Pickl AI

DECEMBER 26, 2024

Defining Cloud Computing in Data Science Cloud computing provides on-demand access to computing resources such as servers, storage, databases, and software over the Internet. For Data Science, it means deploying Analytics , Machine Learning , and Big Data solutions on cloud platforms without requiring extensive physical infrastructure.

Data Science 52

Data Science Cloud Computing Machine Learning Machine Learning 52

IBM Data Science in Practice

MARCH 8, 2023

Source: IBM Cloud Pak for Data Feature Computation Engine Users can transform batch, streaming, and real-time data into features Source: IBM Cloud Pak for Data To productionize a machine learning system, it is necessary to process new data continuously. Spark, Flink, etc.) How to Get Started?

Machine Learning 264

Machine Learning Machine Learning ML ML 264

Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

IBM Journey to AI blog

OCTOBER 22, 2024

Data is the differentiator as business leaders look to utilize their competitive edge as they implement generative AI (gen AI). Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

phData

NOVEMBER 4, 2024

With all this packaged into a well-governed platform, Snowflake continues to set the standard for data warehousing and beyond. Snowflake supports data sharing and collaboration across organizations without the need for complex data pipelines.

Machine Learning 69

Machine Learning Machine Learning Data Science ML 69

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The Data Engineer Not everyone working on a data science project is a data scientist. Data engineers are the glue that binds the products of data scientists into a coherent and robust data pipeline.

Data Science 130

Data Science Data Scientist ML ML 130

AWS Machine Learning Blog

JUNE 18, 2024

More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. The fundamental objective is to build a manufacturer-agnostic database, leveraging generative AI’s ability to standardize sensor outputs, synchronize data, and facilitate precise corrections.

AWS 116

AWS AI AI Python 116

AWS Machine Learning Blog

DECEMBER 15, 2023

Amazon DocumentDB is a fully managed native JSON document database that makes it straightforward and cost-effective to operate critical document workloads at virtually any scale without managing infrastructure. Enter a user name, password, and database name. For this post, we add our restaurant data. Choose Add connection.

Machine Learning 105

Machine Learning Machine Learning AWS ML 105

Precisely

APRIL 11, 2024

The project’s first phase focused on leveraging data replication offered by Precisely to enable near real-time replication of midrange data to AWS with support for heterogeneous source and target databases.

AWS 72

AWS Cloud Computing Database Data Pipeline 72

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

ODSC - Open Data Science

OCTOBER 15, 2023

AI is quickly scaling through dozens of industries as companies, non-profits, and governments are discovering the power of artificial intelligence. This can be helpful for businesses that need to track data from multiple sources, such as sales, marketing, and customer service.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

IBM Journey to AI blog

APRIL 20, 2023

Before a bank can start the process of certifying a risk model, they first need to understand what data is being used and how it changes as it moves from a database to a model. The value of data lineage applies across all industries, but there are three key focuses when you consider it for banking use cases: 1.

Database 86

Database Data Engineering Data Engineer Data Engineering 86

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

AWS 109

AWS Machine Learning Machine Learning ML 109

IBM Journey to AI blog

JULY 30, 2024

Generative artificial intelligence (gen AI) is transforming the business world by creating new opportunities for innovation, productivity and efficiency. Data Engineer: A data engineer sets the foundation of building any generating AI app by preparing, cleaning and validating data required to train and deploy AI models.

AI 106

AI AI Data Scientist Data Preparation 106

Flipboard

FEBRUARY 13, 2023

Project Structure Creating Our Configuration File Creating Our Data Pipeline Preprocessing Faces: Detection and Cropping Summary Citation Information Building a Dataset for Triplet Loss with Keras and TensorFlow In today’s tutorial, we will take the first step toward building our real-time face recognition application. The dataset.py

Data Pipeline 145

Data Pipeline Deep Learning Deep Learning Python 145

AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. Amazon DynamoDB is a fast and flexible nonrelational database service for any scale. The ML model is trained from pet profiles pulled from Purina’s database, assuming the primary breed label is the true label.

AWS 112

AWS ML ML Machine Learning 112

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. The following diagram illustrates the solution architecture.

SQL 112

SQL Data Lakes Data Analyst AWS 112

AWS Machine Learning Blog

MARCH 14, 2024

Generative artificial intelligence (generative AI) has enabled new possibilities for building intelligent systems. Given the data sources, LLMs provided tools that would allow us to build a Q&A chatbot in weeks, rather than what may have taken years previously, and likely with worse performance.

