ODSC - Open Data Science

OCTOBER 31, 2023

For data scientists, ground truth is the holy grail. If we think of AI as software that is taught with examples , instead of instructions, then selecting the right examples is critical to building a system that performs well. This is the data of record that reflects verified examples of the correct outcome.

Data Scientist 52

Data Scientist Data Science AI AI 52

AWS Machine Learning Blog

JANUARY 17, 2023

In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. This post aims to build a model that can process and relate information from multiple modalities such as tabular and textual features. Extract and analyze data from documents. JumpStart solution templates.

Categorization 70

Categorization BERT Machine Learning Neural Network 70

AWS Machine Learning Blog

OCTOBER 31, 2023

Enterprise Resource Planning (ERP) systems are used by companies to manage several business functions such as accounting, sales or order management in one system. An example of account linking would be to identify the relationship between Amazon and its subsidiary, Whole Foods Market [ source ].

LLM 103

LLM Large Language Models ML Machine Learning 103

AWS Machine Learning Blog

OCTOBER 5, 2023

Distributed training of Llama 2 To accommodate Llama 2 with 2,000 and 4,000 sequence length, we implemented the script using NeMo Megatron for Trainium that supports data parallelism (DP), tensor parallelism (TP), and pipeline parallelism (PP). Lastly, we monitor TensorBoard to keep track of training progress: tensorboard --logdir./

Machine Learning 100

Machine Learning Big Data Deep Learning Natural Language Processing 100

AWS Machine Learning Blog

APRIL 5, 2023

For example, you can specify that if your users enter the query “new products 2023,” then select the documents titled “What’s new” and “Coming soon” will feature at the top of the search results page. Choose Add data source. Under Available data sources , select Sample AWS documentation and choose Add dataset. Choose Next.

Software Engineer 75

Software Engineer Machine Learning ML Artificial Intelligence 75

ODSC - Open Data Science

OCTOBER 11, 2023

Be sure to check out his talk, “ How to Practice Data-Centric AI and Have AI Improve its Own Dataset ,” there! Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer.

Auto-classification 52

Auto-classification Auto-complete Data Drift Machine Learning 52

Viso.ai

APRIL 17, 2024

Feature Extraction is the process of transforming raw data, often unorganized, into meaningful features, which are used to train machine learning models. We provide training data to machine learning models to help the algorithm learn underlying patterns to predict the target/output. What data will be useful in this case?

Python 52

Python Machine Learning NLP Computer Vision 52

Heartbeat

FEBRUARY 21, 2023

It’s defined as a tool for exploring, analyzing, and visualizing large-scale multimedia data. Kangas DataGrid, the fundamental class for representing datasets, can easily store millions of rows of data. Group, sort, and filter across millions of data points in seconds with a simple, fast UI. Any data, any environment.

Computer Vision 52

Computer Vision Deep Learning Metadata Data Scientist 52

Defined.ai blog

APRIL 20, 2023

Health data is an extremely personal topic for many individuals, and the laws and regulations reflect this sensitivity. These screens are linked over an individual’s lifetime, making them a valuable resource for tracking life-cycle health and building medical AI models, especially when it comes to predictive health data.

AI Modeling 52

AI Modeling AI AI 52

Heartbeat

NOVEMBER 24, 2023

Photo by Derek Laliberte on Unsplash Retrieval in LangChain refers to fetching and retrieving relevant data or documents from external sources. Retrieval is useful because it allows you to incorporate external data into your language model, providing additional context and information that may not be present in the model’s training data.

Deep Learning 52

Deep Learning Metadata OpenAI Data Scientist 52

Mlearning.ai

JANUARY 26, 2023

Prepared by: Carson Ross (OpenGeoHub) , Tom Hengl (OpenGeoHub) , Leandro Parente (OpenGeoHub) , Vasile Crăciunescu (TerraSigna) Data Cubes are highly organised data infrastructures enabling users to run new analyses and generate insights into processes, patterns and trends.

Metadata 98

Metadata Data Science Big Data Linked Data 98

AWS Machine Learning Blog

MAY 10, 2023

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs.

