Insight

AUGUST 16, 2019

The industry demand for Data Engineers is constantly on the rise, and with it more and more software engineers and recent graduates try to enter the field. The biggest hurdle for newcomers lies in understanding the Data Engineering landscape and getting hands-on experience with relevant frameworks. Want to Become a Data Engineer?

Big Data 51

Big Data Data Science Data Processing Machine Learning 51

IBM Big Data Hub

FEBRUARY 13, 2024

For example, organizations can use generative AI to: Quickly turn mountains of unstructured text into specific and usable document summaries, paving the way for more informed decision-making. Demystifying generative AI At the heart of Generative AI lie massive databases of texts, images, code and other data types.

Enterprise 125

Enterprise Modeling Testing Data-driven 125

Cloudera

JANUARY 30, 2024

With the ability of manufacturers to store a huge volume of historical data, AI can be applied in general business areas of any industry, like developing recommendations for marketing, supply chain optimization, and new product development. With AI, it can even prescribe the appropriate action that needs to be taken and when.

Manufacturing 77

Manufacturing Contextual Data IoT Digital Transformation 77

Analytics Vidhya

APRIL 8, 2023

Introduction Are you a data scientist looking for an exciting and informative read? My latest blog post is jam-packed with fun and innovative experiments that I conducted with ChatGPT over the weekend. In this experiment, I put ChatGPT to the test and challenged it to […] The post How to Use ChatGPT as a Data Scientist?

Testing 347

Testing Analytics IT Data Science 347

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

Rocket-Powered Data Science

FEBRUARY 15, 2023

It is important to realize that the usual “hype cycle” rules prevail in such cases as this. Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. These changes may include requirements drift, data drift, model drift, or concept drift.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

Cloudera

FEBRUARY 11, 2022

However, to understand what Ethical AI is, we need to have at least a basic understanding of ML, ML models and the data science lifecycle and how they are related. This blog post hopes to provide this foundational understanding. Instead, they are learned by training a model on data. What is Machine Learning.

Machine Learning 92

Machine Learning Sales Data Science Modeling 92

Cloudera

OCTOBER 4, 2023

Fast moving data and real time analysis present us with some amazing opportunities. Every organization has some data that happens in real time, whether it is understanding what our users are doing on our websites or watching our systems and equipment as they perform mission critical tasks for us.

Data Warehouse 83

Data Warehouse Experimentation Cost-Benefit Dashboards 83

DataRobot Blog

MARCH 16, 2023

This important milestone is the first step in drastically modernizing not only the development, but most importantly, the interpretation, understanding, and adoption of AI use cases. The integration of DataRobot and Azure OpenAI Service breaks down a barrier that has long existed between data teams and business stakeholders.

Data Science 98

Data Science Technology Data-driven Modeling 98

CIO Business Intelligence

SEPTEMBER 8, 2022

However, we have now reached a tipping point with AI where the compute capacity, ubiquitous connectivity, and wealth of data can match the moment and assist business leaders to create unique competitive advantages by better serving customers, improving processes, enhancing employee experience, or reducing costs. Be clear on the “why”.

Machine Learning 115

Machine Learning Cost-Benefit Data-driven Digital Transformation 115

AWS Big Data

AUGUST 7, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Forecast uses different ML algorithms to perform complex ML tasks for your datasets.

Forecasting 84

Forecasting Data Warehouse Machine Learning Modeling 84

Domino Data Lab

FEBRUARY 4, 2021

Container technology has changed the way data science gets done. The original container use case for data science focused on what I call, “environment management”. Container orchestration has the following benefits in data science work: Remove central IT bottlenecks in the MLOps life cycle.

Modeling 130

Modeling Data Science Data Processing Software 130

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Cloudera

JUNE 3, 2022

Data scientists and machine learning engineers in enterprise organizations need to fully understand their data in order to properly analyze it, build models, and power machine learning use cases across their business. Data scientists are likely to use a variety of different tools to move through their processes.

Dashboards 88

Dashboards Visualization Machine Learning Data Science 88

Andrew White

JULY 5, 2023

I blogged recently about the high level of hype and confusion across Data and Analytics just a few months ago. Here is the original blog from March 2023: Summing Up Three Days at Gartner’s Data and Analytics Conference in Orlando, Florida, USA. The fact that there are different names is one thing.

Metrics 52

Metrics Metadata Modeling Data Quality 52

DataKitchen

AUGUST 9, 2021

Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. Source: Thoughtworks.

Testing 246

Testing Data Lake Metadata Publishing 246

IBM Big Data Hub

APRIL 18, 2024

While AGI remains theoretical, organizations can take proactive steps to prepare for its arrival by building a robust data infrastructure and fostering a collaborative environment where humans and AI work together seamlessly. It might suggest a restaurant based on preferences and current popularity. How can organizations prepare for AGI?

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

Cloudera

APRIL 4, 2022

Vaccine development became the top priority for the life sciences industry – delivering new vaccines at unprecedented speed and maneuvering large-scale production processes. The Impact of Data and Analytics. Choose The Most Impactful Use Cases . Maintain Momentum.

