ODSC - Open Data Science

AUGUST 24, 2023

Evaluating Prompt Completion: The goal is to establish effective evaluation criteria to gauge LLMs’ performance across tasks and domains. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3. Various prompting techniques, such as Zero/Few Shot, Chain-of-Thought (CoT)/Self-Consistency, ReAct, etc.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Responsible AI 52

AWS Machine Learning Blog

APRIL 19, 2024

We use Amazon EKS and were looking for the best solution to auto scale our worker nodes. Solution overview In this section, we present a generic architecture that is similar to the one we use for our own workloads, which allows elastic deployment of models using efficient auto scaling based on custom metrics.

Auto-complete 101

Auto-complete Metadata AI AI 101

AWS Machine Learning Blog

MAY 7, 2024

Veritone’s current media search and retrieval system relies on keyword matching of metadata generated from ML services, including information related to faces, sentiment, and objects. We use the Amazon Titan Text and Multimodal Embeddings models to embed the metadata and the video frames and index them in OpenSearch Service.

Metadata 55

Metadata Generative AI Large Language Models Machine Learning 55

The MLOps Blog

JANUARY 23, 2023

Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. There will be only one type of ML metadata store (model-first), not three. We saw fashion designers sign up for our ML metadata store. Lived through the DevOps revolution. Came to ML from software. So to speak.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

Marktechpost

JULY 11, 2023

Tabnine for JupyterLab Typing code is complex without auto-complete options, especially when first starting out. In addition to the spent time inputting method names, the absence of auto-complete promotes shorter naming styles, which is not ideal. For a development environment to be effective, auto-complete is crucial.

Machine Learning 87

Machine Learning Auto-complete Data Scientist ML 87

The MLOps Blog

FEBRUARY 27, 2023

You would address it in a completely different way, depending on what’s the problem. This is more about picking, for some active learning or for knowing where the data comes from and knowing the metadata to focus on the data that are the most relevant to start with. This is a much smaller scale than Auto ML.

Computer Vision 52

Computer Vision Auto-classification Auto-complete ML 52

The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you see the complete model lineage with data/models/experiments used downstream?

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

JUNE 13, 2023

In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ). 2xlarge instances.

Generative AI 65

Generative AI Auto-complete AI Modeling Machine Learning 65

John Snow Labs

OCTOBER 12, 2023

In this release, we’ve focused on simplifying model sharing, making advanced features more accessible with FREE access to Zero-shot NER prompting, streamlining the annotation process with completions and predictions merging, and introducing Azure Blob backup integration. Click “Submit” to finalize.

NLP 52

NLP Auto-complete Auto-classification Prompt Engineer 52

AWS Machine Learning Blog

JANUARY 8, 2024

When the script ends, a completion status along with the time taken will be returned to the SageMaker studio console. These JSON files will contain all the Amazon Textract metadata, including the text that was extracted from within the documents. The following diagram illustrates the sequence of events within the script.

IDP 89

IDP Auto-complete Python Natural Language Processing 89

The MLOps Blog

JANUARY 19, 2024

<pre class =" hljs " style =" display : block; overflow-x: auto; padding: 0.5 <pre class =" hljs " style =" display : block; overflow-x: auto; padding: 0.5 Here’s the complete code: import evaluate import torch from tqdm.auto import tqdm import numpy as np def get_logits_and_labels (sample_, max_new_tokens) : sample = sample_.copy()

LLM 52

LLM Auto-complete Large Language Models Natural Language Processing 52

AWS Machine Learning Blog

MAY 31, 2023

Each model deployed with Triton requires a configuration file ( config.pbtxt ) that specifies model metadata, such as input and output tensors, model name, and platform. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.

ML 84

ML Auto-classification Auto-complete Natural Language Processing 84

AWS Machine Learning Blog

APRIL 16, 2024

To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret. Complete the following steps: On the Secrets Manager console, choose Store a new secret. Always make sure that sensitive data is handled securely to avoid potential security risks.

Data Scientist 91

Data Scientist Generative AI ML Machine Learning 91

AWS Machine Learning Blog

AUGUST 4, 2023

With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. million reviews spanning May 1996 to July 2014. Next, select a training method.