Data Scientist 74

Data Scientist Python Software Development Machine Learning 74

ODSC - Open Data Science

NOVEMBER 8, 2023

Over the past year, new terms, developments, algorithms, tools, and frameworks have emerged to help data scientists and those working with AI develop whatever they desire. This involves providing the LLM with a dataset of labeled data, where each data point is a pair of input and output. Generative AI is a new field.

Generative AI 52

Generative AI Prompt Engineer Prompt Engineering LLM 52

Snorkel AI

SEPTEMBER 29, 2023

Data labeling remains a core requirement for any organization looking to use machine learning to solve tangible business problems, especially with the increased development and adoption of LLMs. This is where data labeling fits in. What is data labeling? This approach applies across all data modalities.

Large Language Models 59

Large Language Models Machine Learning Data Science Data Scientist 59

Heartbeat

JULY 20, 2023

Fine-tuning a model involves taking a pre-trained model and adapting it to perform well on a new, specific task or data set. This process helps enhance model performance on previously unseen data. This helps deliver an optimized model without going through the entire training process, saving time and computing resources.

Computer Vision 52

Computer Vision Machine Learning Deep Learning Algorithm 52

The MLOps Blog

MARCH 20, 2023

An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition. In such ML projects, you can expect to find large volumes of data accumulated over time, complex algorithms, and increasing compute resources; all of these are characteristics of a maturing ML project.

ML 59

ML Data Drift Machine Learning Algorithm 59

AWS Machine Learning Blog

SEPTEMBER 19, 2023

Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance.

Generative AI 104

Generative AI LLM Python Large Language Models 104

AWS Machine Learning Blog

MAY 25, 2023

Furthermore, FMs are trained with a point in time snapshot of data and have no inherent ability to access fresh data at inference time; without this ability they might provide responses that are potentially incorrect or inadequate. Amazon SageMaker Processing jobs for large scale data ingestion into OpenSearch.

LLM 77

LLM Data Ingestion Python ML 77

AWS Machine Learning Blog

MARCH 2, 2023

Using Amazon Kendra connectors enables you to synchronize data from multiple content repositories with your Amazon Kendra index. The post also demonstrates how to configure your connector for Amazon S3 and configure how your index syncs with your data source when your data source content changes.

Metadata 86

Metadata Machine Learning Software Engineer Algorithm 86

AWS Machine Learning Blog

NOVEMBER 16, 2023

Photo by Scott Webb on Unsplash Determining the value of housing is a classic example of using machine learning (ML). Almost 50 years later, the estimation of housing prices has become an important teaching tool for students and professionals interested in using data and ML in business decision-making.

ML 89

ML Machine Learning Computer Vision Auto-classification 89

Heartbeat

DECEMBER 8, 2023

Examples of end-to-end agents. Agents can be used for applications such as personal assistants, question answering, chatbots, querying tabular data, interacting with APIs, extraction, summarization, and evaluation. This enables the agent to gather relevant data and use it for decision-making. France: 82.7:

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 52

Mlearning.ai

FEBRUARY 18, 2023

with sdk v2 import libraries import tqdm import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns sns.set_style("whitegrid") import the data set # Import required libraries from azure.identity import DefaultAzureCredential from azure.identity import AzureCliCredential from azure.ai.ml

Machine Learning 52

Machine Learning ML Automation Linked Data 52

AWS Machine Learning Blog

AUGUST 14, 2023

This is useful where limited labeled data is available for training. BLOOM is an autoregressive LLM trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text that is hardly distinguishable from text written by humans.

NLP 71

NLP Robotics Prompt Engineer Prompt Engineering 71

The MLOps Blog

FEBRUARY 27, 2023

He has led several data science projects spanning multiple industries like manufacturing, retail, healthcare, insurance, safety, et cetera. Then, what’s usually the first thing to do after defining the goal, the scope is to see the data. They were yet to build the entire device to collect the data, et cetera.

Computer Vision 52

Computer Vision Auto-classification Auto-complete ML 52

The MLOps Blog

MARCH 28, 2023

In this second installment of the series “Real-world MLOps Examples,” Paweł Pęczek , Machine Learning Engineer at Brainly , will walk you through the end-to-end Machine Learning Operations (MLOps) process in the Visual Search team at Brainly. Say the teams have the data and can immediately start the labeling.

