This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. The diagram shows several accounts and personas as part of the overall infrastructure. The following diagram gives a high-level illustration of the use case.
SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).
Kate Nasser, The People Skills Coach™ , is a smart, energizing, experienced speaker, coach, and workshop leader. The most challenging people skill to learn and use seems to be replacing defensive reactions with simple accountability. To engage Kate Nasser’s keynotes, workshops, and coaching, visit her blog.
In the preceding architecture diagram, AWS WAF is integrated with Amazon API Gateway to filter incoming traffic, blocking unintended requests and protecting applications from threats like SQL injection, cross-site scripting (XSS), and DoS attacks. Learn more about building generative AI applications with AWS Workshops for Bedrock.
Prerequisites To follow along and set up this solution, you must have the following: An AWS account A device with access to your AWS account with the following: Python 3.12 Create an S3 bucket in your account. You can also complete these steps by running the script cognito-create-testuser.sh installed Node.js
Reusable scaling scripts for rapid experimentation – HyperPod offers a set of scalable and reusable scripts that simplify the process of launching multiple training runs. In your account, you will have a VPC provisioned with a public and private subnet, and an S3 bucket synced to your FSxL file system via a data repository link.
During a 1-day workshop, we were able to set up a distributed training configuration based on SageMaker within KT’s AWS account, accelerate KT’s training scripts using the SageMaker Distributed Data Parallel (DDP) library, and even test a training job using two ml.p4d.24xlarge 24xlarge instances. region_name}.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker'
Every customer reflexively answered "Fine," until one customer changed the script. I was an account manager for a uniform company. I was working as a contract trainer, facilitating onsite workshops for clients on behalf of the company that hired me. I'm terrible!" All I could muster in response was stunned silence.
However, complex NLQs, such as time series data processing, multi-level aggregation, and pivot or joint table operations, may yield inconsistent Python script accuracy with a zero-shot prompt. For details, refer to Step 1: Create your AWS account. or higher installed on Linux, Mac, or Windows Subsystem for Linux, and an AWS account.
To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts. For more details on how to create HyperPod clusters, refer to Getting started with SageMaker HyperPod and the HyperPod workshop. strip(), pysss.password().AES_256))"
If not, refer to Getting started with SageMaker HyperPod and the HyperPod workshop for guidance on creating and configuring your cluster. To deploy a new VPC and subnets, follow the instructions in the Own Account section of the HyperPod workshop. If you followed the HyperPod workshop instructions, the CIDR range is 10.0.0.0/16.
shell script to automate provisioning of the parameterized AWS CloudFormation template, bedrock-customer-resources.yml , to deploy the following resources: An Amazon DynamoDB table populated with synthetic claims data. shell script to deploy the emulated customer resources defined in the bedrock-insurance-agent.yml CloudFormation template.
“All our workshops and training sessions now had to be provided online. All these manual work along with misdialing, excessive waiting time, and call drop accounted for a 27% decline in their efficiency. Agents could easily take notes while on call or read out call scripts. Thanks to JustCall. Call Monitoring.
SageMaker Domain Lifecycle Configuration to automatically shut down idle Studio notebooks Lifecycle Configurations are shell scripts triggered by Amazon SageMaker Studio lifecycle events, such as starting a new Studio notebook. This Terraform solution creates a KMS key and uses it to encrypt SageMaker Studio’s EFS volume.
To replicate the results reported in this post, the only prerequisite is an AWS account. In this account, we create an EKS cluster and an Amazon FSx for Lustre file system. We also push container images to an Amazon Elastic Container Registry (Amazon ECR) repository in the account. script in the fsx folder.
Each stage in the ML workflow is broken into discrete steps, with its own script that takes input and output parameters. In the following code, the desired number of actors is passed in as an input argument to the script. Let’s look at sections of the scripts that perform this data preprocessing. get("OfflineStoreConfig").get("S3StorageConfig").get("ResolvedOutputS3Uri")
Learn more about prompt engineering and generative AI-powered Q&A in the Amazon Bedrock Workshop. Account Manager with nearly two decades of experience in the technology industry, specializing in sales and data center strategy. For more details see the OpenSearch documentation on structuring a search query.
Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. Save model: This step creates a model from the trained model artifacts.
You will see how you can continue using open-source libraries in your deep learning training script and still make it compatible to run on both Kubernetes and SageMaker in a platform agnostic way. For this walkthrough, you should have the following prerequisites: An AWS account. How does Kubeflow on AWS and SageMaker help? followed by./run.sh.
Additionally, to follow along with this post, you should have an AWS account and familiarity with Neptune, the Gremlin query language, and SageMaker. Now that you’ve seen the structure of the solution, you can deploy it into your account to run an example workflow. The script creates the CSVs to load the data into Neptune.
Run your DLC container with a model training script to fine-tune the RoBERTa model. After model training is complete, package the saved model, inference scripts, and a few metadata files into a tar file that SageMaker inference can use and upload the model package to an Amazon Simple Storage Service (Amazon S3) bucket.
User Query Session Attributes Session prompt Attributes Expected Response API, Knowledge Bases and Guardrails invoked What is my account balance? None None Could you please provide the number of the account that you would like to check the balance for? None What is the balance for the account 1234?
