AWS Machine Learning

MAY 10, 2023

We cover computer vision (CV), natural language processing (NLP), classification, and ranking scenarios for models and ml.c6g, ml.c7g, ml.c5, and ml.c6i SageMaker instances for benchmarking. You can use the sample notebook to run the benchmarks and reproduce the results. Mohan Gandhi is a Senior Software Engineer at AWS.

Benchmark 93

Benchmark Best practices Engineering Scripts 93

AWS Machine Learning

JULY 15, 2024

Welocalize benchmarks the performance of using LLMs and machine translations and recommends using LLMs as a post-editing tool. We use the custom terminology dictionary to compile frequently used terms within video transcription scripts. in Mechanical Engineering from the University of Notre Dame. Here’s an example.

Engineering 121

Engineering Entertainment Big data Benchmark 121

AWS Machine Learning

NOVEMBER 30, 2023

Note that your model artifacts also include an inference script for preprocessing and postprocessing. If you don’t provide an inference script, the default inference handlers for the container you have chosen will be implemented. Gaurav Bhanderi is a Front End engineer with AI platforms team in SageMaker.

Benchmark 128

Benchmark APIs Scripts Engineering 128

AWS Machine Learning

OCTOBER 31, 2022

Data scientists and machine learning engineers are constantly looking for the best way to optimize their training compute, yet are struggling with the communication overhead that can increase along with the overall cluster size. To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script.

Scripts 75

Scripts Benchmark APIs Engineering 75

AWS Machine Learning

JULY 26, 2023

SageMaker LMI containers provide two ways to deploy the model: A no-code option where we just provide a serving.properties file with the required configurations Bring your own inference script We look at both solutions and go over the configurations and the inference script ( model.py ). The container requires your model.py

Scripts 92

Scripts APIs Benchmark Engineering 92

AWS Machine Learning

MAY 29, 2024

The prospect of fine-tuning open source multimodal models like LLaVA are highly appealing because of their cost effectiveness, scalability, and impressive performance on multimodal benchmarks. It sets up a SageMaker training job to run the custom training script from LLaVA. For full parameter fine-tuning, ml.p4d.24xlarge

Scripts 127

Scripts Healthcare Metrics Finance 127

AWS Machine Learning

FEBRUARY 27, 2024

This was the perfect place to start for our prototype—not only would Axfood gain a new AI/ML platform, but we would also get a chance to benchmark our ML capabilities and learn from leading AWS experts. If discrepancies arise, a business logic within the postprocessing script assesses whether retraining the model is necessary.

Best practices 133

Best practices Engineering Scripts Analytics 133

AWS Machine Learning

DECEMBER 16, 2022

Finally, we’ll benchmark performance of 13B, 50B, and 100B parameter auto-regressive models and wrap up with future work. A ready-to-use training script for GPT-2 model can be found at train_gpt_simple.py. You can find an example in the same training script train_gpt_simple.py. Benchmarking performance. 24xlarge nodes.

Benchmark 81

Benchmark Engineering APIs Scripts 81

AWS Machine Learning

APRIL 22, 2024

Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists, and cluster administrators. To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts.

Scripts 112

Scripts Engineering Management Benchmark 112

AWS Machine Learning

APRIL 17, 2023

Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models. For more information, refer to EMNLP: Prompt engineering is the new feature engineering.

Engineering 96

Engineering Benchmark Scripts industry standards 96

AWS Machine Learning

MAY 22, 2024

Customers can more easily locate products that have correct descriptions, because it allows the search engine to identify products that match not just the general category but also the specific attributes mentioned in the product description. The script also merges the LoRA weights into the model weights after training.

Scripts 131

Scripts Engineering Accountability Benchmark 131

AWS Machine Learning

APRIL 26, 2024

Code generation DBRX models demonstrate benchmarked strengths for coding tasks. user Write a Python script to read a CSV file containing stock prices and plot the closing prices over time using Matplotlib. The file should have columns named 'Date' and 'Close' for this script to work correctly.

