Today, we are announcing Buy with AWS, a new way to discover and purchase solutions available in AWS Marketplace from AWS Partner sites. You can use Buy with AWS to accelerate and streamline your product procurement process on websites outside of Amazon Web Services (AWS). This feature provides you the ability to find, try, and buy solutions from Partner websites using your AWS account

AWS Marketplace is a curated digital store for you to find, buy, deploy, and manage cloud solutions from Partners. Buy with AWS is another step towards AWS Marketplace making it easy for you to find and procure the right Partner solutions, when and where you need them. You can conveniently find and procure solutions in AWS Marketplace, through integrated AWS service consoles, and now on Partner websites.

Accelerate cloud solution discovery and evaluation

You can now discover solutions from Partners available for purchase through AWS Marketplace as you explore solutions on the web beyond AWS.

Look for products that are “Available in AWS Marketplace” when browsing on Partner sites, then accelerate your evaluation process with fast access to free trials, demo requests, and inquiries for custom pricing.

For example, I want to evaluate Wiz to see how it can help with my cloud security requirements. While browsing the Wiz website, I come across a page where I see “Connect Wiz with Amazon Web Services (AWS)”.

I choose Try with AWS. It asks me to sign in to my AWS account if I’m not signed in already. I’m then presented with a Wiz and AWS co-branded page for me to sign up for the free trial.

The discovery experience that you see will vary depending on type of the Partner website you’re shopping from. Wiz is an example of how Buy with AWS can be implemented by an independent software vendor (ISV). Now, let’s look at an example of an AWS Marketplace Channel Partner, or reseller, who operates a storefront of their own.

I browse to the Bytes storefront with product listings from AWS Marketplace. I have the option to filter and search from the curated product listings, which are available in AWS Marketplace, on the Bytes site.

I choose View Details for Fortinet and see an option to Request Private Offer from AWS.

As you can tell, on a Channel Partner site, you can browse curated product listings available in AWS Marketplace, filter products, and request custom pricing directly from their website.

Streamline product procurement on AWS Partner sites
I had a seamless experience using Buy with AWS to access a free trial for Wiz and browse through the Bytes storefront to request a private offer.

Now I want to try Databricks for one of the applications I’m building. I sign up for a Databricks trial through their website.

I chose Upgrade and see Databricks is available in AWS Marketplace, which gives me the option to Buy with AWS.

I choose Buy with AWS, and after I sign in to my AWS account, I land on a Databricks and AWS Marketplace co-branded procurement page.

I complete the purchase on the co-branded procurement page and continue to set up my Databricks account.

As you can tell, I didn’t have to navigate the challenge of managing procurement processes for multiple vendors. I also didn’t have to speak with a sales representative or onboard a new vendor in my billing system, which would have required multiple approvals and delayed the overall process.

Access centralized billing and benefits through AWS Marketplace
Because Buy with AWS purchases are transacted through and managed in AWS Marketplace, you also benefit from the post-purchase experience of AWS Marketplace, including consolidated AWS billing, centralized subscription management, and access to cost optimization tools.

For example, through the AWS Billing and Cost Management console, I can centrally manage all my AWS purchases, including Buy with AWS purchases, from one dashboard. I can easily access and process invoices for all of my organization’s AWS purchases. I also need to have valid AWS Identity and Access Management (IAM) permissions to manage subscriptions and make a purchase through AWS Marketplace.

AWS Marketplace not only simplifies my billing but also helps in maintaining governance over spending by helping me manage purchasing authority and subscription access for my organization with centralized visibility and controls. I can manage my budget with pricing flexibility, cost transparency, and AWS cost management tools.

Buy with AWS for Partners
Buy with AWS enables Partners who sell or resell products in AWS Marketplace to create new solution discovery and buying experiences for customers on their own websites. By adding call to action (CTA) buttons to their websites such as “Buy with AWS”, “Try free with AWS”, “Request private offer”, and “Request demo”, Partners can help accelerate product evaluation and the path-to-purchase for customers.

By integrating with AWS Marketplace APIs, Partners can display products from the AWS Marketplace catalog, allow customers to sort and filter products, and streamline private offers. Partners implementing Buy with AWS can access AWS Marketplace creative and messaging resources for guidance on building their own web experiences. Partners who implement Buy with AWS can access metrics for insights into engagement and conversion performance.

The Buy with AWS onboarding guide in the AWS Marketplace Management Portal details how Partners can get started.

