process of adding filters to the search query is manual and can be time consuming, because it requires in-depth familiarity with the product glossary.

To address this challenge, we recently enabled customers to perform free text searches on the event management page, allowing new users to run queries with minimal product knowledge. This was accomplished by using foundation models (FMs) to transform natural language into structured queries that are compatible with our products’ GraphQL API.

In this post, we demonstrate how we used Amazon Bedrock, a fully managed service that makes FMs from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and quickly integrate and deploy them into your applications using AWS tools without having to manage the infrastructure. Amazon Bedrock enabled us to enrich FMs with product-specific knowledge and convert free text inputs from users into structured search queries for the product API that can greatly enhance user experience and efficiency in data management applications.

Solution overview

The Events page includes a filter bar with both event and time range filters. These filters need to be added and updated manually for each query. The following screenshot shows an example of the event filters (1) and time filters (2) as seen on the filter bar (source: Cato knowledge base).

The event filters are a conjunction of statements in the following form:

• Key – The field name
• Operator – The evaluation operator (for example, is, in, includes, greater than, etc.)
• Value – A single value or list of values

For example, the following screenshot shows a filter for action in [ Alert, Block ].

The time filter is a time range following ISO 8601 time intervals standard.

For example, the following screenshot shows a time filter for UTC.2024-10-{01/00:00:00--02/00:00:00}.

Converting free text to a structured query of event and time filters is a complex natural language processing (NLP) task that can be accomplished using FMs. Customizing an FM that is specialized on a specific task is often done using one of the following approaches:

• Prompt engineering – Add instructions in the context/input window of the model to help it complete the task successfully.
• Retrieval Augmented Generation (RAG) – Retrieve relevant context from a knowledge base, based on the input query. This context is augmented to the original query. This approach is used for reducing the amount of context provided to the model to relevant data only.
• Fine-tuning – Train the FM on data relevant to the task. In this case, the relevant context will be embedded into the model weights, instead of being part of the input.

For our specific task, we’ve found prompt engineering sufficient to achieve the results we needed.

Because the event filters on the Events page are specific to our product, we need to provide the FM with the exact instructions for how to generate them, based on free text queries. The main considerations when creating the prompt are:

• Include the relevant context – This includes the following:
  • The available keys, operators, and values the model can use.
  • Specific instructions. For example, numeric operators can only be used with keys that have numeric values.
• Make sure it’s simple to validate – Given the extensive number of instructions and limitations, we can’t trust the model output without checking the results for validity. For example, what if the model generates a filter with a key not supported by our API?

Instead of asking the FM to generate the GraphQL API request directly, we can use the following method:

1. Instruct the model to return a response following a well-known JSON schema validation IETF standard.
2. Validate the JSON schema on the response.
3. Translate it to a GraphQL API request.

Request prompt

Based on the preceding examples, the system prompt will be structured as follows:

# Genral Instructions

Your task is to convert free text queries to a JSON format that will be used to query security and network events in a SASE management console of Cato Networks. You are only allowed to output text in JSON format. Your output will be validated against the following schema that is compatible with the IETF standard:

# Schema definition
{
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "title": "Query Schema",
   "description": "Query object to be executed in the 'Events' management console page. ",
    "type": "object",
    "properties":
    {
        "filters":
        {
            "type": "array",
           "description": "List of filters to apply in the query, based on the free text query provided.",
            "items":
            {
                "oneOf":
                [
                    {
                        "$ref": "#/$defs/Action"
                    },
                    .
                    .
                    .
                ]
            }
        },
        "time":
        {
            "description": "Start datetime and end datetime to be used in the query.",
            "type": "object",
            "required":
            [
                "start",
                "end"
            ],
            "properties":
            {
                "start":
                {
                    "description": "start datetime",
                    "type": "string",
                    "format": "date-time"
                },
                "end":
                {
                    "description": "end datetime",
                    "type": "string",
                    "format": "date-time"
                }
            }
        },
        "$defs":
        {
            "Operator":
            {
                "description": "The operator used in the filter.",
                "type": "string",
                "enum":
                [
                    "is",
                    "in",
                    "not_in",
                    .
                    .
                    .
                ]
            },
            "Action":
            {
                "required":
                [
                    "id",
                    "operator",
                    "values"
                ],
                "description": "The action taken in the event.",
                "properties":
                {
                    "id":
                    {
                        "const": "action"
                    },
                    "operator":
                    {
                        "$ref": "#/$defs/Operator"
                    },
                    "values":
                    {
                        "type": "array",
                        "minItems": 1,
                        "items":
                        {
                            "type": "string",
                            "enum":
                            [
                                "Block",
                                "Allow",
                                "Monitor",
                                "Alert",
                                "Prompt"
                            ]
                        }
                    }
                }
            },
            .
            .
            .
        }
    }
}

Each user query (appended to the system prompt) will be structured as follows:

# Free text query
Query: {free_text_query}

# Add current timestamp for context (used for time filters) 
Context: If you need a reference to the current datetime, it is {datetime}, and the current day of the week is {day_of_week}

The same JSON schema included in the prompt can also be used to validate the model’s response. This step is crucial, because model behavior is inherently non-deterministic, and responses that don’t comply with our API will break the product functionality.

In addition to validating alignment, the JSON schema can also point out the exact schema violation. This allows us to create a policy based on different failure types. For example:

• If there are missing fields marked as required, output a translation failure to the user
• If the value given for an event filter doesn’t comply with the format, remove the filter and create an API request from other values, and output a translation warning to the user

After the FM successfully translates the free text into structured output, converting it into an API request—such as GraphQL—is a straightforward and deterministic process.