Transportation 124

Transportation Scripts Accountability Benchmark 124

AWS Machine Learning

APRIL 27, 2023

Trainium support for custom operators Trainium (and AWS Inferentia2) supports CustomOps in software through the Neuron SDK and accelerates them in hardware using the GPSIMD engine (General Purpose Single Instruction Multiple Data engine). The scalar and vector engines are highly parallelized and optimized for floating-point operations.

APIs 85

APIs Engineering Scripts Benchmark 85

AWS Machine Learning

DECEMBER 16, 2022

In this post, we outline the key benefits and pain points addressed by SageMaker Training Managed Warm Pools, as well as benchmarks and best practices. Benchmarks. We performed benchmarking tests to measure job startup latency using a 1.34 Overview of SageMaker Training Managed Warm Pools. When should you use warm pools?

Best practices 88

Best practices Management Engineering Benchmark 88

AWS Machine Learning

MARCH 20, 2023

Refer to the appendix for instance details and benchmark data. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. Benchmark data The following table compares the cost and relative performance between c5 and c6 instances.

Calibration 90

Calibration Scripts Benchmark APIs 90

AWS Machine Learning

OCTOBER 27, 2022

Our benchmarks show up to 46% price performance benefit after enabling heterogeneous clusters in a CPU-bound TensorFlow computer vision model training. AI Engineering, Mobileye. Performance benchmark results. You can build logic in your training script to assign the instance groups to certain training and data processing tasks.

Scripts 81

Scripts Benchmark Metrics Transportation 81

AWS Machine Learning

MARCH 14, 2024

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

Best practices 128

Best practices Engineering Chatbots Enterprise 128

AWS Machine Learning

JANUARY 19, 2024

The concepts illustrated in this post can be applied to applications that use PLM features, such as recommendation systems, sentiment analysis, and search engines. We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. He specializes in Generative AI and Machine Learning Data Engineering.

Metrics 121

Metrics Scripts Benchmark Enterprise 121

AWS Machine Learning

MAY 8, 2023

To serve models, Triton supports various backends as engines to support the running and serving of various ML models for inference. With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Import the ONNX model into TensorRT and generate the TensorRT engine.

Engineering 97

Engineering APIs Best practices Scripts 97

AWS Machine Learning

MARCH 13, 2024

. * The `if __name__ == "__main__"` block checks if the script is being run directly or imported. To run the script, you can use the following command: ``` python hello.py ``` * The output will be printed in the console: ``` Hello, world! Evaluate model on test set, compare to benchmarks, analyze errors and biases.

Benchmark 130

Benchmark Scripts APIs Feedback 130

AWS Machine Learning

NOVEMBER 17, 2022

AlexaTM 20B has shown competitive performance on common natural language processing (NLP) benchmarks and tasks, such as machine translation, data generation and summarization. To use a large language model in SageMaker, you need an inferencing script specific for the model, which includes steps like model loading, parallelization and more.

Scripts 102

Scripts APIs Government Engineering 102

AWS Machine Learning

FEBRUARY 16, 2023

Briefly, this is made possible by an installation script specified by CustomActions in the YAML file used for creating the ParallelCluster (see Create ParallelCluster ). You can invoke neuron-top during the training script run to inspect NeuronCore utilization at each node. Jeffrey Huynh is a Principal Engineer in AWS Annapurna Labs.

Scripts 98

Scripts Benchmark APIs Engineering 98

AWS Machine Learning

JUNE 6, 2023

The following figure shows a performance benchmark of fine-tuning a RoBERTa model on Amazon EC2 p4d.24xlarge inference with AWS Graviton processors for details on AWS Graviton-based instance inference performance benchmarks for PyTorch 2.0. Run your DLC container with a model training script to fine-tune the RoBERTa model.

Scripts 81

Scripts APIs Benchmark Management 81

Totango

AUGUST 23, 2022

Touchpoints may involve any medium you use to interact with customers, including: Search engine marketing. This may occur through encountering your brand or product through a search engine result, a search engine ad, a social media post, a video, a review on a technology website, word-of-mouth or other means. Blog content.

B2B 119

B2B Journey mapping SaaS Upselling 119

AWS Machine Learning

OCTOBER 24, 2022

DL scripts often require boilerplate code, notably the aforementioned double for loop structure that splits the dataset into minibatches and the training into epochs. At the time of this writing, it supports PyTorch and includes 25 techniques—called methods in the MosaicML world—along with standard models, datasets, and benchmarks.