Learn more
Visit the Buy with AWS page to learn more and explore Partner sites that offer Buy with AWS.

To learn more about selling or reselling products using Buy with AWS on your website, visit:

• Buy with AWS seller page
• Buy with AWS onboarding guide in the AWS Marketplace Management Portal

– Prasad

Today, we’re announcing the general availability of Amazon SageMaker HyperPod recipes to help data scientists and developers of all skill sets to get started training and fine-tuning foundation models (FMs) in minutes with state-of-the-art performance. They can now access optimized recipes for training and fine-tuning popular publicly available FMs such as Llama 3.1 405B, Llama 3.2 90B, or Mixtral 8x22B.

At AWS re:Invent 2023, we introduced SageMaker HyperPod to reduce time to train FMs by up to 40 percent and scale across more than a thousand compute resources in parallel with preconfigured distributed training libraries. With SageMaker HyperPod, you can find the required accelerated compute resources for training, create the most optimal training plans, and run training workloads across different blocks of capacity based on the availability of compute resources.

SageMaker HyperPod recipes include a training stack tested by AWS, removing tedious work experimenting with different model configurations, eliminating weeks of iterative evaluation and testing. The recipes automate several critical steps, such as loading training datasets, applying distributed training techniques, automating checkpoints for faster recovery from faults, and managing the end-to-end training loop.

With a simple recipe change, you can seamlessly switch between GPU- or Trainium-based instances to further optimize training performance and reduce costs. You can easily run workloads in production on SageMaker HyperPod or SageMaker training jobs.

SageMaker HyperPod recipes in action
To get started, visit the SageMaker HyperPod recipes GitHub repository to browse training recipes for popular publicly available FMs.

You only need to edit straightforward recipe parameters to specify an instance type and the location of your dataset in cluster configuration, then run the recipe with a single line command to achieve state-of-art performance.

You need to edit the recipe config.yaml file to specify the model and cluster type after cloning the repository.

$ git clone --recursive https://github.com/aws/sagemaker-hyperpod-recipes.git
$ cd sagemaker-hyperpod-recipes
$ pip3 install -r requirements.txt.
$ cd ./recipes_collections
$ vim config.yaml

The recipes support SageMaker HyperPod with Slurm, SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS), and SageMaker training jobs. For example, you can set up a cluster type (Slurm orchestrator), a model name (Meta Llama 3.1 405B language model), an instance type (ml.p5.48xlarge), and your data locations, such as storing the training data, results, logs, and so on.

defaults:
- cluster: slurm # support: slurm / k8s / sm_jobs
- recipes: fine-tuning/llama/hf_llama3_405b_seq8k_gpu_qlora # name of model to be trained
debug: False # set to True to debug the launcher configuration
instance_type: ml.p5.48xlarge # or other supported cluster instances
base_results_dir: # Location(s) to store the results, checkpoints, logs etc.

You can optionally adjust model-specific training parameters in this YAML file, which outlines the optimal configuration, including the number of accelerator devices, instance type, training precision, parallelization and sharding techniques, the optimizer, and logging to monitor experiments through TensorBoard.

run:
  name: llama-405b
  results_dir: ${base_results_dir}/${.name}
  time_limit: "6-00:00:00"
restore_from_path: null
trainer:
  devices: 8
  num_nodes: 2
  accelerator: gpu
  precision: bf16
  max_steps: 50
  log_every_n_steps: 10
  ...
exp_manager:
  exp_dir: # location for TensorBoard logging
  name: helloworld 
  create_tensorboard_logger: True
  create_checkpoint_callback: True
  checkpoint_callback_params:
    ...
  auto_checkpoint: True # for automated checkpointing
use_smp: True 
distributed_backend: smddp # optimized collectives
# Start training from pretrained model
model:
  model_type: llama_v3
  train_batch_size: 4
  tensor_model_parallel_degree: 1
  expert_model_parallel_degree: 1
  # other model-specific params

To run this recipe in SageMaker HyperPod with Slurm, you must prepare the SageMaker HyperPod cluster following the cluster setup instruction.

Then, connect to the SageMaker HyperPod head node, access the Slurm controller, and copy the edited recipe. Next, you run a helper file to generate a Slurm submission script for the job that you can use for a dry run to inspect the content before starting the training job.

$ python3 main.py --config-path recipes_collection --config-name=config

After training completion, the trained model is automatically saved to your assigned data location.