To validate this approach, we’ve created a benchmark with hundreds of text queries and their corresponding expected JSON outputs. For example, let’s consider the following text query:

Security events with high risk level from IPS and Anti Malware engines

For this query, we expect the following response from the model, based on the JSON schema provided:

{
    "filters":
    [
        {
            "id": "risk_level",
            "operator": "is",
            "values":
            [
                "High"
            ]
        },
        {
            "id": "event_type",
            "operator": "is",
            "values":
            [
                "Security"
            ]
        },
        {
            "id": "event_subtype ",
            "operator": "in",
            "values":
            [
                "IPS",
                "Anti Malware"
            ]
        }
    ]
}

For each response of the FM, we define three different outcomes:

• Success:
  • Valid JSON
  • Valid by schema
  • Full match of filters
• Partial:
  • Valid JSON
  • Valid by schema
  • Partial match of filters
• Error:
  • Invalid JSON or invalid by schema

Because translation failures lead to a poor user experience, releasing the feature was contingent on achieving an error rate below 0.05, and the selected FM was the one with the highest success rate (ratio of responses with full match of filters) passing this criterion.

Working with Amazon Bedrock

Amazon Bedrock is a fully managed service that simplifies access to a wide range of state-of-the-art FMs through a single, serverless API. It offers a production-ready service capable of efficiently handling large-scale requests, making it ideal for enterprise-level deployments.

Amazon Bedrock enabled us to efficiently transition between different models, making it simple to benchmark and optimize for accuracy, latency, and cost, without the complexity of managing the underlying infrastructure. Additionally, some vendors within the Amazon Bedrock landscape, such as Cohere and Anthropic’s Claude, offer models with native understanding of JSON schemas and structured data, further enhancing their applicability to our specific task.

Using our benchmark, we evaluated several FMs on Amazon Bedrock, taking into account accuracy, latency, and cost. Based on the results, we selected anthropic.claude-3-5-sonnet-20241022-v2:0, which met the error rate criterion and achieved the highest success rate while maintaining reasonable costs and latency. Following this, we proceeded to develop the complete solution, which includes the following components:

• Management console – Cato’s management application that the user interacts with to view their account’s network and security events.
• GraphQL server – A backend service that provides a GraphQL API for accessing data in a Cato account.
• Amazon Bedrock – The cloud service that handles hosting and serving requests to the FM.
• Natural language search (NLS) service – An Amazon Elastic Kubernetes Service (Amazon EKS) hosted service to bridge between Cato’s management console and Amazon Bedrock. This service is responsible for creating the complete prompt for the FM and validating the response using the JSON schema.

The following diagram illustrates the workflow from the user’s manual query to the extraction of relevant events.

With the new capability, users can also use free text query mode, which is processed as shown in the following diagram.

The following screenshot of the Events page displays free text query mode in action.

Business impact

The recent feature update has received positive customer feedback. Users, especially those unfamiliar with Cato, have found the new search capability more intuitive, making it straightforward to navigate and engage with the system. Additionally, the inclusion of multi-language input, natively supported by the FM, has made the Events page more accessible for non-native English speakers to use, helping them interact and find insights in their own language.

One of the standout impacts is the significant reduction in query time—cut down from minutes of manual filtering to near-instant results. Account admins using the new feature have reported near-zero time to value, experiencing immediate benefits with minimal learning curve.

Conclusion

Accurately converting free text inputs into structured data is crucial for applications that involve data management and user interaction. In this post, we introduced a real business use case from Cato Networks that significantly improved user experience.

By using Amazon Bedrock, we gained access to state-of-the-art generative language models with built-in support for JSON schemas and structured data. This allowed us to optimize for cost, latency, and accuracy without the complexity of managing the underlying infrastructure.

Although a prompt engineering solution met our needs, users handling complex JSON schemas might want to explore alternative approaches to reduce costs. Including the entire schema in the prompt can lead to a significantly high token count for a single query. In such cases, consider using Amazon Bedrock to fine-tune a model, to embed product knowledge more efficiently.

About the Authors

Asaf Fried leads the Data Science team in Cato Research Labs at Cato Networks. Member of Cato Ctrl. Asaf has more than six years of both academic and industry experience in applying state-of-the-art and novel machine learning methods to the domain of networking and cybersecurity. His main research interests include asset discovery, risk assessment, and network-based attacks in enterprise environments.

Daniel Pienica is a Data Scientist at Cato Networks with a strong passion for large language models (LLMs) and machine learning (ML). With six years of experience in ML and cybersecurity, he brings a wealth of knowledge to his work. Holding an MSc in Applied Statistics, Daniel applies his analytical skills to solve complex data problems. His enthusiasm for LLMs drives him to find innovative solutions in cybersecurity. Daniel’s dedication to his field is evident in his continuous exploration of new technologies and techniques.

Sergey Volkovich is an experienced Data Scientist at Cato Networks, where he develops AI-based solutions in cybersecurity & computer networks. He completed an M.Sc. in physics at Bar-Ilan University, where he published a paper on theoretical quantum optics. Before joining Cato, he held multiple positions across diverse deep learning projects, ranging from publishing a paper on discovering new particles at the Weizmann Institute to advancing computer networks and algorithmic trading. Presently, his main area of focus is state-of-the-art natural language processing.

Omer Haim is a Senior Solutions Architect at Amazon Web Services, with over 6 years of experience dedicated to solving complex customer challenges through innovative machine learning and AI solutions. He brings deep expertise in generative AI and container technologies, and is passionate about working backwards from customer needs to deliver scalable, efficient solutions that drive business value and technological transformation.