Data Lake 77

Data Lake Digital Transformation Manufacturing Sales 77

Cloudera

JUNE 2, 2022

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. This need has generated a market opportunity for a universal data distribution service. Why does every organization need it when using a modern data stack?

Enterprise 98

Enterprise Data Lake Data Collection Data-driven 98

Cloudera

JUNE 26, 2023

Over time, using the wrong tool for the job can wreak havoc on environmental health. Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. Over time, those practices lead to cluster and Impala instability.

Testing 75

Testing Data Processing Visualization Data Science 75

Dataiku

MARCH 2, 2021

According to a survey of 1,200 global companies sponsored by Dataiku in September 2020, 66% of life sciences or pharmaceutical organizations believe AI is either considerably or very important to the future of their business. This goes to show the immense potential that AI and machine learning have in the pharmaceutical industry.

Machine Learning 105

Machine Learning Technology IT 105

IBM Big Data Hub

JULY 19, 2023

Life sciences industry is beginning to take notice and aims to leapfrog the technological advances. Life sciences industry has—for decades now—moved from the traditional discovery-based drug development to target market-based drug development paradigm.

Consulting 65

Consulting Machine Learning Manufacturing Optimization 65

IBM Big Data Hub

DECEMBER 16, 2022

Over the last week, millions of people around the world have interacted with OpenAI’s ChatGPT, which represents a significant advance for generative artificial intelligence (AI) and the foundation models that underpin many of these use cases. How can we ensure that these models are being used responsibly? The potential is vast.

Uncertainty 118

Uncertainty Metadata Modeling Data Collection 118

IBM Big Data Hub

DECEMBER 19, 2023

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements.

Machine Learning 72

Machine Learning Optimization Unstructured Data Sales 72

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 88

Metrics Machine Learning Risk Statistics 88

DataRobot Blog

OCTOBER 18, 2022

Organizations are looking for AI platforms that drive efficiency, scalability, and best practices, trends that were very clear at Big Data & AI Toronto. DataRobot Booth at Big Data & AI Toronto 2022. Today, his team is using open-source packages without a standardized AI platform.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

Cloudera

JULY 27, 2021

Only through hands-on experimentation can we discern truly useful new algorithmic capabilities from hype. These fleshed-out web applications are representative end products of data science work. There are many uses for interactive applications in the machine learning development lifecycle.

Data Science 111

Data Science Enterprise Experimentation Machine Learning 111

DataRobot Blog

MARCH 8, 2023

Today, SAP and DataRobot announced a joint partnership to enable customers connect core SAP software, containing mission-critical business data, with the advanced Machine Learning capabilities of DataRobot to make more intelligent business predictions with advanced analytics.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

CIO Business Intelligence

APRIL 25, 2022

After launching industry-specific data lakehouses for the retail, financial services and healthcare sectors over the past three months, Databricks is releasing a solution targeting the media and the entertainment (M&E) sector. The most effective recommendation engines are very specific to industries and use cases.

Recreation/Entertainment 119

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 119

CIO Business Intelligence

JULY 13, 2022

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. This need has generated a market opportunity for a universal data distribution service. Why does every organization need it when using a modern data stack?

Enterprise 88

Enterprise Data Lake Data Collection Data-driven 88

IBM Big Data Hub

MAY 8, 2023

This blog continues the discussion, now investigating the risks of adopting AI and proposes measures for a safe and judicious response to adopting AI. Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality.

Insurance 74

Insurance Risk Testing Data Quality 74

Dataiku

FEBRUARY 25, 2022

You may have not even noticed, but geospatial data has become an indispensable part of our life. We use maps and GPS trackers almost every day — generating or consuming lots of data with coordinates in one way or another. Is this the case for you?

Data Science 121

Data Science 121

Dataiku

FEBRUARY 23, 2022

In January, Dataiku’s Léo Dreyfus-Schmidt and Reda Affane presented our annual webinar on up-and-coming machine learning (ML) trends — this year, with a spin on grounding those research trends in reality with actual use cases from our data science team.

Data Science 59

Data Science Machine Learning Optimization Modeling 59

DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. As a DataRobot data scientist , I have worked with team members on a variety of projects to improve the business value of our customers.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Domino Data Lab

SEPTEMBER 7, 2021

In this blog post we look at their history, intended use-cases, strengths and weaknesses, in an attempt to understand how to select the most appropriate one for specific data science use-cases. Apache Spark, Dask, and Ray are three of the most popular frameworks for distributed computing.

Data Science 52

Data Science 52

IBM Big Data Hub

JANUARY 25, 2024

We expect technologies such as artificial intelligence (AI) to not lie to us, to not discriminate, and to be safe for us and our children to use. Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. How are you assessing fairness?

Modeling 91

Modeling Predictive Modeling Risk Statistics 91

IBM Big Data Hub

FEBRUARY 6, 2024

You immediately shift resources around to cope with the changing pattern, using automated traffic steering to shed load away from overburdened servers. If you’re using a basic pre-packaged registrar Domain Name System (DNS) offering, you probably don’t have access to DNS traffic data at all.

Dashboards 72

Dashboards Data Collection Modeling IT 72