To run this recipe on SageMaker HyperPod with Amazon EKS, clone the recipe from the GitHub repository, install the requirements, and edit the recipe (cluster: k8s) on your laptop. Then, create a link between your laptop and running the EKS cluster and subsequently use the HyperPod Command Line Interface (CLI) to run the recipe.

$ hyperpod start-job –recipe fine-tuning/llama/hf_llama3_405b_seq8k_gpu_qlora 
--persistent-volume-claims fsx-claim:data 
--override-parameters 
'{
  "recipes.run.name": "hf-llama3-405b-seq8k-gpu-qlora",
  "recipes.exp_manager.exp_dir": "/data/<your_exp_dir>",
  "cluster": "k8s",
  "cluster_type": "k8s",
  "container": "658645717510.dkr.ecr.<region>.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121",
  "recipes.model.data.train_dir": "<your_train_data_dir>",
  "recipes.model.data.val_dir": "<your_val_data_dir>",
}'

You can also run recipe on SageMaker training jobs using SageMaker Python SDK. The following example is running PyTorch training scripts on SageMaker training jobs with overriding training recipes.

...
recipe_overrides = {
    "run": {
        "results_dir": "/opt/ml/model",
    },
    "exp_manager": {
        "exp_dir": "",
        "explicit_log_dir": "/opt/ml/output/tensorboard",
        "checkpoint_dir": "/opt/ml/checkpoints",
    },   
    "model": {
        "data": {
            "train_dir": "/opt/ml/input/data/train",
            "val_dir": "/opt/ml/input/data/val",
        },
    },
}
pytorch_estimator = PyTorch(
           output_path=<output_path>,
           base_job_name=f"llama-recipe",
           role=<role>,
           instance_type="p5.48xlarge",
           training_recipe="fine-tuning/llama/hf_llama3_405b_seq8k_gpu_qlora",
           recipe_overrides=recipe_overrides,
           sagemaker_session=sagemaker_session,
           tensorboard_output_config=tensorboard_output_config,
)
...

As training progresses, the model checkpoints are stored on Amazon Simple Storage Service (Amazon S3) with the fully automated checkpointing capability, enabling faster recovery from training faults and instance restarts.

Now available
Amazon SageMaker HyperPod recipes are now available in the SageMaker HyperPod recipes GitHub repository. To learn more, visit the SageMaker HyperPod product page and the Amazon SageMaker AI Developer Guide.

Give SageMaker HyperPod recipes a try and send feedback to AWS re:Post for SageMaker or through your usual AWS Support contacts.

— Channy

Building on the work that we and our partners have been doing for many years, Amazon is committing up to $100 million in cloud technology and technical resources to help existing, dedicated learning organizations reach more learners by creating new and innovative digital learning solutions, all as part of the AWS Education Equity Initiative.

The Work So Far
AWS and Amazon have a long-standing commitment to learning and education. Here’s a sampling of what we have already done:

AWS AI & ML Scholarship Program – This program has awarded $28 million in scholarships to approximately 6000 students.

Machine Learning University – MLU offers a free program helping community colleges and Historically Black Colleges and Universities (HBCUs) teach data management, artificial intelligence, and machine learning concepts. The program is designed to address opportunity gaps by supporting students who are historically underserved and underrepresented in technology disciplines.

Amazon Future Engineer – Since 2021, up to $46 million in scholarships has been awarded to 1150 students through this program. In the past year, more than 2.1 million students received over 17 million hours of STEM education, literacy, and career exploration courses through this and other Amazon philanthropic education programs in the United States. I was able to speak to one such session last year and it was an amazing experience:

Free Cloud Training – In late 2020 we set a goal of helping 29 million people grow their tech skills with free cloud computing training by 2025. We worked hard and met that target a year ahead of time!

There’s More To Do
Despite all of this work and progress, there’s still more to be done. The future is definitely not evenly distributed: over half a billion students cannot be reached by digital learning today.

We believe that Generative AI can amplify the good work that socially-minded edtech organizations, non-profits, and governments are already doing. Our goal is to empower them to build new and innovative digital learning systems that can amplify their work and allow them to reach a bigger audience.

With the launch of the AWS Education Equity Initiative, we want to help pave the way for the next generation of technology pioneers as they build powerful tools, train foundation models at scale, and create AI-powered teaching assistants.