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Businesses today deal with a reality that is increasingly complex and volatile. Companies across retail, manufacturing, healthcare, and other sectors face pressing challenges in accurate planning and forecasting. Predicting future inventory needs, setting achievable strategic goals, and budgeting effectively involve grappling with ever-changing consumer demand and global market forces. Inventory shortages, surpluses, and unmet customer expectations pose constant threats. Supply chain forecasting is critical to helping businesses tackle these uncertainties.

By using historical sales and supply data to anticipate future shifts in demand, supply chain forecasting supports executive decision-making on inventory, strategy, and budgeting. Analyzing past trends while accounting for impacts ranging from seasons to world events provides insights to guide business planning. Organizations that tap predictive capabilities to inform decisions can thrive amid fierce competition and market volatility. Overall, mastering demand predictions allows businesses to fulfill customer expectations by providing the right products at the right times.

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. We dive into Amazon SageMaker Canvas and explain how SageMaker Canvas can solve forecasting challenges for retail and consumer packaged goods (CPG) enterprises.

Introduction to Amazon SageMaker Canvas

Amazon SageMaker Canvas is a powerful no-code ML service that gives business analysts and data professionals the tools to build accurate ML models without writing a single line of code. This visual, point-and-click interface democratizes ML so users can take advantage of the power of AI for various business applications. SageMaker Canvas supports multiple ML modalities and problem types, catering to a wide range of use cases based on data types, such as tabular data (our focus in this post), computer vision, natural language processing, and document analysis. To learn more about the modalities that Amazon SageMaker Canvas supports, visit the Amazon SageMaker Canvas product page.

For time-series forecasting use cases, SageMaker Canvas uses autoML to train six algorithms on your historical time-series dataset and combines them using a stacking ensemble method to create an optimal forecasting model. The algorithms are: Convolutional Neural Network – Quantile Regression (CNN-QR), DeepAR+, Prophet, Non-Parametric Time Series (NPTS), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS). To learn more about these algorithms visit Algorithms support for time-series forecasting in the Amazon SageMaker documentation.

How Amazon SageMaker Canvas can help retail and CPG manufacturers solve their forecasting challenges

The combination of a user-friendly UI interface and automated ML technology available in SageMaker Canvas gives users the tools to efficiently build, deploy, and maintain ML models with little to no coding required. For example, business analysts who have no coding or cloud engineering expertise can quickly use Amazon SageMaker Canvas to upload their time-series data and make forecasting predictions. And this isn’t a service to be used by business analysts only. Any team at a retail or CPG company can use this service to generate forecasting data using the user-friendly UI of SageMaker Canvas.

To effectively use Amazon SageMaker Canvas for retail forecasting, customers should use their sales data for a set of SKUs for which they would like to forecast demand. It’s crucial to have data across all months of the year, considering the seasonal variation in demand in a retail environment. Additionally, it’s essential to provide a few years’ worth of data to eliminate anomalies or outliers within the data.

Retail and CPG organizations rely on industry standard methods in their approach to forecasting. One of these methods is quantiles. Quantiles in forecasting represent specific points in the predicted distribution of possible future values. They allow ML models to provide probabilistic forecasts rather than merely single point estimates. Quantiles help quantify the uncertainty in predictions by showing the range and spread of possible outcomes. Common quantiles used are the 10th, 50th (median), and 90th percentiles. For example, the 90th percentile forecast means there’s a 90% chance the actual value will be at or below that level.

By providing a probabilistic view of future demand, quantile forecasting enables retail and CPG organizations to make more informed decisions in the face of uncertainty, ultimately leading to improved operational efficiency and financial performance.

Amazon SageMaker Canvas addresses this need with ML models coupled with quantile regression. With quantile regression, you can select from a wide range of planning scenarios, which are expressed as quantiles, rather than rely on single point forecasts. It’s these quantiles that offer choice.

What do these quantiles mean? Check the following figure, which is a sample of a time-series forecasting prediction using Amazon SageMaker Canvas. The figure provides a visual of a time-series forecast with multiple outcomes, made possible through quantile regression. The red line, denoted with p05, offers a probability that the real number, whatever it may be, is expected to fall below the p05 line about 5% of the time. Conversely, this means 95% of the time the true number will likely fall above the p05 line.

Retail or CPG organizations can evaluate multiple quantile prediction points with a consideration for the over- and under-supply costs of each item to automatically select the quantile likely to provide the most profit in future periods. When necessary, you can override the selection when business rules desire a fixed quantile over a dynamic one.

To learn more about how to use quantiles for your business, check out this Beyond forecasting: The delicate balance of serving customers and growing your business.

Another powerful feature that Amazon SageMaker Canvas offers is what-if analysis, which complements quantile forecasting with the ability to interactively explore how changes in input variables affect predictions. Users can change model inputs and immediately observe how these changes impact individual predictions. This feature allows for real-time exploration of different scenarios without needing to retrain the model.

What-if analysis in SageMaker Canvas can be applied to various scenarios, such as:

• Forecasting inventory in coming months
• Predicting sales for the next quarter
• Assessing the effect of price reductions on holiday season sales
• Estimating customer footfall in stores over the next few hours

How to generate forecasts

The following example illustrates the steps to follow for users to generate forecasts from a time-series dwe use a consumer electronics dataset to forecast 5 months of sales based on current and historic demand. To download a copy of this dataset, visit .

In order to access Amazon SageMaker Canvas, you can either directly sign in using the AWS Management Console and navigate to Amazon SageMaker Canvas, or you can access Amazon SageMaker Canvas directly using single sign-on as detailed in Enable single sign-on access of Amazon SageMaker Canvas using AWS IAM Identity Center. In this post, we access Amazon SageMaker Canvas through the AWS console.

