O'Reilly on Data

FEBRUARY 18, 2020

Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%. But sustained interest in cloud migrations—usage was up almost 10% in 2019, on top of 30% in 2018—gets at another important emerging trend. Still cloud-y, but with a possibility of migration.

Data-driven 363

Data-driven Software Statistics Marketing 363

O'Reilly on Data

APRIL 12, 2019

Machine learning needs to ensure privacy, both of the training data and the model, and needs to be fair and invulnerable to tampering. Continue reading Strata San Francisco, 2019: Opportunities and Risks. Shafi Goldwasser challenged developers to create “Safe ML” : machine learning that can’t be abused.

Risk 206

Risk Machine Learning Sales Data Governance 206

O'Reilly on Data

OCTOBER 31, 2019

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

Modeling 187

Modeling 187

O'Reilly on Data

NOVEMBER 25, 2019

In this interview from O’Reilly Foo Camp 2019, Dean Wampler, head of evangelism at Anyscale.io, talks about moving AI and machine learning into real-time production environments. ’ or something like this, I need to know exactly what model was used and how it was trained. Are you prejudiced against me?’

Machine Learning 322

Machine Learning Modeling Technology IT 322

O'Reilly on Data

NOVEMBER 13, 2019

In this interview from O’Reilly Foo Camp 2019, Eric Jonas, assistant professor at the University of Chicago, pierces the hype around artificial intelligence. Highlights from the interview include: Jonas argues that “AI is a lie”—meaning that our expectations far outsize the reality of what’s currently possible.

Machine Learning 277

Machine Learning Data Collection Modeling IT 277

O'Reilly on Data

OCTOBER 31, 2019

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

Modeling 154

Modeling 154

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

CIO Business Intelligence

DECEMBER 17, 2024

When we started with generative AI and large language models, we leveraged what providers offered in the cloud. Now that we have a few AI use cases in production, were starting to dabble with in-house hosted, managed, small language models or domain-specific language models that dont need to sit in the cloud.

Digital Transformation 105

Digital Transformation Data Processing Optimization Machine Learning 105

O'Reilly on Data

FEBRUARY 4, 2019

In this post, I’ll describe some of the key areas of interest and concern highlighted by respondents from Europe, while describing how some of these topics will be covered at the upcoming Strata Data conference in London (April 29 - May 2, 2019). Machine Learning model lifecycle management. Transportation and Logistics.

Machine Learning 279

Machine Learning Enterprise IoT Big Data 279

KDnuggets

SEPTEMBER 9, 2019

In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.

Deep Learning 123

Deep Learning Modeling 123

KDnuggets

DECEMBER 16, 2019

Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. This guide defines model drift and how to identify it, and includes approaches to enable model training. So, retraining and redeploying will likely be required.

Modeling 121

Modeling Machine Learning IT 121

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

Rocket-Powered Data Science

MARCH 19, 2021

This article quotes an older market projection (from 2019) , which estimated “the global industrial IoT market could reach $14.2 Another dimension to this story, of course, is the Future of Work discussion, including creation of new job titles and roles, and the demise of older job titles and roles. trillion by 2030.”.

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

CIO Business Intelligence

NOVEMBER 1, 2024

Paul Beswick, CIO of Marsh McLennan, served as a general strategy consultant for most of his 23 years at the firm but was tapped in 2019 to relaunch the risk, insurance, and consulting services powerhouse’s global digital practice. Gen AI is quite different because the models are pre-trained,” Beswick explains.

IT 122

IT Insurance Consulting Risk 122

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. ModelOps and MLOps fall under the umbrella of DataOps,with a specific focus on the automation of data science model development and deployment workflows.

Testing 300

Testing Machine Learning Consulting Data Quality 300

datapine

DECEMBER 11, 2019

SaaS is a software distribution model that offers a lot of agility and cost-effectiveness for companies, which is why it’s such a reliable option for numerous business models and industries. 2019 was a breakthrough year for the SaaS world in many ways. Instead, they have the option of utilizing various pricing structures.

Software 314

Software Cost-Benefit Data-driven Data Processing 314

KDnuggets

SEPTEMBER 20, 2019

When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?

Modeling 113

Modeling Machine Learning 113

O'Reilly on Data

NOVEMBER 18, 2019

In this interview from O’Reilly Foo Camp 2019, Hands-On Unsupervised Learning Using Python author Ankur Patel discusses the challenges and opportunities in making machine learning and AI accessible and financially viable for enterprise applications. Then you have pre-trained models you can do transfer learning with.

ROI 164

ROI Unstructured Data Machine Learning Modeling 164

CIO Business Intelligence

NOVEMBER 19, 2024

From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. We paused the activities and got to work modeling the costs.

Testing 135

Testing Cost-Benefit Interactive ROI 135

datapine

NOVEMBER 19, 2019

While we’ve seen traces of this in 2019, it’s in 2020 that computer vision will make a significant mark in both the consumer and business world. Already in our shortlist of tech buzzwords 2019, artificial intelligence is on the front scene for next year again. Artificial Intelligence (AI). Connected Retail. Hyperautomation.