Scripts 89

Scripts Enterprise Benchmark APIs 89

AWS Machine Learning

AUGUST 3, 2022

In this post, we walk you through the benchmarking process and the results we obtained while working on subsampled datasets. Sampling configuration and benchmarking process. This was done by using a custom script designed to create subsampled datasets in which each entity type appears at least k times, within a minimum of n documents.

Benchmark 78

Benchmark APIs Metrics Scripts 78

AWS Machine Learning

JANUARY 13, 2023

For benchmark analysis, we considered the task of predicting the in-hospital mortality of patients [2]. You can place the data in any folder of your choice, as long as the path is consistently referenced in the training script and has access enabled. Import the data loader into the training script. and data_loader.py

Analytics 85

Analytics Healthcare Scripts Accountability 85

AWS Machine Learning

AUGUST 16, 2023

Prepare the BigEarthNet-S2 dataset BigEarthNet-S2 is a benchmark archive that contains 590,325 multispectral images collected by the Sentinel-2 satellite. To train the classifier, we create a SageMaker PyTorch Estimator that runs the training script, eval_linear.py. file: init_distributed_mode function in the util.py

Scripts 83

Scripts Analytics Technology Benchmark 83

AWS Machine Learning

MAY 31, 2023

In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. Q: Can I see the model weights and scripts of proprietary models in preview with Amazon SageMaker JumpStart? Each question should have 4 options.

Scripts 82

Scripts Engineering Accountability Benchmark 82

Comm100

JANUARY 21, 2020

Online customers in the pre-purchase stage typically find companies in one of two ways: on social media or through a search engine. Recommended for you: 8 Proactive Chat Best Practices with Ready-to-Use Scripts. Comm100’s 2020 Live Chat Benchmark Report found that 74.5 Ask us about our free shipping codes!”).

B2C 84

B2C B2B Benchmark Best practices 84

AWS Machine Learning

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Engineering 127

Engineering Accountability Construction APIs 127

AWS Machine Learning

AUGUST 22, 2024

Snowflake Arctic is a family of enterprise-grade large language models (LLMs) built by Snowflake to cater to the needs of enterprise users, exhibiting exceptional capabilities (as shown in the following benchmarks ) in SQL querying, coding, and accurately following instructions.

Enterprise 123

Enterprise APIs Benchmark Scripts 123

SharpenCX

AUGUST 26, 2020

The Agile Manifesto , which is usually your engineering team’s go-to guide for management, can help leaders in every department. Set benchmarks and measure your team on how they perform against them. Reward and recognize those who achieve or surpass your benchmarks and coach up those who don’t. Be a Good Performance Assessor.

Call Center 67

Call Center Abandon rate Management Benchmark 67

AWS Machine Learning

NOVEMBER 7, 2022

Amazon SageMaker Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare high-quality features for machine learning (ML) applications via a visual interface. If you don’t validate the data before building your model, you have wasted a lot of resources as well as engineer time.

Best practices 80

Best practices Engineering Analytics Benchmark 80

AWS Machine Learning

DECEMBER 6, 2022

In real-world toxicity detection applications, toxicity filtering is mostly used in security-relevant industries like gaming platforms, where models are constantly being challenged by social engineering and adversarial attacks. Social engineers can use this type of characteristic of NLP models to bypass toxicity filtering systems.

Engineering 86

Engineering APIs Construction Benchmark 86

Taylor Reach Group

JUNE 11, 2018

Phone is the customer service channel used most in the US, more often than email, in-person, live chat, mobile app, self-service, search engine, social media, online community, text message, or Chatbot. In fact, the percentage of Contact Centers using call scripting has risen from 48.3% of the vote. Call Centre Helper).

Contact Center 50

Contact Center Self service Scripts Call Center 50

Comm100

DECEMBER 7, 2021

This will put you on the right path to manage your expectations, set targets, and benchmark your progress along the way. Keyword chatbots use a simple keyword engine to understand what the visitor is asking and respond with the most appropriate answer. Keyword chatbots.

Chatbots 52

Chatbots Wait times Education Benchmark 52