Best AI Infrastructure Platforms for Amazon SageMaker

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

Amazon SageMaker lets you identify raw data, such as images, text files and videos. You can also add descriptive labels to generate synthetic data and create high-quality training data sets to support your machine learning (ML). SageMaker has two options: Amazon SageMaker Ground Truth Plus or Amazon SageMaker Ground Truth. These options allow you to either use an expert workforce or create and manage your data labeling workflows. data labeling. SageMaker GroundTruth allows you to manage and create your data labeling workflows. SageMaker Ground Truth, a data labeling tool, makes data labeling simple. It also allows you to use human annotators via Amazon Mechanical Turk or third-party providers.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

Optimize ML models with real-time training metrics capture and alerting when anomalies are detected. To reduce the time and costs of training ML models, stop training when the desired accuracy has been achieved. To continuously improve resource utilization, automatically profile and monitor the system's resource utilization. Amazon SageMaker Debugger reduces troubleshooting time from days to minutes. It automatically detects and alerts you when there are common errors in training, such as too large or too small gradient values. You can view alerts in Amazon SageMaker Studio, or configure them through Amazon CloudWatch. The SageMaker Debugger SDK allows you to automatically detect new types of model-specific errors like data sampling, hyperparameter value, and out-of bound values.

Amazon SageMaker Model training reduces the time and costs of training and tuning machine learning (ML), models at scale, without the need for infrastructure management. SageMaker automatically scales infrastructure up or down from one to thousands of GPUs. This allows you to take advantage of the most performant ML compute infrastructure available. You can control your training costs better because you only pay for what you use. SageMaker distributed libraries can automatically split large models across AWS GPU instances. You can also use third-party libraries like DeepSpeed, Horovod or Megatron to speed up deep learning models. You can efficiently manage your system resources using a variety of GPUs and CPUs, including P4d.24xl instances. These are the fastest training instances available in the cloud. Simply specify the location of the data and indicate the type of SageMaker instances to get started.

Amazon SageMaker offers all the tools and libraries needed to build ML models. It allows you to iteratively test different algorithms and evaluate their accuracy to determine the best one for you. Amazon SageMaker allows you to choose from over 15 algorithms that have been optimized for SageMaker. You can also access over 150 pre-built models available from popular model zoos with just a few clicks. SageMaker offers a variety model-building tools, including RStudio and Amazon SageMaker Studio Notebooks. These allow you to run ML models on a small scale and view reports on their performance. This allows you to create high-quality working prototypes. Amazon SageMaker Studio Notebooks make it easier to build ML models and collaborate with your team. Amazon SageMaker Studio notebooks allow you to start working in seconds with Jupyter notebooks. Amazon SageMaker allows for one-click sharing of notebooks.

Amazon SageMaker Studio Lab provides a free environment for machine learning (ML), which includes storage up to 15GB and security. Anyone can use it to learn and experiment with ML. You only need a valid email address to get started. You don't have to set up infrastructure, manage access or even sign-up for an AWS account. SageMaker Studio Lab enables model building via GitHub integration. It comes preconfigured and includes the most popular ML tools and frameworks to get you started right away. SageMaker Studio Lab automatically saves all your work, so you don’t have to restart between sessions. It's as simple as closing your computer and returning later. Machine learning development environment free of charge that offers computing, storage, security, and the ability to learn and experiment using ML. Integration with GitHub and preconfigured to work immediately with the most popular ML frameworks, tools, and libraries.

SageMaker Edge Agent allows for you to capture metadata and data based on triggers you set. This allows you to retrain existing models with real-world data, or create new models. This data can also be used for your own analysis such as model drift analysis. There are three options available for deployment. GGv2 (size 100MB) is an integrated AWS IoT deployment method. SageMaker Edge has a smaller, built-in deployment option for customers with limited device capacities. Customers who prefer a third-party deployment mechanism can plug into our user flow. Amazon SageMaker Edge Manager offers a dashboard that allows you to see the performance of all models across your fleet. The dashboard allows you to visually assess your fleet health and identify problematic models using a dashboard within the console.

Amazon SageMaker Clarify is a machine learning (ML), development tool that provides purpose-built tools to help them gain more insight into their ML training data. SageMaker Clarify measures and detects potential bias using a variety metrics so that ML developers can address bias and explain model predictions. SageMaker Clarify detects potential bias in data preparation, model training, and in your model. You can, for example, check for bias due to age in your data or in your model. A detailed report will quantify the different types of possible bias. SageMaker Clarify also offers feature importance scores that allow you to explain how SageMaker Clarify makes predictions and generates explainability reports in bulk. These reports can be used to support internal or customer presentations and to identify potential problems with your model.

Amazon SageMaker JumpStart can help you speed up your machine learning (ML). SageMaker JumpStart gives you access to pre-trained foundation models, pre-trained algorithms, and built-in algorithms to help you with tasks like article summarization or image generation. You can also access prebuilt solutions to common problems. You can also share ML artifacts within your organization, including notebooks and ML models, to speed up ML model building. SageMaker JumpStart offers hundreds of pre-trained models from model hubs such as TensorFlow Hub and PyTorch Hub. SageMaker Python SDK allows you to access the built-in algorithms. The built-in algorithms can be used to perform common ML tasks such as data classifications (images, text, tabular), and sentiment analysis.

Amazon SageMaker Autopilot takes out the tedious work of building ML models. SageMaker Autopilot simply needs a tabular data set and the target column to predict. It will then automatically search for the best model by using different solutions. The model can then be directly deployed to production in one click. You can also iterate on the suggested solutions to further improve its quality. Even if you don't have the correct data, Amazon SageMaker Autopilot can still be used. SageMaker Autopilot fills in missing data, provides statistical insights on columns in your dataset, extracts information from non-numeric column, such as date/time information from timestamps, and automatically fills in any gaps.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

Distributed workflows for production and prototype that are event-driven and span AI models, databases, APIs, ETL systems and applications. All on one platform. Reduce operational overheads and infrastructure complexity to enable a faster time-to-value for your organization. Focus on investing in proprietary logical and accelerating feature deliveries without wasting time with platform and architecture choices that slow down development and execution. Revolutionize emergency response through real-time transcription, keyword identification and keyphrase recognition, and integrated connectivity with external systems. Connect the physical and digital realms and optimize maintenance by monitoring sensors, creating a triage for operator review after an alert and creating service tickets on your work order platform. By generating responses based on data from various platforms, you can apply past experience to current problems in new ways.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

Deep Learning Containers are Docker images pre-installed with the most popular deep learning frameworks. Deep Learning Containers allow you to quickly deploy custom ML environments without the need to build and optimize them from scratch. You can quickly deploy deep learning environments using prepackaged, fully tested Docker images. Integrate Amazon SageMaker, Amazon EKS and Amazon ECS to create custom ML workflows that can be used for validation, training, and deployment.