IBM Journey to AI blog

JULY 17, 2023

IBM software products are embedding watsonx capabilities across digital labor, IT automation, security, sustainability, and application modernization to help unlock new levels of business value for clients. ” Romain Gaborit, CTO, Eviden, an ATOS business “We’re looking at the potential usage of Large Language Models.

Machine Learning 189

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Marktechpost

JULY 17, 2023

IBM merged the critical capabilities of the vendor into its more contemporary Watson Studio running on the IBM Cloud Pak for Data platform as it continues to innovate. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.

Data Mining 96

Data Mining Machine Learning Data Scientist Artificial Intelligence 96

ODSC - Open Data Science

SEPTEMBER 29, 2023

In today’s digital world, data is king. Organizations that can capture, store, format, and analyze data and apply the business intelligence gained through that analysis to their products or services can enjoy significant competitive advantages. But, the amount of data companies must manage is growing at a staggering rate.

Big Data 52

Big Data ETL Data Science Data Ingestion 52

Mlearning.ai

FEBRUARY 16, 2023

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.

Business Intelligence 52

Business Intelligence Data Ingestion Metadata Machine Learning 52

Pickl AI

DECEMBER 4, 2023

First, I will answer the fundamental question ‘What is Data Intelligence?’. What is Data Intelligence in Data Science? Wondering what is Data Intelligence in Data Science? In simple terms, Data Intelligence is like having a super-smart assistant for big companies.

Data Analysis 52

Data Analysis Artificial Intelligence Artificial Intelligence Data Quality 52

Artificial Corner

JUNE 6, 2023

This period also saw the development of the first data warehouses, large storage repositories that held data from different sources in a consistent format. The concept of data warehousing was introduced by Bill Inmon, often referred to as the “father of data warehousing.” Morgan Kaufmann. Apache Kafka documentation.

Data Mining 52

Data Mining Big Data ETL Machine Learning 52

Artificial Corner

JUNE 6, 2023

With a single shake of their staff they can command the power of data into magical intelligence never seen before, intelligence that will finally provide the answer to the unanswerable. With large scale investment in server farms, where immense amounts of data could be captured, stored and somehow used.

Big Data 52

Big Data Data Mining Data Scientist Business Intelligence 52

Pickl AI

APRIL 26, 2024

A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics. Have you worked with cloud-based data platforms like AWS, Google Cloud, or Azure?

Data Analysis 52

Data Analysis Machine Learning ETL Explainability 52

Unite.AI

JUNE 28, 2023

Xavier Conort is a visionary data scientist with more than 25 years of data experience. He began his career as an actuary in the insurance industry before transitioning to data science. He’s a top-ranked Kaggle competitor and was the Chief Data Scientist at DataRobot before co-founding FeatureByte.

Data Scientist 264

Data Scientist Machine Learning Data Science ML Engineer 264

Marktechpost

NOVEMBER 1, 2024

Databricks Unified Data Analytics Platform Databricks provides a single cloud-based platform for the large-scale deployment of enterprise-grade AI and data analytics solutions. Google Cloud Smart Analytics supports organizations in building data-driven workflows and implementing AI at scale.

Artificial Intelligence 94

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The MLOps Blog

MARCH 21, 2023

By storing all model-training-related artifacts, your data scientists will be able to run experiments and update models iteratively. Versioning Your data science team will benefit from using good MLOps practices to keep track of versioning, particularly when conducting experiments during the development stage.

Machine Learning 69

Machine Learning Data Scientist Metadata ML 69