Generate forecasts

To generate forecasts, follow these steps:

1. On the Amazon SageMaker console, in the left navigation pane, choose Canvas.
2. Choose Open Canvas on the right side under Get Started, as shown in the following screenshot. If this is your first time using SageMaker Canvas, you need to create a SageMaker Canvas user by following the prompts on the screen. A new browser tab will open for the SageMaker Canvas console.

3. In the left navigation pane, choose Datasets.
4. To import your time-series dataset, choose the Import data dropdown menu and then choose Tabular, as shown in the following screenshot.

5. In Dataset name, enter a name such as Consumer_Electronics and then choose Create, as shown in the following screenshot.

6. Upload your dataset (in CSV or Parquet format) from your computer or an Amazon Simple Storage Service (Amazon S3) bucket.
7. Preview the data, then choose Create dataset, as shown in the following screenshot.

Under Status, your dataset import will show as Processing. When it shows as Complete, proceed to the next step.

8. Now that you have your dataset created and your time-series data file uploaded, create a new model to generate forecasts for your dataset. In the left navigation pane, choose My Models, then choose New model, as shown in the following screenshot.

9. In Model name, enter a name such as consumer_electronics_forecast. Under Problem type, select your use case type. Our use case is Predictive analysis, which builds models using tabular datasets for different problems, including forecasts.
10. Choose Create.

11. You will be transferred to the Build In the Target column dropdown menu, select the column where you want to generate the forecasts. This is the demand column in our dataset, as shown in the followings screenshot. After you select the target column, SageMaker Canvas will automatically select Time series forecasting as the Model type.
12. Choose Configure model.

13. A window will pop up asking you to provide more information, as shown in the following screenshot. Enter the following details:
    1. Choose the column that uniquely identifies the items in your dataset – This configuration determines how you identify your items in the datasets in a unique way. For this use case, select item_id because we’re planning to forecast sales per store.
    2. Choose a column that groups the forecast by the values in the column – If you have logical groupings of the items selected in the previous field, you can choose that feature here. We don’t have one for this use case, but examples would be state, region, country, or other groupings of stores.
    3. Choose the column that contains the time stamps – The timestamp is the feature that contains the timestamp information. SageMaker Canvas requires data timestamp in the format YYYY-MM-DD HH:mm:ss (for example, 2022-01-01 01:00:00).
    4. Specify the number of months you want to forecast into the future – SageMaker Canvas forecasts values up to the point in time specified in the timestamp field. For this use case, we will forecast values up to 5 months in the future. You may choose to enter any valid value, but be aware a higher number will impact the accuracy of predictions and also may take longer to compute.
    5. You can use a holiday schedule to improve your prediction accuracy – (Optional) You can enable Use holiday schedule and choose a relevant country if you want to learn how it helps with accuracy. However, it might not have much impact on this use case because our dataset is synthetic.

14. To change the quantiles from the default values as explained previously, in the left navigation pane, choose Forecast quantiles. In the Forecast quantiles field, enter your own values, as shown in the following screenshot.

SageMaker Canvas chooses an AutoML algorithm based on your data and then trains an ensemble model to make predictions for time-series forecasting problems. Using time-series forecasts, you can make predictions that can vary with time, such as forecasting:

• Your inventory in the coming months
• Your sales for the next months
• The effect of reducing the price on sales during the holiday season
• The number of customers entering a store in the next several hours
• How a reduction in the price of a product affects sales over a time period

If you’re not sure which forecasting algorithms to try, select all of them. To help you decide which algorithms to select, refer to Algorithms support for time-series forecasting, where you can learn more details and compare algorithms.

15. Choose Save.

Train the model

Now that the configuration is done, you can train the model. SageMaker Canvas offers two build options:

• Quick build – Builds a model in a fraction of the time compared to a standard build. Potential accuracy is exchanged for speed.
• Standard build – Builds the best model from an optimized process powered by AutoML. Speed is exchanged for greatest accuracy.

1. For this walkthrough, we choose Standard build, as shown in the following screenshot.

2. When the model training finishes, you will be routed to the Analyze There, you can find the average prediction accuracy and the column impact on prediction outcome.

Your numbers might differ from what the following screenshot shows. This is due to the stochastic nature of the ML process.

Here are explanations of what these metrics mean and how you can use them:

• wQL – The average Weighted Quantile Loss (wQL) evaluates the forecast by averaging the accuracy at the P10, P50, and P90 quantiles (unless the user has changed them). A lower value indicates a more accurate model. In our example, we used the default quantiles. If you choose quantiles with different percentiles, wQL will center on the numbers you choose.
• MAPE – Mean absolute percentage error (MAPE) is the percentage error (percent difference of the mean forecasted value compared to the actual value) averaged over all time points. A lower value indicates a more accurate model, where MAPE = 0 is a model with no errors.
• WAPE – Weighted Absolute Percent Error (WAPE) is the sum of the absolute error normalized by the sum of the absolute target, which measure the overall deviation of forecasted values from observed values. A lower value indicates a more accurate model, where WAPE = 0 is a model with no errors.
• RMSE – Root mean square error (RMSE) is the square root of the average squared errors. A lower RMSE indicates a more accurate model, where RMSE = 0 is a model with no errors.
• MASE – Mean absolute scaled error (MASE) is the mean absolute error of the forecast normalized by the mean absolute error of a simple baseline forecasting method. A lower value indicates a more accurate model, where MASE < 1 is estimated to be better than the baseline and MASE > 1 is estimated to be worse than the baseline.

