List of AWS SDK for Python (Boto3) Customers
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Since 2010, our global team of researchers has been studying AWS SDK for Python (Boto3) customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased AWS SDK for Python (Boto3) for API Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using AWS SDK for Python (Boto3) for API Management include: AI21 Labs, a Israel based Professional Services organisation with 230 employees and revenues of $15.0 million, Falkonry, a United States based Professional Services organisation with 70 employees and revenues of $11.0 million and many others.
Contact us if you need a completed and verified list of companies using AWS SDK for Python (Boto3), including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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AI21 Labs | Professional Services | 230 | $15M | Israel | Amazon Web Services (AWS) | AWS SDK for Python (Boto3) | API Management | 2021 | n/a | In 2021, AI21 Labs implemented the AWS SDK for Python (Boto3) within its API Management stack to orchestrate GPU compute for large scale model training on Amazon Web Services. The work began with bringing up the code base on Amazon EC2 P4d Instances activated for Elastic Fabric Adapter EFA, and included an explicit verification phase to test multinode scaling and performance of distributed training workflows. The implementation used an in house orchestration layer that allocates EC2 capacity through the AWS SDK for Python (Boto3), coupling instance provisioning with job scheduling and distributed training orchestration. Functional capabilities implemented include instance lifecycle control, multinode training coordination, and quick validation runs of full size models that span hundreds of GPUs to verify function and performance prior to longer training campaigns. Integrations are centered on EC2 P4d Instances and EFA networking for low latency GPU interconnect, with the AWS SDK for Python (Boto3) providing the programmatic interface to AWS services used in the training pipeline. Operational coverage focused on model engineering and training workflows, enabling Jurassic-1 Jumbo training on AWS once performance and scaling were validated. Governance and rollout followed a staged approach, moving from initial environment bring up to quick full size verification runs, and then to sustained training of the production scale model. The documented outcomes from the verification steps were confirmation of efficient scaling and functional readiness, which supported commencing training of the Jurassic-1 Jumbo model on AWS. | |
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Falkonry | Professional Services | 70 | $11M | United States | Amazon Web Services (AWS) | AWS SDK for Python (Boto3) | API Management | 2022 | n/a | In 2022, Falkonry implemented the AWS SDK for Python (Boto3) to support API Management of cloud native machine learning pipelines. The deployment centers on programmatic control of pipeline artifacts, data movement, and experiment orchestration using the AWS SDK for Python (Boto3) as the primary API integration layer. Implementation work focused on embedding classification and anomaly detection model workflows, geographical density estimation, and predictive object tracking within a Python driven stack. Functional capabilities include automated data engineering scripts, hyper parameter tuning workflows, model training orchestration for experimental neural network models, and generation of repeatable reports for customer facing presentations. Integrations explicitly include Amazon S3 for data storage, Rancher for container orchestration, and Docker for container packaging, with boto3 used to script and manage interactions with AWS services. Operational coverage is centered in Cupertino, CA with development activity documented from November 2021 to present, and the implementation is applied across Falkonry professional services engagements that require iterative model development and customer collaboration. Governance and process changes emphasize automation of routine engineering tasks, standardization of data ingest and reporting pipelines, and programmatic orchestration of model experiments using the AWS SDK for Python (Boto3). The narrative reflects an API Management oriented approach that centralizes programmatic access, reproducible workflows, and developer driven control of the machine learning lifecycle. |
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