We are committing up to $100 million in cloud technology and comprehensive technical advising over the next five years. The awardees will have access to the portfolio of AWS services and technical expertise so that they can build and scale learning management systems, mobile apps, chatbots, and other digital learning tools. As part of the application process, applicants will be asked to demonstrate how their proposed solution will benefit students from underserved and underrepresented communities.

As I mentioned earlier, our partners are already doing a lot of great work in this area. For example:

Code.org has already used AWS to scale their free computer science curriculum to millions of students in more than 100 countries. With this initiative, they will expand their use of Amazon Bedrock to provide an automated assessment of student projects, freeing up educator time that can be use for individual instruction and tailored learning.

Rocket Learning focuses on early childhood education in India. They will use Amazon Q in QuickSight to enhance learning outcomes for more than three million children.

I’m super excited about this initiative and look forward to seeing how it will help to create and educate the next generation of technology pioneers!

— Jeff;

Today, we announced a new capability of Amazon Q in QuickSight that helps users perform scenario analyses to find answers to complex problems quickly. This AI-assisted data analysis experience helps business users find answers to complex problems by guiding them step-by-step through in-depth data analysis—suggesting analytical approaches, automatically analyzing data, and summarizing findings with suggested actions—using natural language prompts. This new capability eliminates hours of tedious and error-prone manual work traditionally required to perform analyses using spreadsheets or other alternatives. In fact, Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets. This capability expands upon existing data Q&A capabilities of Amazon QuickSight so business professionals can start their analysis by simply asking a question.

How it works
Business users are often faced with complex questions that have traditionally required specialized training and days or weeks of time analyzing data in spreadsheets or other tools to address. For example, let’s say you’re a franchisee with multiple locations to manage. You might use this new capability in Amazon Q in QuickSight to ask, “How can I help our new Chicago store perform as well as the flagship store in New York?” Using an agentic approach, Amazon Q would then suggest analytical approaches needed to address the underlying business goal, automatically analyze data, and present results complete with visualizations and suggested actions. You can conduct this multistep analysis in an expansive analysis canvas, giving you the flexibility to make changes, explore multiple analysis paths simultaneously, and adapt to situations over time.

This new analysis experience is part of Amazon QuickSight meaning it can read from QuickSight dashboards which connect to sources such as Amazon Athena, Amazon Aurora, Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service. Specifically, this new experience is part of Amazon Q in QuickSight, which allows it to seamlessly integrate with other generative business intelligence (BI) capabilities such as data Q&A. You can also upload either a .csv or a single-table, single-sheet .xlsx file to incorporate into your analysis.

Here’s a visual walkthrough of this new analysis experience in Amazon Q in QuickSight.

I’m planning a customer event, and I’ve received an Excel spreadsheet of all who’ve registered to attend the event. I want to learn more about the attendees, so I analyze the spreadsheet and ask a few questions. I start by describing what I want to explore.

I upload the spreadsheet to start my analysis. Firstly, I want to understand how many people have registered for the event.

To design an agenda that’s suitable for the audience, I want to understand the various roles that will be attending. I select on the + icon to add a new block for asking a question following along the thread from the previous block.

I can continue to ask more questions. However, there are suggested questions for analyzing my data even further, and I now select one of these suggested questions. I want to increase marketing efforts at companies that don’t currently have a lot of attendees in this case, companies with fewer than two attendees.

Amazon Q executes the required analysis and keeps me updated of the progress. Step 1 of the process identifies companies that have fewer than two attendees and lists them.

Step 2 gives an estimate of how many more attendees I might get from each company if marketing efforts are increased.

In Step 3 I can see the potential increase in total attendees (including the percentage increase) in line with the increase in marketing efforts.

Lastly, Step 4 goes even further to highlight companies I should prioritize for these increased marketing efforts.

To increase the potential number of attendees even more, I wanted to change the analysis to identify companies with fewer than three attendees instead of two attendees. I choose the AI sparkle icon in the upper right to launch a modal that I then use to provide more context and make specific changes to the previous result.

This change resulted in new projections, and I can choose to consider them for my marketing efforts or keep to the previous projections.

Now available
Amazon Q in QuickSight Pro users can use this new capability in preview in the following AWS Regions at launch: US East (N. Virginia) and US West (Oregon). Get started with a free 30-day trial of QuickSight today. To learn more, visit the Amazon QuickSight User Guide. You can submit your questions to AWS re:Post for Amazon QuickSight, or through your usual AWS Support contacts.

– Veliswa.