Machine Learning 59

Machine Learning Automation Data Scientist ML 59

Google Research AI blog

NOVEMBER 17, 2022

Earlier this year at the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), we published Data Cards , a dataset documentation framework aimed at increasing transparency across dataset lifecycles. The Data Cards Playbook incorporates the latest in fairness, accountability, and transparency research.

Metadata 45

Metadata ML Responsible AI Data Scientist 45

ODSC - Open Data Science

MAY 10, 2023

Experience shows that tabular data is a highly valuable data source for sales, marketing, churn management, operations, and risk management, among other business use cases. Tabular Data Has a Popularity Problem So why the bias against tabular data? It’s an innate part of our evolutionary journey.

Data Scientist 87

Data Scientist Data Science Deep Learning Algorithm 87

AWS Machine Learning Blog

APRIL 26, 2024

The DBRX LLM employs a fine-grained mixture-of-experts (MoE) architecture, pre-trained on 12 trillion tokens of carefully curated data and a maximum context length of 32,000 tokens. The model is deployed in an AWS secure environment and under your VPC controls, helping provide data security.

Python 102

Python ML LLM Generative AI 102

Heartbeat

MAY 10, 2023

Image source: Freepik Do you want to quickly log your data and visualize it in Comet with the new built-in data panel tool? In this article, we will talk about how to quickly log tabular data(this means data that is displayed in columns or tables) such as generic tabular data (.dat), csv”, or “.tsv”

Deep Learning 69

Deep Learning Machine Learning Data Scientist ML 69

TensorFlow

SEPTEMBER 11, 2023

Data often changes over time or is only available or valuable at a certain point in time. For example, market prices and weather conditions change constantly. Temporal data is also often highly discriminative in decision-making tasks. This blog post demonstrates how to train a forecasting model on transactional data.

Machine Learning 120

Machine Learning BERT ML Data Analysis 120

AWS Machine Learning Blog

MAY 1, 2024

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. It allows you to retrieve data from sources beyond the foundation model, enhancing prompts by integrating contextually relevant retrieved data.

Chatbots 101

Chatbots Automation Machine Learning DevOps 101

Pickl AI

AUGUST 24, 2023

These innovative approaches have revolutionised the process we manage data. It is the practice of storing and accessing data and applications over the internet. The businesses and individual users can use remote servers maintained by cloud service providers to store data. This minimizes the risk of data loss and downtime.

Machine Learning 52

Machine Learning Big Data Automation Data Analysis 52

Google Research AI blog

MAY 18, 2023

For example, they may visit architectural blogs to learn what domain-specific vocabulary they can adopt to help produce distinctive images of buildings. As an example, we developed new methods for extracting semantically meaningful structure from natural language prompts.

Responsible AI 65

Responsible AI AI AI ML 65

Snorkel AI

OCTOBER 12, 2023

Snorkel AI hosted the 2023 installment of its Future of Data-Centric AI virtual conference in June. The two-day event brought together researchers, practitioners, and industry leaders to discuss the latest trends and advances in data-centric AI, and we recorded each session as a video.

Data Scientist 64

Data Scientist AI AI ML 64

Snorkel AI

OCTOBER 12, 2023

Snorkel AI hosted the 2023 installment of its Future of Data-Centric AI virtual conference in June. The two-day event brought together researchers, practitioners, and industry leaders to discuss the latest trends and advances in data-centric AI, and we recorded each session as a video.

Data Scientist 59

Data Scientist AI AI ML 59

AssemblyAI

MARCH 21, 2023

This suggests a potential shift of perspective: graph-structured data provides a general and flexible framework for describing and analyzing any possible set of entities and their mutual interactions. What are the actual advantages of Graph Machine Learning?

Neural Network 317

Neural Network Deep Learning AI AI 317

IBM Journey to AI blog

OCTOBER 20, 2023

AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually. AI platforms assist with a multitude of tasks ranging from enforcing data governance to better workload distribution to the accelerated construction of machine learning models.

Machine Learning 200

Machine Learning Automation AI AI 200