You can change the default metric based on your needs. wQL is the default metric. Companies should choose a metric that aligns with their specific business goals and is straightforward for  stakeholders to interpret. The choice of metric should be driven by the specific characteristics of the demand data, the business objectives, and the interpretability requirements of stakeholders.

For instance, a high-traffic grocery store that sells perishable items requires the lowest possible wQL. This is crucial to prevent lost sales from understocking while also avoiding overstocking, which can lead to spoilage of those perishables.

It’s often recommended to evaluate multiple metrics and select the one that best aligns with the company’s forecasting goals and data patterns. For example, wQL is a robust metric that can handle intermittent demand and provide a more comprehensive evaluation of forecast accuracy across different quantiles. However, RMSE gives higher weight to larger errors due to the squaring operation, making it more sensitive to outliers.

3. Choose Predict to open the Predict

To generate forecast predictions for all the items in the dataset, select Batch prediction. To generate forecast predictions for a specific item (for example, to predict demand in real-time), select Single prediction. The following steps show how to perform both operations.

To generate forecast predictions for a specific item, follow these steps:

1. Choose Single item and select any of the items from the item dropdown list. SageMaker Canvas generates a prediction for our item, showing the average prediction (that is, demand of that item with respect to timestamp). SageMaker Canvas provides results for all upper bound, lower bound, and expected forecast.

It’s a best practice to have bounds rather than a single prediction point so that you can pick whichever fits best your use case. For example, you might want to reduce waste of resources of overstock by choosing to use the lower bound, or you might want to choose to follow the upper bound to make sure that you meet customer demand. For instance, a highly advertised item in a promotional flyer might be stocked at the 90th percentile (p90) to make sure of availability and prevent customer disappointment. On the other hand, accessories or bulky items that are less likely to drive customer traffic could be stocked at the 40th percentile (p40). It’s generally not advisable to stock below the 40th percentile, to avoid being consistently out of stock.

2. To generate the forecast prediction, select the Download prediction dropdown menu button to download the forecast prediction chart as image or forecast prediction values as CSV file.

You can use the What if scenario button to explore how changing the price will affect the demand of an item. To use this feature, you must leave empty the future dated rows with the feature you’re predicting. This dataset has empty cells for a few items, which means that this feature is enabled for them. Choose What if scenario and edit the values for the different dates to view how changing the price will affect demand. This feature helps organizations test specific scenarios without making changes to the underlying data.

To generate batch predictions on the entire dataset, follow these steps:

1. Choose All items and then choose Start Predictions. The Status will show as Generating predictions, as shown in the following screenshot.

2. When it’s complete, the Status will show as Ready, as shown in the following screenshot. Select the three-dot additional options icon and choose Preview. This will open the prediction results in a preview page.

3. Choose Download to export these results to your local computer or choose Send to Amazon QuickSight for visualization, as shown in the following screenshot.

Training time and performance

SageMaker Canvas provides efficient training times and offers valuable insights into model performance. You can inspect model accuracy, perform backtesting, and evaluate various performance metrics for the underlying models. By combining multiple algorithms in the background, SageMaker Canvas significantly reduces the time required to train models compared to training each model individually. Additionally, by using the model leaderboard dashboard, you can assess the performance of each trained algorithm against your specific time-series data, ranked based on the selected performance metric (wQL by default).

This dashboard also displays other metrics, which you can use to compare different algorithms trained on your data across various performance measures, facilitating informed decision-making and model selection.

To view the leaderboard, choose Model leaderboard, as shown in the following screenshot.

The model leaderboard shows you the different algorithms used to train your data along with their performance based on all the available metrics, as shown in the following screenshot.

Integration

Retail and (CPG) organizations often rely on applications such as inventory lifecycle management, order management systems, and business intelligence (BI) dashboards, which incorporate forecasting capabilities. In these scenarios, organizations can seamlessly integrate the SageMaker Canvas forecasting service with their existing applications, enabling them to harness the power of forecasting data. To use the forecasting data within these applications, an endpoint for the forecasting model is required. Although SageMaker Canvas models can be deployed to provide endpoints, this process may require additional effort from a machine learning operations (MLOps) perspective. Fortunately, Amazon SageMaker streamlines this process, streamlining the deployment and integration of SageMaker Canvas models.

The following steps show how you can deploy SageMaker Canvas models using SageMaker:

1. On the SageMaker console, in the left navigation pane, choose My Models.
2. Select the three-dot additional options icon next to the model you want to deploy and choose Deploy, as shown in the following screenshot.

3. Under Instance type, select the size of the instance where your model will be deployed to. Choose Deploy and wait until your deployment status changes to In service.

4. After your deployment is in service, in the left navigation pane, choose ML Ops to get your deployed model endpoint, as shown in the following screenshot. You can test your deployment or start using the endpoint in your applications.

Reproducibility and API management

It’s important to understand that Amazon SageMaker Canvas uses Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs in the AWS Machine Learning Blog.

Insights

Retail and CPG enterprises typically use visualization tools such as Amazon QuickSight or third-party software such as Tableau to understand forecast results and share them across business units. To streamline the visualization, SageMaker Canvas provides embedded visualization for exploring forecast results. For those retail and CPG enterprises who want to visualize the forecasting data in their own BI dashboard systems (such as Amazon QuickSight, Tableau, and Qlik), SageMaker Canvas forecasting models can be deployed to generate forecasting endpoints. Users can also generate a batch prediction file to Amazon QuickSight for batch prediction from the predict window as shown in the following screenshot.

