Cloudera

FEBRUARY 16, 2022

While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Machine Learning Model Lineage.

Machine Learning 86

Machine Learning Artificial Inteligence Weak Development Team Construction 86

TechCrunch

JULY 12, 2022

Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. e-commerce recommendations). One of its proponents is Mike Del Balso, the CEO of Tecton.

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O'Reilly Media - Data

JULY 18, 2018

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. Economic value of data.

Machine Learning 137

Machine Learning Artificial Inteligence Data Marketing 137

O'Reilly Media - Data

APRIL 3, 2018

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

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Machine Learning Artificial Inteligence Company Case Study 155

Perficient

FEBRUARY 13, 2023

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform Using COPY and SNOWPIPE is the fastest and cheapest way to load data. In fact, this is another example of using the right tools. I’m here.

Data Engineering 69

Data Engineering Engineering Data Storage 69

Mentormate

DECEMBER 12, 2023

In a previous blog post, we introduced a five-phase framework to plan out Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The Traditional Machine Learning Workflow Initiating a traditional ML project begins with collecting data. Duplicated records are identified and rectified.

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Artificial Inteligence Machine Learning Technical Review Development 52

Trigent

JUNE 20, 2022

Businesses worldwide are realizing the importance of the considerable volume of data they possess and the need to extract value from it and incorporate it into enhancing Customer Experience or simplifying operational processes for improved results. This is where data engineering services providers come into play.

Data Engineering 52

Data Engineering Engineering Data Strategy 52

Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle.

Machine Learning 133

Machine Learning Artificial Inteligence Data Applications 133

TechCrunch

MAY 18, 2022

Heartex, a startup that bills itself as an “open source” platform for data labeling, today announced that it landed $25 million in a Series A funding round led by Redpoint Ventures. We agreed that the only viable solution was to have internal teams with domain expertise be responsible for annotating and curating training data.

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Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Continuous Operations for Production Machine Learning (COPML) helps companies think about the entire life cycle of an ML model.

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Machine Learning Artificial Inteligence eBook Sustainability 124

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

CIO

APRIL 12, 2023

By George Trujillo, Principal Data Strategist, DataStax I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. Real-time AI involves processing data for making decisions within a given time frame.

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CIO

FEBRUARY 15, 2024

We took it seriously and said we need to have software, data, and AI capabilities,” says Nilles, who signed on to the CDIO role at the time. “We But to achieve Henkel’s digital vision, Nilles would need to attract data scientists, data engineers, and AI experts to an industry they might not otherwise have their eye on.

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Artificial Inteligence B2C Machine Learning Artificial Intelligence 346

Netflix Tech

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. For example?—?clinical

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Data Engineering Engineering Data Culture 52

Cloudera

OCTOBER 19, 2021

Airflow has been adopted by many Cloudera Data Platform (CDP) customers in the public cloud as the next generation orchestration service to setup and operationalize complex data pipelines. A simple example of this would be parameterizing SQL query within the CDW operator. Easing development friction.

Data Engineering 119

Data Engineering Engineering Data Virtualization 119

Cloudera

JANUARY 20, 2021

to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Machine learning is now being used to solve many real-time problems. One big use case is with sensor data. Training Data in HBase and HDFS. Background / Overview.

Machine Learning 113

Machine Learning Artificial Inteligence Applications Data 113

TechCrunch

OCTOBER 7, 2021

Increasingly, conversations about big data, machine learning and artificial intelligence are going hand-in-hand with conversations about privacy and data protection. They could see that the longer-term issue would be a growing need and priority for data privacy. The germination for Gretel.ai

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Artificial Inteligence Technical Review Engineering Data 246

Cloudera

APRIL 30, 2021

Apache Spark is now widely used in many enterprises for building high-performance ETL and Machine Learning pipelines. Cloudera Data Engineering (CDE) is a cloud-native service purpose-built for enterprise data engineering teams. Here is an example showing a simple PySpark program querying an ACID table.

Data Engineering 63

Data Engineering Engineering Data Software Review 63

O'Reilly Media - Ideas

NOVEMBER 13, 2018

In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Developers have taken notice and are beginning to learn about ML.

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Machine Learning Artificial Inteligence Software Review Conference 92

TechCrunch

APRIL 12, 2022

While companies find AI’s predictive power alluring, particularly on the data analytics side of the organization, achieving meaningful results with AI often proves to be a challenge. It’s true that AI can help to project revenue, for example, by identifying trends in buying and selling. ” Taking Flyte.

