Snorkel AI

NOVEMBER 9, 2023

As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling. According to our poll participants, data preparation still occupies more data scientists’ hours than anything else. or request a demo to get started or to learn more.

Data Scientist 59

Data Scientist Data Science AI AI 59

Data Science Dojo

JULY 17, 2023

Explore the top 10 machine learning demos and discover cutting-edge techniques that will take your skills to the next level. Case studies highlighting its effectiveness Scikit-learn has been used in a variety of successful data analysis projects. It is open-source, so it is free to use and modify.

Data Analysis 195

Data Analysis Data Analysis Tableau Machine Learning 195

phData

MAY 3, 2023

The only other requirement is that you have access to data similar to the accelerator you want to use. More on data preparation later. Several accelerators also have demo videos that walk you through the dashboard. However, we must jump to the Required Attributes section above the visualization for data prep purposes.

Tableau 52

Tableau Data Preparation 52

Heartbeat

JUNE 5, 2023

MLFlow From data preparation through application deployment, MLFlow is an open-source platform that manages the whole machine learning lifecycle. It provides a set of tools for creating interactive demos and visualizations of machine learning models.

ML 59

ML ML Machine Learning Machine Learning 59

Snorkel AI

JANUARY 26, 2024

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

Data Science 64

Data Science AI AI Machine Learning 64

Tableau

FEBRUARY 22, 2021

Relevant predictions are also integrated in Tableau Prep Builder, offering insights during data preparation and giving people the power to write ML predictions (scores) directly into their data sets. Learn more about Einstein Discovery in Tableau and see it in action with a quick demo.

Tableau 102

Tableau ML ML AI 102

Snorkel AI

JANUARY 26, 2024

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

Data Science 52

Data Science AI AI Machine Learning 52

AWS Machine Learning Blog

AUGUST 14, 2023

Often, to get an NLP application working for production use cases, we end up having to think about data preparation and cleaning. This is covered with Haystack indexing pipelines , which allows you to design your own data preparation steps, which ultimately write your documents to the database of your choice.

AWS 69

AWS AI AI Database 69

Tableau

FEBRUARY 22, 2021

Relevant predictions are also integrated in Tableau Prep Builder, offering insights during data preparation and giving people the power to write ML predictions (scores) directly into their data sets. Learn more about Einstein Discovery in Tableau and see it in action with a quick demo.

Tableau 52

Tableau ML ML AI 52

Explosion

NOVEMBER 16, 2021

It all boils down to your config.cfg file and data preparation script, and the spaCy CLI handles everything. For my NER example, I’d use the NER demo pipeline : python -m spacy project clone pipelines/ner_demo From here on in, everything is up to us. Common user workflow for spaCy CLI and its configuration system And that’s it!

Machine Learning 52

Machine Learning Machine Learning Python Data Preparation 52

IBM Journey to AI blog

DECEMBER 7, 2023

Increased operational efficiency benefits Reduced data preparation time : OLAP data preparation capabilities streamline data analysis processes, saving time and resources. IBM watsonx.data is the next generation OLAP system that can help you make the most of your data.

Data Preparation 63

Data Preparation Database AI AI 63

Snorkel AI

MARCH 19, 2024

Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring. This demo steps you through the iterative approach and we cover the steps in detail below. See what Snorkel can do to accelerate your data science and machine learning teams.

AI 52

AI AI Data Scientist ML 52

Snorkel AI

MARCH 19, 2024

Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring. This demo steps you through the iterative approach and we cover the steps in detail below. See what Snorkel can do to accelerate your data science and machine learning teams.

AI 52

AI AI Data Scientist ML 52

AWS Machine Learning Blog

NOVEMBER 22, 2023

Interact with several demos that feature new applications, including a competition that involves using generative AI tech to pilot a drone around an obstacle course. Join this chalk talk for a deep dive on FM customizations through an interactive demo. Generative AI is at the heart of the AWS Village this year. Reserve your seat now!