Auto-complete 74

Auto-complete ML Metadata Python 74

AWS Machine Learning Blog

MARCH 1, 2023

To solve this problem, we make the ML solution auto-deployable with a few configuration changes. The training and inference ETL pipeline creates ML features from the game logs and the player’s metadata stored in Athena tables, and stores the resulting feature data in an Amazon Simple Storage Service (Amazon S3) bucket.

Automation 88

Automation ETL Data Drift ML 88

AWS Machine Learning Blog

MARCH 2, 2023

script will create the VPC, subnets, auto scaling groups, the EKS cluster, its nodes, and any other necessary resources. When this step is complete, delete the cluster by using the following script in the eks folder: /eks-delete.sh Unless you specify Spot Instances in conf, instances will be created on demand. eks-create.sh

BERT 76

BERT Deep Learning Metadata Auto-complete 76

AWS Machine Learning Blog

MAY 8, 2023

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Input and output – These fields are required because NVIDIA Triton needs metadata about the model. Note that the cell takes around 30 minutes to complete. !docker This is run as part of the generate_model.sh

ML 84

ML BERT Deep Learning Auto-complete 84

PyImageSearch

OCTOBER 2, 2023

in their paper Auto-Encoding Variational Bayes. script sets up the autoencoder model hyperparameters and creates an output directory for storing training progress metadata, model weights, and post-training analysis plots. Auto-Encoding Variational Bayes. VAEs were introduced in 2013 by Diederik et al. The config.py The torch.nn

Computer Vision 120

Computer Vision Deep Learning Neural Network Auto-complete 120

Snorkel AI

MARCH 2, 2023

Others, toward language completion and further downstream tasks. In media and gaming: designing game storylines, scripts, auto-generated blogs, articles and tweets, and grammar corrections and text formatting. Very large core pie, and very efficient in certain sets of things. Let’s take a look at what the breakdown is.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Others, toward language completion and further downstream tasks. In media and gaming: designing game storylines, scripts, auto-generated blogs, articles and tweets, and grammar corrections and text formatting. Very large core pie, and very efficient in certain sets of things. Let’s take a look at what the breakdown is.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

The MLOps Blog

MARCH 12, 2024

Model management Teams typically manage their models, including versioning and metadata. Observability tools: Use platforms that offer comprehensive observability into LLM performance, including functional logs (prompt-completion pairs) and operational metrics (system health, usage statistics). using techniques like RLHF.)

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

AWS Machine Learning Blog

SEPTEMBER 21, 2023

We normalize these images into a set of uniform thumbnails, which constitute the functional input for the active learning pipeline (auto-labeling and inference). The auto-labeling pipeline focuses on automating SageMaker Ground Truth jobs and sampling images for labeling through those jobs.

Auto-complete 99

Auto-complete Machine Learning Computer Vision ML 99

AWS Machine Learning Blog

MARCH 6, 2023

FSx for Lustre uses distributed file storage (stripping) and physically separates file metadata from file content to achieve high-performance read/writes. This results in faster restarts and workload completion. Amazon FSx is an open-source parallel file system, popular in high-performance computing (HPC).

Large Language Models 79

Large Language Models LLM Machine Learning ML 79

Dlabs.ai

JANUARY 16, 2023

Using new_from_file only loads image metadata. The return value will contain a NumPy array of unsigned 8-bit integers with scaled image contents. Please note that the libvips API creates an image processing pipeline. Same thing with resize : no actual resizing is performed. A CSV file guides execution.

Neural Network 52

Neural Network ML Auto-classification Convolutional Neural Networks 52

AWS Machine Learning Blog

MAY 2, 2023

Complete the following steps to use Autopilot AutoML to build, train, deploy, and share an ML model with a business analyst: Download the dataset , upload it to an Amazon S3 ( Amazon Simple Storage Service ) bucket, and make a note of the S3 URI. Complete the steps listed in the README file. Set the target column as churn.

ML 67

ML Data Scientist Auto-complete Metadata 67

AWS Machine Learning Blog

FEBRUARY 19, 2024

As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity.

ML 95

ML Auto-complete Deep Learning Software Engineer 95