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Artificial Inteligence Machine Learning Open Source Biotech 202

TechCrunch

NOVEMBER 28, 2022

It’s only as good as the models and data used to train it, so there is a need for sourcing and ingesting ever-larger data troves. But annotating and manipulating that training data takes a lot of time and money, slowing down the work or overall effectiveness, and maybe both. V7’s specific USP is automation.

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TechCrunch

MAY 3, 2022

“There were no purpose-built machine learning data tools in the market, so [we] started Galileo to build the machine learning data tooling stack, beginning with a [specialization in] unstructured data,” Chatterji told TechCrunch via email.

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Artificial Inteligence Machine Learning Development Software Review 223

Altexsoft

MAY 27, 2020

Machine learning (ML) history can be traced back to the 1950s, when the first neural networks and ML algorithms appeared. Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

O'Reilly Media - Ideas

AUGUST 18, 2020

So you need to redesign your company’s data infrastructure. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows. The Two Cultures of Data Tooling.

Machine Learning 144

Machine Learning Artificial Inteligence Data Data Engineering 144

CIO

DECEMBER 11, 2023

You have to balance the potential benefits of generative AI with significant, important operational issues, such as ensuring patient data privacy and complying with regulatory requirements. Any and all activities that focus on improving patient data, processes and communications are prime for improvement.

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Generative AI Artificial Inteligence Healthcare Artificial Intelligence 189

Cloudera

JUNE 2, 2021

Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable.

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Data Data Engineering Machine Learning Artificial Inteligence 93

Hacker Earth Developers Blog

JUNE 26, 2019

Data science is one of the most sought after jobs of the 21st century. But how do you hire a data scientist who fits the bill? According to Firstround.com , in a competitive field like data science, strong candidates often receive 3 or more offers, so success rates of hiring are commonly below 50%. Data Science.

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Data How To Recruiting Machine Learning 200

CIO

FEBRUARY 13, 2023

This includes spending on strengthening cybersecurity (35%), improving customer service (32%) and improving data analytics for real-time business intelligence and customer insight (30%). CIOs anticipate an increased focus on cybersecurity (70%), data analysis (55%), data privacy (55%), AI/machine learning (55%), and customer experience (53%).

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Budget Artificial Inteligence Technical Review VR 363

CIO

AUGUST 7, 2023

Examples include GitHub Copilot, an off-the-shelf solution to generate code, or Adobe Firefly, which assists designers with image generation and editing. This archetype is the simplest, both in terms of engineering and infrastructure needs, and is generally the fastest to get up and running. There are two common approaches for Shapers.

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Abhishek Tiwari

DECEMBER 23, 2017

Data integration generally requires in-depth domain knowledge, a strong understanding of data schemas and underlying relationships. This can be time-consuming and bit challenging if you are dealing with hundreds of data sources and thousands of event types (see my recent article on ELT architecture ).

Machine Learning 40

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Cloudera

MARCH 28, 2023

Today’s healthcare is driven as much by the promise of emerging technologies centered on data processing and advanced analytics as by developing new and specialized drugs. Precision healthcare leverages data and AI-powered advanced analytics to predict and prevent disease and to identify the most effective treatments for each patient.

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TechCrunch

APRIL 26, 2021

Faculty , a VC-backed artificial intelligence startup, has won a tender to work with the NHS to make better predictions about its future requirements for patients, based on data drawn from how it handled the COVID-19 pandemic. Palantir doesn’t really do AI, they do data engineering in a big way.

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O'Reilly Media - Data

APRIL 11, 2018

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

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Data Engineering Engineering Data Artificial Inteligence 280

CIO

SEPTEMBER 18, 2023

There’s a lot of coordination and cleanup of data, workflow, and, with some customers, circuits,” he says. IDC analyst Jason Leigh says Verizon made the right move to build a tool to facilitate customer migrations, but adds that there will be challenges whenever a CIO or C-suite move their data and traffic to new environments.

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Telecommunications Network Systems Review Software Review 275

Altexsoft

FEBRUARY 11, 2023

Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. What is a data architect?

Data 87

Data Data Engineering Big Data Architecture 87

OpenCredo

JULY 31, 2018

Machine Learning, alongside a mature Data Science, will help to bring IT and business closer together. By leveraging data for actionable insights, IT will increasingly drive business value. The Role of Data. The reason for this is the central role that data plays in machine learning.

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Artificial Inteligence Machine Learning Data Artificial Intelligence 40

Xebia

MARCH 10, 2021

Many organisations have a new ambition to become a data-driven organisation. In essence, this means the organisation wants to make better business decisions based on insights provided by data [4]. Data itself is not able to advise a business for better decision-making. Data & Analytics as a separate business domain.

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