Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

AWS Machine Learning Blog

JANUARY 26, 2024

Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies. With Security Lake, you can get a more complete understanding of your security data across your entire organization.

AWS 124

AWS AI AI ML 124

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 84

AWS Data Lakes Clustering Data Preparation 84

AWS Machine Learning Blog

MARCH 8, 2023

Data collection and ingestion The data collection and ingestion layer connects to all upstream data sources and loads the data into the data lake. Therefore, the ingestion components need to be able to manage authentication, data sourcing in pull mode, data preprocessing, and data storage.

AWS 74

AWS Machine Learning Machine Learning Analytics 74

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

ML 93

ML ML AWS Data Warehouse 93

Alation

SEPTEMBER 23, 2021

Data Catalogs for Data Science & Engineering – Data catalogs that are primarily used for data science and engineering are typically used by very experienced data practitioners. It also catalogs datasets and operations that includes data preparation features and functions.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

The MLOps Blog

OCTOBER 20, 2023

Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas data wrangling, or create plots is not important for readers. in a pandas DataFrame) but in the company’s data warehouse (e.g., documentation. Redshift).

SQL 52

SQL Database Data Scientist Python 52

IBM Journey to AI blog

MARCH 14, 2024

Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose. Unlock competitive advantages with accelerated data insights through an AI-powered conversational interface, with no SQL expertise required.

AWS 82

AWS Database AI AI 82

phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

Analytics 52

Analytics Analytics Database Python 52

phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts!

Clustering 98

Clustering Database SQL Data Pipeline 98

IBM Journey to AI blog

JULY 17, 2023

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods.

AI 66

AI AI Machine Learning Machine Learning 66

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. Notebook for interactive Python, SQL, and R editors for coding data pipelines.

Machine Learning 134

Machine Learning Machine Learning ML ML 134