The following screenshot shows the batch prediction file in QuickSight as a database that you can use for analysis

When your dataset is in Amazon QuickSight, you can start analyzing or even visualizing your data using the visualizations tools, as shown in the following screenshot.

Cost

Amazon SageMaker Canvas offers a flexible, cost-effective pricing model based on three key components: workspace instance runtime, utilization of pre-built models, and resource consumption for custom model creation and prediction generation. The billing cycle commences upon launching the SageMaker Canvas application, encompassing a range of essential tasks including data ingestion, preparation, exploration, model experimentation, and analysis of prediction and explainability results. This comprehensive approach means that users only pay for the resources they actively use, providing a transparent and efficient pricing structure. To learn more about pricing examples, check out Amazon SageMaker Canvas pricing.

Ownership and portability

More retail and CPG enterprises have embraced multi-cloud deployments for several reasons. To streamline portability of models built and trained on Amazon SageMaker Canvas to other cloud providers or on-premises environments, Amazon SageMaker Canvas provides downloadable model artifacts.

Also, several retail and CPG companies have many business units (such as merchandising, planning, or inventory management) within the organization who all use forecasting for solving different use cases. To streamline ownership of a model and facilitate straightforward sharing between business units, Amazon SageMaker Canvas now extends its Model Registry integration to timeseries forecasting models. With a single click, customers can register the ML models built on Amazon SageMaker Canvas with the SageMaker Model Registry, as shown in the following screenshot. Register a Model Version in the Amazon SageMaker Developer Guide shows you where to find the S3 bucket location where your model’s artifacts are stored.

Clean up

To avoid incurring unnecessary costs, you can delete the model you just built, then delete the dataset, and sign out of your Amazon SageMaker Canvas domain. If you also signed up for Amazon QuickSight, you can unsubscribe and remove your Amazon QuickSight account.

Conclusion

Amazon SageMaker Canvas empowers retail and CPG companies with a no-code forecasting solution. It delivers automated time-series predictions for inventory planning and demand anticipation, featuring an intuitive interface and rapid model development. With seamless integration capabilities and cost-effective insights, it enables businesses to enhance operational efficiency, meet customer expectations, and gain a competitive edge in the fast-paced retail and consumer goods markets.

We encourage you to evaluate how you can improve your forecasting capabilities using Amazon SageMaker Canvas. Use the intuitive no-code interface to analyze and improve the accuracy of your demand predictions for retail and CPG products, enhancing inventory management and operational efficiency. To get started, you can review the workshop Amazon SageMaker Canvas Immersion Day.

About the Authors

Aditya Pendyala is a Principal Solutions Architect at AWS based out of NYC. He has extensive experience in architecting cloud-based applications. He is currently working with large enterprises to help them craft highly scalable, flexible, and resilient cloud architectures, and guides them on all things cloud. He has a Master of Science degree in Computer Science from Shippensburg University and believes in the quote “When you cease to learn, you cease to grow.

Julio Hanna, an AWS Solutions Architect based in New York City, specializes in enterprise technology solutions and operational efficiency. With a career focused on driving innovation, he currently leverages Artificial Intelligence, Machine Learning, and Generative AI to help organizations navigate their digital transformation journeys. Julio’s expertise lies in harnessing cutting-edge technologies to deliver strategic value and foster innovation in enterprise environments.

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Chat-based assistants have become an invaluable tool for providing automated customer service and support. This post builds on a previous post, Integrate QnABot on AWS with ServiceNow, and explores how to build an intelligent assistant using Amazon Lex, Amazon Bedrock Knowledge Bases, and a custom ServiceNow integration to create an automated incident management support experience.

Amazon Lex is powered by the same deep learning technologies used in Alexa. With it, developers can quickly build conversational interfaces that can understand natural language, engage in realistic dialogues, and fulfill customer requests. Amazon Lex can be configured to respond to customer questions using Amazon Bedrock foundation models (FMs) to search and summarize FAQ responses. Amazon Bedrock Knowledge Bases provides the capability of amassing data sources into a repository of information. Using knowledge bases, you can effortlessly create an application that uses Retrieval Augmented Generation (RAG), a technique where the retrieval of information from data sources enhances the generation of model responses.

ServiceNow is a cloud-based platform for IT workflow management and automation. With its robust capabilities for ticketing, knowledge management, human resources (HR) services, and more, ServiceNow is already powering many enterprise service desks.

By connecting an Amazon Lex chat assistant with Amazon Bedrock Knowledge Bases and ServiceNow, companies can provide 24/7 automated support and self-service options to customers and employees. In this post, we demonstrate how to integrate Amazon Lex with Amazon Bedrock Knowledge Bases and ServiceNow.

Solution overview

The following diagram illustrates the solution architecture.

The workflow includes the following steps:

1. The ServiceNow knowledge bank is exported into Amazon Simple Storage Service (Amazon S3), which will be used as the data source for Amazon Bedrock Knowledge Bases. Data in Amazon S3 is encrypted by default. You can further enhance security by Using server-side encryption with AWS KMS keys (SSE-KMS).
2. Amazon AppFlow can be used to sync between ServiceNow and Amazon S3. Other alternatives like AWS Glue can also be used to ingest data from ServiceNow.
3. Amazon Bedrock Knowledge Bases is created with Amazon S3 as the data source and Amazon Titan (or any other model of your choice) as the embedding model.
4. When users of the Amazon Lex chat assistant ask queries, Amazon Lex fetches answers from Amazon Bedrock Knowledge Bases.
5. If the user requests a ServiceNow ticket to be created, it invokes the AWS Lambda
6. The Lambda function fetches secrets from AWS Secrets Manager and makes an HTTP call to create a ServiceNow ticket.
7. Application Auto Scaling is enabled on AWS Lambda to automatically scale Lambda according to user interactions.
8. The solution will confer with responsible AI policies and Guardrails for Amazon Bedrock will enforce organizational responsible AI policies.
9. The solution is monitored using Amazon CloudWatch, AWS CloudTrail, and Amazon GuardDuty.

