CIO Business Intelligence

MAY 8, 2023

It eliminates a lot of experimentation time … and accelerates our research quite dramatically.” We have worked closely with them as an innovation partner specifically in this area since between 2019 and 2020 when [CAS] started to refresh its business model and have a services organization,” Wilmot says.

Manufacturing 76

Manufacturing Experimentation Digital Transformation Technology 76

Domino Data Lab

JUNE 13, 2019

Many data scientists and researchers have used the MNIST test set of 10,000 samples for training and testing models for over 20 years. 2018 , 2019 ], the rediscovery of the 50,000 lost MNIST test digits provides an opportunity to quantify the degradation of the official MNIST test set over a quarter-century of experimental research.”

Testing 83

Testing Data Science Experimentation Metadata 83

Hurwitz & Associates

JULY 19, 2018

Edge-to-cloud is the central focus of Hewlett Packard Enterprise (HPE) marketing and go-to-market efforts in 2018/2019. The $4 billion investment will be used for R&D, product development, technical services and the development of new consumption models for Edge and cloud. Consumption models are changing. Key Takeaways.

Strategy 40

Strategy Data-driven Experimentation Software 40

CIO Business Intelligence

SEPTEMBER 12, 2022

Prioritize time for experimentation. The team was given time to gather and clean data and experiment with machine learning models,’’ Crowe says. Employees will rally around a new business model, a new market opportunity, a new product offering,’’ Rybchin says. That starts to get people familiar with the agile model.”.

Experimentation 133

Experimentation Consulting Technology Machine Learning 133

Data Science and Beyond

APRIL 5, 2021

The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Instead, many companies have switched to a locally-remote model, hiring remotely within the same country or timezone region. Only time will tell. Sustainability. Technical work.

Experimentation 61

Experimentation Testing Publishing Measurement 61

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation. What differentiates Fractal Analytics?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Sisense

MARCH 6, 2019

Gartner chose to group the rest of the keynote into three main messages according to the following categories: Here are some of the highlights as presented for each of them: Data Driven – “Adopt an Experimental Mindset”. At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.

Data Analytics 75

Data Analytics Analytics Experimentation Data-driven 75

Domino Data Lab

JANUARY 24, 2019

Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

FineReport

DECEMBER 19, 2019

If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Of course, other BI tools such as Power BI and Qlikview also have their own advantages. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Sisense

MARCH 26, 2021

Companies surveyed by Harvard Business Review Analytic Services (HBR) report that two of the most important strategic benefits of using data analytics are (1) identifying new revenue and business models and (2) becoming more innovative. But there’s a gap between expectations and reality, and companies are falling short of their aspirations.

Analytics 98

Analytics Data-driven Dashboards Experimentation 98

CIO Business Intelligence

JUNE 27, 2022

You pointed to frontend as a key area in 2019. A lot of the current approaches feel very experimental and are tough to see as maintainable, so there’s certainly still room for growth here. Tyson: In a sense bridge the gap between app and system monitoring. What do you see as the most interesting areas of activity in dev right now?

Software 109

Software Experimentation Testing Marketing 109

O'Reilly on Data

MARCH 18, 2020

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind?

Enterprise 309

Enterprise Deep Learning Data Governance Risk 309

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Remember that digital transformation is about transforming your business and operating models with technology.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Decision Management Solutions

JUNE 25, 2019

The last mile – getting ML models embedded into production systems – is critically important for analytic value and yet it is hard and often neglected. Her presentation really showed the importance of persistence, experimentation and lateral thinking in developing an analytic solution.

Predictive Analytics 75

Predictive Analytics Analytics Data Science Experimentation 75

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 307

Testing Machine Learning Consulting Data Quality 307

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 2 in frequency in proposal topics; a related term, “models,” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Ensure a culture that supports a steady process of learning and experimentation. Here’s our CFP , open through February 26. ethics in AI.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. In 2019 the market for graph databases and knowledge graphs started heating up – appearing on Gartner’s hype curves in 2018. Ontotext develops re-usable domain models as pre-packaged knowledge graphs.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. In 2019 the market for graph databases and knowledge graphs started heating up – appearing on Gartner’s hype curves in 2018. The first 18 years: Develop vision and products and deliver to innovation leaders.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. For instance, if I’m reading a paper from 2019, a popular song from that year could start playing. Setting the Stage: Who Am I and What Am I Trying to Build? This choice also inspired me to call my project Swift Papers.

Consulting 246

Consulting Interactive Software IT 246

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. See also: Caroline Lemieux’s slides for that NeurIPS talk, and Rohan Bavishi’s video from the RISE Summer Retreat 2019. Program Synthesis 101 ” – Alexander Vidiborskiy (2019-01-20). Model-Driven Data Queries.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Corinium

APRIL 25, 2019

I’ve found many IT as well as Business leaders have a mental model of data in that it is simply part of, or belongs to, a specific database or application, and thus they falsely conclude that just procuring a tool to protect that given environment will sufficiently protect that data. This is a much more proactive and scalable model.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment. For example, if a stock has a beta of 1.4

Statistics 27

Statistics Uncertainty Risk Marketing 27

DataRobot

JUNE 2, 2021

They went on to say that investing in MLOps directly answers one of the biggest questions facing AI practitioners in the enterprise: how to move from experimentation to transformation. The Growing Importance of MLOps, Including Operationalizing Models and Putting Models into Production.

Experimentation 52

Experimentation Machine Learning Reporting Modeling 52

O'Reilly on Data

JANUARY 7, 2020

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning. New models and methods are emerging. We also expect to see new use cases for reinforcement learning emerge.

Deep Learning 178

Deep Learning Machine Learning Experimentation Modeling 178