Fully managed batch processing
AWS Batch is a fully managed batch computing service that plans, schedules, and runs your containerized batch ML, simulation, and analytics workloads across the full range of AWS compute offerings, such as Amazon ECS, Amazon EKS, AWS Fargate, and Spot or On-Demand Instances.
Benefits of AWS Batch
Focus on analyzing results, not managing infrastructure
Native integration with AWS
Optimize compute costs
Scale compute resources automatically
How AWS Batch can work for your industry
AWS Batch lets developers, scientists, and engineers across industries efficiently run hundreds of thousands of batch computing jobs while optimizing compute resources, providing the ability to focus on analyzing results and solving problems.
AV/ADAS feature development
Automotive companies rely on simulations when developing and testing autonomous vehicles (AV) and advanced driver assistance systems (ADAS). Engineers model each element of a simulation (vehicle sensors, traffic, 3D environment) into smaller, modular components using containers. With the ability to run multi-container jobs with AWS Batch, you benefit from the advanced scaling, scheduling, and cost optimization capabilities of AWS Batch without rebuilding your system into a complex monolithic container. Instead, you can use multiple smaller, modular containers that each represent different system components. This capability accelerates development times by reducing job preparation steps, eliminates the need to build extra in-house tools, and simplifies software development (Dev) and IT operations (Ops) and debugging.
High performance computing, post-trade analytics, and fraud surveillance
Financial services organizations, ranging from fintech startups to established enterprises, use AWS Batch to streamline operations, minimize errors, and enhance speed, accuracy, and cost efficiency through automation. For high performance computing workloads like pricing, market analysis, and risk management, AWS Batch can automate the resourcing and scheduling of these jobs to save costs and accelerate decision-making. For post-trade analytics, AWS Batch can automate the end-of-day processing of large data sets from multiple sources so that you can understand the pertinent risks going into the next day’s trading cycle. To better detect fraud, you can use AWS machine learning in conjunction with AWS Batch to automate the analysis required to detect irregular patterns in data.
Drug screening and DNA sequencing
Biopharmaceutical and genomics companies rely on high performance computing to bring products to market. AWS Batch streamlines operations across applications like computational chemistry, clinical modeling, molecular dynamics, and genomic sequencing testing and analysis. In drug screening, AWS Batch enables research scientists to efficiently search libraries of small molecules to identify which are most likely to bind to a drug target, typically a protein receptor or enzyme. This process aides in drug design, potentially leading to the development of more effective drugs and therapies. For DNA sequencing, after bioinformaticians complete their primary analysis of a genomic sequence to produce raw files, they can use AWS Batch to automate and reduce errors their secondary analysis involving assembling the raw DNA reads into a complete genomic sequence.
Rendering, transcoding, and media supply chain
Media and entertainment companies rely on highly scalable batch computing for efficient data processing and content creation. AWS Batch accelerates content creation, dynamically scales media packaging, and automates asynchronous media supply chain workflows. Content producers and post-production houses can use AWS Batch to automate content rendering, reducing the need for human intervention. For batch and file-based transcoding, AWS Batch can automate workflows, overcome resource bottlenecks, and reduce the number of manual processes. AWS Batch also simplifies complex media supply chain workflows by coordinating the execution of disparate and dependent jobs at different stages of processing and supports a common content preparation framework across teams.
Test complex systems using simulation
Run simulations at scale when testing complex systems like those used in robotics, autonomous vehicles, and advanced driver assistance systems (ADAS).
Run financial services analyses
Automate analyses of the day’s transaction costs, completion reports, and market performance.
Screen for drugs and sequence genomes
Rapidly search libraries of small molecules to capture better data for drug design.
Render visual effects
Automate content-rendering workloads and reduce the need for human intervention due to dependencies.
Train ML models
Efficiently run compute-intense ML model training and inference at any scale.