Be sure to follow least privilege access policies while giving access to any system resources.

Prerequisites

The following prerequisites need to be completed before building the solution.

1. On the Amazon Bedrock console, sign up for access to the Anthropic Claude model of your choice using the instructions at Manage access to Amazon Bedrock foundation models. For information about pricing for using Amazon Bedrock, see Amazon Bedrock pricing.
2. Sign up for a ServiceNow account if you do not have one. Save your username and password. You will need to store them in AWS Secrets Manager later in this walkthrough.
3. Create a ServiceNow instance following the instructions in Integrate QnABot on AWS ServiceNow.
4. Create a user with permissions to create incidents in ServiceNow using the instructions at Create a user. Make a note of these credentials for use later in this walkthrough.

The instructions provided in this walkthrough are for demonstration purposes. Follow ServiceNow documentation to create community instances and follow their best practices.

Solution overview

To integrate Amazon Lex with Amazon Bedrock Knowledge Bases and ServiceNow, follow the steps in the next sections.

Deployment with AWS CloudFormation console

In this step, you first create the solution architecture discussed in the solution overview, except for the Amazon Lex assistant, which you will create later in the walkthrough. Complete the following steps:

1. On the CloudFormation console, verify that you are in the correct AWS Region and choose Create stack to create the CloudFormation stack.
2. Download the CloudFormation template and upload it in the Specify template Choose Next.
3. For Stack name, enter a name such as ServiceNowBedrockStack.
4. In the Parameters section, for ServiceNow details, provide the values of ServiceNow host and ServiceNow username created earlier.
5. Keep the other values as default. Under Capabilities on the last page, select I acknowledge that AWS CloudFormation might create IAM resources. Choose Submit to create the CloudFormation stack.
6. After the successful deployment of the whole stack, from the Outputs tab, make a note of the output key value BedrockKnowledgeBaseId because you will need it later during creation of the Amazon Lex assistant.

Integration of Lambda with Application Auto Scaling is beyond the scope of this post. For guidance, refer to the instructions at AWS Lambda and Application Auto Scaling.

Store the secrets in AWS Secrets Manager

Follow these steps to store your ServiceNow username and password in AWS Secrets Manager:

1. On the CloudFormation console, on the Resources tab, enter the word “secrets” to filter search results. Under Physical ID, select the console URL of the AWS Secrets Manager secret you created using the CloudFormation stack.
   
2. On the AWS Secrets Manager console, on the Overview tab, under Secret value, choose Retrieve secret value.
3. Select Edit and enter the username and password of the ServiceNow instance you created earlier. Make sure that both the username and password are correct.
   

Download knowledge articles

You need access to ServiceNow knowledge articles. Follow these steps:

1. Create a knowledge base if you don’t have one. Periodically, you may need to sync your knowledge base to keep it up to date.
2. Sync the data from ServiceNow to Amazon S3 using Amazon AppFlow by following instructions at ServiceNow. Alternatively, you can use AWS Glue to ingest data from ServiceNow to Amazon S3 by following instructions at the blog post, Extract ServiceNow data using AWS Glue Studio in an Amazon S3 data lake and analyze using Amazon Athena.
3. Download a sample article.

Sync Amazon Bedrock Knowledge Bases:

This solution uses the fully managed Knowledge Base for Amazon Bedrock to seamlessly power a RAG workflow, eliminating the need for custom integrations and data flow management. As the data source for the knowledge base, the solution uses Amazon S3. The following steps outline uploading ServiceNow articles to an S3 bucket created by a CloudFormation template.

1. On the CloudFormation console, on the Resources tab, enter “S3” to filter search results. Under Physical ID, select the URL for the S3 bucket created using the CloudFormation stack.
   
2. Upload the previously downloaded knowledge articles to this S3 bucket.

Next you need to sync the data source.

3. On the CloudFormation console, on the Outputs tab, enter “Knowledge” to filter search results. Under Value, select the console URL of the knowledge bases that you created using the CloudFormation stack. Open that URL in a new browser tab.
   
4. Scroll down to Data source and select the data source. Choose Sync.
   

You can test the knowledge base by choosing the model in the Test the knowledge base section and asking the model a question.

Responsible AI using Guardrails for Amazon Bedrock

Conversational AI applications require robust guardrails to safeguard sensitive user data, adhere to privacy regulations, enforce ethical principles, and mitigate hallucinations, fostering responsible development and deployment. Guardrails for Amazon Bedrock allow you to configure your organizational policies against the knowledge bases. They help keep your generative AI applications safe by evaluating both user inputs and model responses

To set up guardrails, follow these steps:

1. Follow the instructions at the Amazon Bedrock User Guide to create a guardrail.

You can reduce the hallucinations of the model responses by enabling grounding check and relevance check and adjusting the threshold

2. Create a version of the guardrail.
3. Select the newly created guardrail and copy the guardrail ID. You will use this ID later in the intent creation.
   

Amazon Lex setup

In this section, you configure your Amazon Lex chat assistant with intents to call Amazon Bedrock. This walkthrough uses Amazon Lex V2.