Technology 279

Technology Internet of Things IT IoT 279

Data Science 101

NOVEMBER 7, 2019

Azure Machine Learning is an environment to help with all the aspects of data science from data cleaning to model training to deployment. Azure Machine Learning now has a new web interface and it just got support for the R programming language. Python support has been available for a while. Visual Studio Online.

Data Science 106

Data Science Machine Learning Data Lake Visualization 106

KDnuggets

OCTOBER 17, 2019

This post aims to make you get started with putting your trained machine learning models into production using Flask API.

Machine Learning 123

Machine Learning Modeling 123

CIO Business Intelligence

NOVEMBER 1, 2024

Paul Beswick, CIO of Marsh McLellan, served as a general strategy consultant for most of his 23 years at the firm but was tapped in 2019 to relaunch the risk, insurance, and consulting services powerhouse’s global digital practice. Gen AI is quite different because the models are pre-trained,” Beswick explains.

IT 105

IT Insurance Consulting Risk 105

Smart Data Collective

MAY 2, 2019

Big data is vital for helping SEO companies identify and rectify inefficiencies in their models. Local businesses need to rely heavily on SEO in 2019. The post 5 Ways Local SEO Companies Are Optimizing Their Models With Big Data appeared first on SmartData Collective. Familiarity with local slang and trends.

Big Data 112

Big Data Optimization Modeling Deep Learning 112

Ontotext

JUNE 27, 2024

In the ever-evolving field of automation, the need for sophisticated models to efficiently describe and manage complex tasks has never been greater. This blog post delves into the PPR modeling paradigm, highlighting its significance and application in robot-based automation. What is the PPR Modeling Paradigm?

Modeling 72

Modeling Manufacturing Software Management 72

Rocket-Powered Data Science

MARCH 10, 2020

In 2019, I was asked to write the Foreword for the book “ Graph Algorithms: Practical Examples in Apache Spark and Neo4j “ , by Mark Needham and Amy E. And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model?

Metadata 250

Metadata Machine Learning Prescriptive Analytics ROI 250

O'Reilly on Data

JUNE 27, 2019

Improvements in documentation, ease-of-use, and its production-ready implementation of key deep learning models, combined with speed, scalability, and accuracy has made Spark NLP a viable option for enterprises needing an NLP library. Related content : “Lessons learned building natural language processing systems in health care”.

Deep Learning 194

Deep Learning Enterprise Modeling IT 194

datapine

NOVEMBER 27, 2019

2019 was a particularly major year for the business intelligence industry. Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

KDnuggets

AUGUST 14, 2019

At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

Machine Learning 123

Machine Learning Statistics Modeling IT 123

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Also, a column in the dataset indicates if each flight had arrived on time or late.

Modeling 81

Modeling Statistics Machine Learning Testing 81

FineReport

AUGUST 18, 2020

According to “IDC Semiannual Software Tracker for the Second Half of 2019”, the scale of China’s business intelligence software market reached US$490 million in 2019, with a year-on-year increase of 22.6%. In terms of deployment models, in 2019, traditional deployment models accounted for 82.7% billion U.S.

Business Intelligence 98

Business Intelligence Marketing Digital Transformation Software 98

DataKitchen

APRIL 28, 2021

Gartner included data fabrics in their top ten trends for data and analytics in 2019. Data fabrics provide reusable services that span data integration, access, transformation, modeling, visualization, governance, and delivery. From an industry perspective, the topic of data fabrics is on fire. What is a Data Fabric?

Statistics 246

Statistics Optimization Data Analytics Technology 246

CIO Business Intelligence

JUNE 12, 2023

The ISACA announced an updated version of COBIT in 2018, ditching the version number and naming it COBIT 2019. COBIT 2019 was introduced to build governance strategies that are more flexible and collaborative and that address new and changing technology.

Risk Management 94

Risk Management Strategy Digital Transformation Enterprise 94

Dataiku

JUNE 26, 2020

From 2013 with the first deep learning model to successfully learn a policy directly from pixel input using reinforcement learning to the OpenAI Dexterity project in 2019, we live in an exciting moment in RL research. In the last few years, we’ve seen a lot of breakthroughs in reinforcement learning (RL).

Deep Learning 105

Deep Learning Modeling 105

Cloudera

DECEMBER 17, 2024

Since 5G networks began rolling out commercially in 2019, telecom carriers have faced a wide range of new challenges: managing high-velocity workloads, reducing infrastructure costs, and adopting AI and automation. As with many industries, the future of telecommunications lies in AI and automation.

Enterprise 62

Enterprise Data Architecture Data-driven Optimization 62

KDnuggets

AUGUST 27, 2019

Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

Machine Learning 110

Machine Learning Modeling 110

KDnuggets

AUGUST 1, 2019

Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.

Deep Learning 122

Deep Learning Modeling Management 122