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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

To address the problems associated with complex searches, this post describes in detail how you can achieve a search engine that is capable of searching for complex images by integrating Amazon Kendra and Amazon Rekognition. A Python script is used to aid in the process of uploading the datasets and generating the manifest file.

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How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

AWS Machine Learning

Before moving to full-scale production, BigBasket tried a pilot on SageMaker to evaluate performance, cost, and convenience metrics. Log model training metrics. Each worker then proceeds with the forward and backward pass defined in your training script on each GPU. Copy the final model to an S3 bucket.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning

Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Ideally, we instead want to load the model PyTorch scripts, extract the features from model input, and run model inference entirely in Java. They use the DJL PyTorch engine to initialize the model predictor.

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Next generation Amazon SageMaker Experiments – Organize, track, and compare your machine learning trainings at scale

AWS Machine Learning

When solving a new use case, data scientists and ML engineers iterate through various parameters to find the best model configurations (aka hyperparameters) that can be used in production to solve the identified business challenge. Custom parameters and metrics can be logged using the log_parameter , log_parameters , and log_metric functions.

Metrics 78
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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Fine-tune the pre-trained model on a new custom dataset.

Scripts 71
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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Metrics, Measure, and Monitor – Make sure your metrics and associated goals are clear and concise while aligning with efficiency and effectiveness. Make each metric public and ensure everyone knows why that metric is measured. Interactive agent scripts from Zingtree solve this problem. Bill Dettering.