1. On the CloudFormation console, on the Outputs tab, copy the value of BedrockKnowledgeBaseId. You will need this ID later in this section.
2. On the Outputs tab, under Outputs, enter “bot” to filter search results. Choose the console URL of the Amazon Lex assistant you created using the CloudFormation stack. Open that URL in a new browser tab.
   
3. On the Amazon Lex Intents page, choose Create another intent. On the Add intent dropdown menu, choose Use built-in intent.
   
4. On the Use built-in intent screen, under Built-in intent, choose QnAIntent- Gen AI feature.
5. For Intent name, enter BedrockKb and select Add.
   
6. In the QnA configuration section, under Select model, choose Anthropic and Claude 3 Haiku or a model of your choice.
7. Expand Additional Model Settings and enter the Guardrail ID for the guardrails you created earlier. Under Guardrail Version, enter a number that corresponds to the number of versions you have created.
   
8. Enter the Knowledge base for Amazon Bedrock Id that you captured earlier in the CloudFormation outputs section. Choose Save intent at the bottom.
   

You can now add more QnAIntents pointing to different knowledge bases.

9. Return to the intents list by choosing Back to intents list in the navigation pane.
   
10. Select Build to build the assistant.
    

A green banner on the top of the page with the message Successfully built language English (US) in bot: servicenow-lex-bot indicates the Amazon Lex assistant is now ready.

Test the solution

To test the solution, follow these steps:

1. In the navigation pane, choose Aliases. Under Aliases, select TestBotAlias.
   
2. Under Languages, choose English (US). Choose Test.
   
3. A new test window will pop up in the bottom of the screen.
   
4. Enter the question “What benefits does AnyCompany offer to its employees?” Then press Enter.

The chat assistant generates a response based on the content in knowledge base.

5. To test Amazon Lex to create a ServiceNow ticket for information not present in the knowledge base, enter the question “Create a ticket for password reset” and press Enter.

The chat assistant generates a new ServiceNow ticket because this information is not available in the knowledge base.

To search for the incident, log in to the ServiceNow endpoint that you configured earlier.

Monitoring

You can use CloudWatch logs to review the performance of the assistant and to troubleshoot issues with conversations. From the CloudFormation stack that you deployed, you have already configured your Amazon Lex assistant CloudWatch log group with appropriate permissions.

To view the conversation logs from the Amazon Lex assistant, follow these directions.

On the CloudFormation console, on the Outputs tab, enter “Log” to filter search results. Under Value, choose the console URL of the CloudWatch log group that you created using the CloudFormation stack. Open that URL in a new browser tab.

To protect sensitive data, Amazon Lex obscures slot values in conversation logs. As security best practice, do not store any slot values in request or session attributes. Amazon Lex V2 doesn’t obscure the slot value in audio. You can selectively capture only text using the instructions at Selective conversation log capture.

Enable logging for Amazon Bedrock ingestion jobs

You can monitor Amazon Bedrock ingestion jobs using CloudWatch. To configure logging for an ingestion job, follow the instructions at Knowlege bases logging.

AWS CloudTrail logs

AWS CloudTrail is an AWS service that tracks actions taken by a user, role, or an AWS service. CloudTrail is enabled on your AWS account when you create the account. When activity occurs in that activity is recorded in a CloudTrail event along with other AWS service events in Event history. You can view, search, and download recent events in your AWS account. For more information, see Working with CloudTrail Event history.

As security best practice, you should monitor any access to your environment. You can configure Amazon GuardDuty to identify any unexpected and potentially unauthorized activity in your AWS environment.

Cleanup

To avoid incurring future charges, delete the resources you created. To clean up the AWS environment, use the following steps:

1. Empty the contents of the S3 bucket you created as part of the CloudFormation stack.
2. Delete the CloudFormation stack you created.

Conclusion

As customer expectations continue to evolve, embracing innovative technologies like conversational AI and knowledge management systems becomes essential for businesses to stay ahead of the curve. By implementing this integrated solution, companies can enhance operational efficiency and deliver superior service to both their customers and employees, while also adapting the responsible AI policies of the organization.

Stay up to date with the latest advancements in generative AI and start building on AWS. If you’re seeking assistance on how to begin, check out the Generative AI Innovation Center.

About the Authors

Marcelo Silva is an experienced tech professional who excels in designing, developing, and implementing cutting-edge products. Starting off his career at Cisco, Marcelo worked on various high-profile projects including deployments of the first ever carrier routing system and the successful rollout of ASR9000. His expertise extends to cloud technology, analytics, and product management, having served as senior manager for several companies such as Cisco, Cape Networks, and AWS before joining GenAI. Currently working as a Conversational AI/GenAI Product Manager, Marcelo continues to excel in delivering innovative solutions across industries.

Sujatha Dantuluri is a seasoned Senior Solutions Architect on the US federal civilian team at AWS, with over two decades of experience supporting commercial and federal government clients. Her expertise lies in architecting mission-critical solutions and working closely with customers to ensure their success. Sujatha is an accomplished public speaker, frequently sharing her insights and knowledge at industry events and conferences. She has contributed to IEEE standards and is passionate about empowering others through her engaging presentations and thought-provoking ideas.

NagaBharathi Challa is a solutions architect on the US federal civilian team at Amazon Web Services (AWS). She works closely with customers to effectively use AWS services for their mission use cases, providing architectural best practices and guidance on a wide range of services. Outside of work, she enjoys spending time with family and spreading the power of meditation.

Pranit Raje is a Cloud Architect on the AWS Professional Services India team. He specializes in DevOps, operational excellence, and automation using DevSecOps practices and infrastructure as code. Outside of work, he enjoys going on long drives with his beloved family, spending time with them, and watching movies.

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