AWS 111

AWS ML ML AI 111

Snorkel AI

NOVEMBER 1, 2023

When Vertex Model Monitoring detects data drift, input feature values are submitted to Snorkel Flow, enabling ML teams to adapt labeling functions quickly, retrain the model, and then deploy the new model with Vertex AI. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

ML 64

ML ML Machine Learning Machine Learning 64

Heartbeat

MARCH 28, 2023

Gradio is an open-source Python library that helps you build easy-to-use demos for your ML model that you can share with other people. The ML lifecycle is an ongoing process from data preparation to deployment and monitoring of the model. We briefly talked about the libraries we’ll use.

Deep Learning 52

Deep Learning Deep Learning ML ML 52

Snorkel AI

DECEMBER 20, 2023

Six months ago, Snorkel AI announced Snorkel Foundry , an offering for building specialized generative AI solutions using our cutting-edge programmatic data development techniques and expertise. Our collaborative data development efforts extended beyond data preparation.

AI 59

AI AI Data Preparation Data Scientist 59

Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. It won’t be a long demo, it’ll be a very quick demo of what you can do and how you can operationalize stuff in Snowflake. And finally, you’ll see that in action today. I don’t have a lot of time, so we’ll jump into it.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. It won’t be a long demo, it’ll be a very quick demo of what you can do and how you can operationalize stuff in Snowflake. And finally, you’ll see that in action today. I don’t have a lot of time, so we’ll jump into it.

SQL 52

SQL ML ML Python 52

AWS Machine Learning Blog

AUGUST 22, 2023

Data is split into a training dataset and a testing dataset. Both the training and validation data are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket for model training in the client account, and the testing dataset is used in the server account for testing purposes only.

Machine Learning 71

Machine Learning Machine Learning AWS ML 71

IBM Journey to AI blog

MARCH 14, 2024

  Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

AWS 83

AWS Database AI AI 83

Flipboard

AUGUST 17, 2023

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml For Prepare template , select Template is ready. Enter a stack name, such as Demo-Redshift. yaml locally. On the AWS CloudFormation console, choose Create stack.

ML 93

ML ML AWS Data Warehouse 93

Iguazio

FEBRUARY 8, 2024

What’s in store for LLMOps and how can data professionals prepare? Transitioning from Prototyping to Business Value Creation - The engineering focus will shift from showcasing demos to actual productization of GenAI and deploying LLMs to support live use cases. Here are a few expected trends: 1.

ML 52

ML ML AI AI 52

Towards AI

JANUARY 31, 2023

Here is a quick demo of how it works Let's now dive deep into how I went about the project. Data preparation The first thing I did was import the necessary libraries. I need to mount the data since the dataset is on my Google Drive. Sounds cool, right? Sure, it does. In total, I downloaded about 200 images.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

DataRobot Blog

FEBRUARY 15, 2023

The demo from the session highlights unique and differentiated capabilities that empower all users—from the analysts to the data scientists and even the person at the end of the journey who just needs to access an instant price estimate. After setting up your project, you can get started.

AI 52

AI AI Data Scientist Machine Learning 52

AWS Machine Learning Blog

JULY 5, 2023

This solution contains data preparation and visualization functionality within SageMaker and allows you to train and optimize the hyperparameters of deep learning models for your dataset. You can use your own data or try the solution with a synthetic dataset as part of this solution. Finally, you launch SageMaker Studio.

AWS 69

AWS Deep Learning Deep Learning ML 69

The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

SQL 52

SQL Database Data Scientist Python 52

The MLOps Blog

MAY 9, 2023

Again, what goes on in this component is subjective to the data scientist’s initial (manual) data preparation process, the problem, and the data used. DEMO: End-to-end ML pipeline example In this example, you will build an ML pipeline with Kubeflow Pipelines based on the infamous Titanic ML competition on Kaggle.

ML 98

ML ML Machine Learning Machine Learning 98

The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

DataRobot Blog

OCTOBER 3, 2017

Many announcements at Strata centered on product integrations, with vendors closing the loop and turning tools into solutions, most notably: A Paxata-HDInsight solution demo, where Paxata showcased the general availability of its Adaptive Information Platform for Microsoft Azure.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52