SQL 118

SQL AWS AI AI 118

Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases. Furthermore, the notebooks can be integrated into the corporate Git repositories to collaborate using version control.

ML 153

ML ML AWS AI 153

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

PyImageSearch

FEBRUARY 5, 2024

Implementing Face Recognition and Verification Given that we want to identify people with id-1021 to id-1024 , we are given 1 image (or a few samples) of each person, which allows us to add the person to our face recognition database. On Lines 40 and 41 , we define the path to our face database (i.e.,

Database 61

Database Data Pipeline Deep Learning Deep Learning 61

ODSC - Open Data Science

SEPTEMBER 27, 2024

We’re thrilled to announce our first group of Keynote Speakers, representing the groundbreaking AI companies shaking up the industry including Anthropic, Voltron Data, NVIDIA, Google DeepMind, and Microsoft. Develop the tools to build your future in AI at ODSC West. Learn more about this lineup here!

Artificial Intelligence 40

Artificial Intelligence Artificial Intelligence AI AI 40

phData

JULY 5, 2024

The release of ChatGPT in late 2022 introduced generative artificial intelligence to the general public and triggered a new wave of AI-oriented companies, products, and open-source projects that provide tools and frameworks to enable enterprise AI.

AI 52

AI AI Database AWS 52

IBM Journey to AI blog

OCTOBER 16, 2023

Artificial intelligence (AI) adoption is still in its early stages. The Stanford Institute for Human-Centered Artificial Intelligence’s Center for Research on Foundation Models (CRFM) recently outlined the many risks of foundation models, as well as opportunities. Trustworthiness is critical.

AI 88

AI AI Data Warehouse ML 88

Mlearning.ai

JANUARY 15, 2024

The constant evolution of artificial intelligence has opened up exciting new perspectives in the field of natural language processing (NLP). Modeling tabular data distributions with GReaT Generate datasets without training data What is an LLM and how does it work? Outline What is an LLM and how does it work?

Database 52

Database Artificial Intelligence Artificial Intelligence Natural Language Processing 52

Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss. And that’s exactly what I do.

Deep Learning 144

Deep Learning Deep Learning Data Pipeline Computer Science 144

phData

JULY 4, 2024

This blog will cover creating customized nodes in Coalesce, what new advanced features can already be used as nodes, and how to create them as part of your data pipeline. They’re essentially an entire data pipeline within itself. Snowflake even handles the orchestration and scheduling of the refresh.

SQL 52

SQL Data Pipeline Data Engineering Data Engineer 52

Precisely

SEPTEMBER 18, 2023

Data observability is a key element of data operations (DataOps). It enables a big-picture understanding of the health of your organization’s data through continuous AI/ML-enabled monitoring – detecting anomalies throughout the data pipeline and preventing data downtime.

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

ODSC - Open Data Science

AUGUST 23, 2023

Modin empowers practitioners to use pandas on data at scale, without requiring them to change a single line of code. Modin leverages our cutting-edge academic research on dataframes — the abstraction underlying pandas to bring the best of databases and distributed systems to dataframes. Run operations in pandas - all in Snowflake!

Data Warehouse 52

Data Warehouse Data Science Database SQL 52

Alation

SEPTEMBER 28, 2022

Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. DataOps encourages better collaboration between data professionals and other IT roles. DataOps makes processes more efficient by automating as much of the data pipeline as possible.

DataOps 52

DataOps Data Pipeline Data Quality Analytics 52

Precisely

SEPTEMBER 30, 2024

Data enrichment adds context to existing information, enabling business leaders to draw valuable new insights that would otherwise not have been possible. Here are five data quality best practices which business leaders should focus.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2024

It’s the critical process of capturing, transforming, and loading data into a centralised repository where it can be processed, analysed, and leveraged. Data Ingestion Meaning At its core, It refers to the act of absorbing data from multiple sources and transporting it to a destination, such as a database, data warehouse, or data lake.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. In practice, tabular data is anything but clean and uncomplicated.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

ODSC - Open Data Science

OCTOBER 13, 2024

She’ll cover the distinct challenges that come with handling different data types and how modern tools can turn what feels like chaos into a manageable, streamlined architecture. Data lakes allow for the ingestion of vast amounts of data — regardless of type or format — without the need for a pre-defined schema.

Data Lakes 40

Data Lakes Database Data Pipeline SQL 40

Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. The goal is to retrieve the required data efficiently without overwhelming the source systems.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52