Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

CIO Business Intelligence

JANUARY 24, 2024

Generative AI and large language models (LLMs) like ChatGPT are only one aspect of AI. Model sizes: ~5 billion to >1 trillion parameters. Model sizes: ~Millions to billions of parameters. Downsides: Not generative; model behavior can be a black box; results can be challenging to explain.

Deep Learning 86

Deep Learning Unstructured Data Machine Learning Modeling 86

IBM Big Data Hub

DECEMBER 18, 2023

These maps, graphs, statistics and cartograms display geographical features like location, natural resources, streets and buildings as well as demographics. Further, the integration of artificial intelligence (AI) is expanding with the development of scalable foundational models that help analyze all types of data from remote sensors.

Internet of Things 100

Internet of Things IoT Forecasting Visualization 100

Rocket-Powered Data Science

JULY 14, 2023

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Analytics 166

Analytics Internet of Things Prescriptive Analytics Data Science 166

Paul DeBeasi

JANUARY 30, 2019

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. The challenge has been figuring out what to do once the model is built.

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

CIO Business Intelligence

MAY 18, 2022

But then we created a community across the entire company that could participate in learning how to think about data, how to think about data governance, how to think about building the machine learning models, deploying them, types of use cases. We embedded about 120 IoT sensors in our printers. And we did that over time.

IoT 86

IoT Internet of Things Statistics Technology 86

bridgei2i

FEBRUARY 21, 2020

In fact, statistics from Maryville University on Business Data Analytics predict that the US market will be valued at more than $95 billion by the end of this year. As companies work towards becoming digital enterprises, there’s a thrust on developing machine learning models that leverage NLP, CV, RL, etc. IoT Continues to Boom.

IoT 83

IoT Analytics Deep Learning B2B 83

Rocket-Powered Data Science

JULY 19, 2023

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Cloudera

OCTOBER 5, 2023

If your organization lacks such skills, here’s what to do: employ technology that enables your data analysts to attack data science problems without necessitating a degree in statistics. There are literally hundreds available including large language models which are all the rage.

Data-driven 82

Data-driven Enterprise Data Governance Unstructured Data 82

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. It culminates with a capstone project that requires creating a machine learning model. Switchup rating: 5.0 (out

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

datapine

NOVEMBER 19, 2019

Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. This feature hierarchy and the filters that model significance in the data, make it possible for the layers to learn from experience. AI is undoubtedly one of the most prominent 2020 buzzwords to look out for.

Technology 279

Technology Internet of Things IT IoT 279

Smart Data Collective

JUNE 26, 2019

There are two essential classifiers for developing machine learning applications with this library: a supervised learning model known as an SVM and a Random Forest (RF). Some of the Premier benefits include: Regression modeling. Advanced probability modeling. It is also frequently used in the IoT for manufacturing.

Machine Learning 77

Machine Learning Statistics Testing IoT 77

CIO Business Intelligence

JUNE 28, 2022

More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis. It’s easy to spend 10 times as much time on cleaning up data for use in a data science project than just starting up the routine in R or Python to actually perform the statistical analysis.

Management 132

Management Internet of Things Statistics Data-driven 132

Sisense

OCTOBER 15, 2020

The world is moving faster than ever, and companies processing large amounts of rapidly changing or growing data need to evolve to keep up — especially with the growth of Internet of Things (IoT) devices all around us. The first step is to navigate to the “Data” tab and create a new live model. You can give it whatever name you like.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

datapine

NOVEMBER 27, 2019

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. Prescriptive analytics goes a step further into the future.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Sisense

MARCH 4, 2019

In addition, since Hunch’s DNNs are typically on the Mb scale, they can be easily deployed and distributed to thousands of users or IOT devices, putting incredibly fast Big Data analytics almost anywhere. The power of the Hunch system lies in the DNN training process, which ultimately yields the query approximation model.

Big Data 68

Big Data Data Analytics Analytics IoT 68

Sisense

JUNE 12, 2019

our annual client conference, I gave a presentation that took a deep dive into artificial intelligence and subgroups including AI, ML, and statistics. This includes such things as AI-powered data cleansing and modeling as well as general statistical analytics of the underlying data itself. Yesterday, during Eureka! ,

Analytics 75

Analytics Data-driven Dashboards IoT 75

Cloudera

NOVEMBER 13, 2020

Data science experiment result and performance analysis, for example, calculating model lift. Operational, Cybersecurity, and IoT reporting where the current point in time state of an individual or single device needs to be analyzed. . For this reason, Impala also implements a streaming pipelined execution model.

Optimization 143

Optimization Metadata Statistics Cost-Benefit 143

Cloudera

MAY 18, 2021

Prior the introduction of CDP Public Cloud, many organizations that wanted to leverage CDH, HDP or any other on-prem Hadoop runtime in the public cloud had to deploy the platform in a lift-and-shift fashion, commonly known as “Hadoop-on-IaaS” or simply the IaaS model.

Cost-Benefit 85

Cost-Benefit Data-driven Machine Learning Data Warehouse 85

Smart Data Collective

NOVEMBER 4, 2021

In 2021 the tendency is not expected to slow down as in IoT sector alone cyberattacks are projected to double in the next five years. If the answer is so easy why the worrying statistics? Cyberattacks have been named one of five top-rated risks in 2020, according to Global Risks Report for both private individuals and businesses.

Strategy 118

Strategy Snapshot Risk IoT 118

CIO Business Intelligence

JUNE 5, 2023

Equally telling is another statistic from that research: Just 35% of these enterprises have achieved their digital goals or are on track to do so. They’re transforming their whole business model,” he says. Betzig is currently recruiting for a tech leader for a Midwestern healthcare company developing its own software platform.

Digital Transformation 142

Digital Transformation Manufacturing Strategy Management 142

Corinium

APRIL 25, 2019

And more recently, we have also seen innovation with IOT (Internet Of Things). For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. This is a much more proactive and scalable model. The protection policy follows the data.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Jet Global

MAY 9, 2024

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. This allows them to make informed decisions about the next steps in their analysis or modeling process.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

bridgei2i

OCTOBER 7, 2020

Importance of capturing market data for optimized pricing models. Underwriting essentially means evaluating the risk proposition of an insurance cover and determining how much an insurer should charge for the same by studying historical data and statistical models. Upfront decisioning – if insured can be given insurance or not.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Sisense

APRIL 9, 2019

The secret to its speed and success in adapting to the current market could be its distributed cloud-storage model, which allows it to process extremely large datasets. Originally created in 2006, it’s one of the most popular open source BI tools. PostgreSQL. As the name implies, PostgreSQL is built on and extends the SQL language.

Analytics 75

Analytics Software IoT Big Data 75

Alation

JULY 7, 2022

The Bureau of Labor Statistics projects the job outlook for data scientists to grow 22% from 2020 to 2030. The rapid growth of data roles critical to data-centric business models demonstrate an awareness of this need. LinkedIn’s 2020 Emerging Job Report lists Data Scientist at #3 with 37% annual growth.

Metadata 52

Metadata Data-driven Insurance Statistics 52

Business Over Broadway

MAY 22, 2019

IoT Artificial Intelligence. Remember that digital transformation is about transforming your business and operating models with technology. How employees can drive transformation and emerging technology adoption: Both are data-heavy endeavors; We know that statistics/math knowledge is related to data science project success.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Domino Data Lab

MARCH 3, 2019

What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production? “Three surveys of AI adoption reveal key advice from more mature practices”. Management.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49