AWS Partner Network (APN) Blog

Tag: Python


Optimize Your Business Document Storage and Signature Workflow with Dropbox Solutions

As companies realize the need to remove unnecessary friction caused by outdated contracting and agreements, they are looking for ways to prepare, send, sign, and track eSignatures effortlessly and increase signature completion rates within document processing. This post walks through how HelloSign, Dropbox’s eSignature software, empowers businesses to send and sign agreements faster through a secure and reliable approach.

How KNIME Users Can Build Intelligent Workflows By Accessing AWS Services Through Boto3 SDK Integration

To quickly build intelligent data-driven workflows, organizations need business analysts to work with data scientists and development teams to unlock useful insights from unstructured or semi-structured data. Learn how KNIME’s end-to-end data science product portfolio helps bridge the gap between the ideation and productionalization steps of data science projects, while also assisting in the communication of key data science aspects between teams.


Signing Data Using Keys Stored in AWS CloudHSM with Python

AWS CloudHSM enables you to generate and use your own encryption keys on AWS. The standard service for managing keys for signing would usually be AWS KMS, but due to legacy requirements from the customer side the team at BJSS needed to support both SHA256 and SHA1. Learn how BJSS successfully signed some data with a key from AWS CloudHSM using Python, and walk through the setup of an AWS CloudHSM cluster for testing using a sample application.

How to Export a Model from Domino for Deployment in Amazon SageMaker

Data science is driving significant value for many organizations, including fueling new revenue streams, improving longstanding processes, and optimizing customer experience. Domino Data Lab empowers code-first data science teams to overcome these challenges of building and deploying data science at scale. Learn how to build and export a model from the Domino platform for deployment in Amazon SageMaker. Deploying models within Domino provides insight into the full model lineage.

Machine Learning-4

Using Fewer Resources to Run Deep Learning Inference on Intel FPGA Edge Devices

Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.


How Slalom and WordStream Used MLOps to Unify Machine Learning and DevOps on AWS 

Deploying AI solutions with ML models into production introduces new challenges. Machine Learning Operations (MLOps) has been evolving rapidly as the industry learns to marry new ML technologies and practices with incumbent software delivery systems and processes. WordStream is a SaaS company using ML capabilities to help small and mid-sized businesses get the most out of their online advertising. Learn how Slalom developed ML architecture to help WordStream productionize their machine learning efforts.

Machine Learning-4

How to Use Amazon SageMaker to Improve Machine Learning Models for Data Analysis

Amazon SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. Amazon SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.

Cognizant_AWS Solutions

Accelerating Data Warehouse Migration to Amazon Redshift Using Cognizant Intelligent Data Works

Many organizations are looking to migrate existing, on-premises enterprise data warehouse systems to cloud-based data warehouse systems such as Amazon Redshift. Here, we discuss how Cognizant’s Intelligent Migration Workbench (IMW) can be used to accelerate the data warehouse migrations while converting Oracle PL/SQL and Tetradata BTEQ scripts. IMW makes it easy to move mission critical proprietary code to AWS, giving customers competitive edge through faster time to market.