The CloudFormation scripts are typically run as a set of nested stacks in a production environment. To go through this walkthrough, you need an AWS account where the solution will be deployed. The L4MLiveDetectorAlert.yaml CloudFormation script creates the Lookout for Metrics anomaly detector alert with an SMS target.
Cold Call Scripting . This is why the practice of preparing cold call scripts comes in handy. Mapping out a cold call script helps sales reps deal with prospects more confidently. A half-baked, raw sales script is not going to cut the deal. Train your sales representatives on how to frame scripts accordingly.
Nobody expects things to run perfectly all the time, but they do expect accountability. Run workshops or video tutorials to get them comfortable, and always provide hands-on practice. Clear communication about delays demonstrates you value your customers time and are willing to take ownership of any hiccups. Updating your chatbot?
This VPC doesn’t appear in the customer account. This VPC doesn’t appear in the customer account. The nodes connect to the EKS control plane through a cross-account elastic network interface (ENI). You can find the Container Insights set up guide in Amazon EKS Support in Amazon SageMaker HyperPod Workshop.
The workflow for instantiating the solution presented in this post in your own AWS account is as follows: Run the CloudFormation template provided with this post in your account. Prerequisites To implement the solution provided in this post, you should have an AWS account and familiarity with LLMs, OpenSearch Service and SageMaker.
Check out our workshop lab for more information about the toxicity detection model using an Amazon Comprehend custom classifier. JumpStart makes fine-tuning the Stable Diffusion Model easy by providing the transfer learning scripts using the DreamBooth method.
These models can be fine-tuned and deployed to endpoints under your AWS account, giving you full ownership of model weights and script codes. Resources For the full script discussed in this post and some sample data, refer to the GitHub repo.
As we will see, the main goal of a self-service system is to reduce the time the agents spend on simple, repetitive tasks or transactions such as paying a bill or getting account balance information. For example, clients don’t need to go through a lengthy account verification process with an agent on the phone to check their balance.
As we will see, the main goal of a self-service system is to reduce the time the agents are spending on simple, repetitive tasks or transactions such as paying a bill or getting account balance information. Who would have thought that a self-service setup could also contribute to the well-being of your HR department? What About the Clients?
How Generative AI Is Changing Customer Support From Rule-Based Chatbots to NLP and LLMs Traditional chatbots used to rely on fixed rules and preprogrammed scripts when interacting with customers. Proactive service : AI can offer product suggestions or account tips based on previous behavior or common problems.
How Generative AI Is Changing Customer Support From Rule-Based Chatbots to NLP and LLMs Traditional chatbots used to rely on fixed rules and preprogrammed scripts when interacting with customers. Proactive service : AI can offer product suggestions or account tips based on previous behavior or common problems.
Effective change management needs to take this change fatigue into account and build a plan around the reality of our times, especially now that we’re entering the third year of the pandemic. For example, they recommend a specific workshop strategy to cascade change through the organization. Overcoming change fatigue.
Day 3 is practicing the scripts on the phones by calling me or my team. Call recording has been instrumental in not only holding the agents accountable but also for training purposes. You can thus optimize the workshops or trainings for the newly joined agents. Day 2 is software - learn the CRM, Phone system, Tracing Program.
Agents could easily take notes while on call or read out call scripts. agents were able to solve frequent queries around timings of online classes, fees for online workshops, registration for online tests, details of online practicals, and so on. In such cases, agents’ accountability became a big concern for the company.
A leader isn’t looking for consistency in process, protocol, script, and activities. A leader isn’t looking for consistency in process, protocol, script, and activities. KAUFMAN: This is a terrific question, because a leader isn’t looking for consistency in process, protocol, script, and activities.
When a call gets through, after confirming it’s a customer (and not a machine), the software connects it to the next available agent — presenting the agent with all the necessary information, such as contact name, history, and account details. Depending on its fixed parameters.
A leader isn’t looking for consistency in process, protocol, script, and activities. A leader isn’t looking for consistency in process, protocol, script, and activities. RON KAUFMAN: This is a terrific question, because a leader isn’t looking for consistency in process, protocol, script, and activities.
When participants leave sales training, there must be a structured follow-up process that ensures: Accountability for real-world application Repetition Feedback on progress Positive reinforcement and supportive coaching For sales training and skills to turn into a way of doing business, they have to be hard-wired into the culture.
Example 2: Health Insurance Portability and Accountability Act (HIPAA) For call centers dealing with healthcare information, maintaining compliance with HIPAA is a major challenge. Watch our free, on-demand workshop about How to Boost Outbound Efficiency While Remaining TCPA Compliant.
We have experienced big workshops and initiatives, and then, poof, they're gone. It's got to really move from the corporate workshop to whoever you are working directly for. Jeanne Bliss: Then nothing, yeah. I think what happens, Tom, is that it's that. You've got. If you have to be. You have to try to go through onboarding.
It provides your team with the ‘how-to’ on promoting relevant products or services in an account. Build trust and consistency, and account expansion will naturally follow. Collaborate Across Teams: Account expansions cannot be the sole responsibility of the account management or the customer success team.
Good scripting can lessen the amount of decision making, but another way to counteract. What I liked the most are the seminars and workshops we did all year round. One of the workshops I attended was about identifying the type of trainee you’re dealing with so that you can customize their learning. Get your team talking…”.
We organize all of the trending information in your field so you don't have to. Join 34,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content