O'Reilly Media - Ideas

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 145

Marketing Weak Development Team Metrics UI/UX 145

Kentik

APRIL 12, 2023

With modern networks’ increasing complexity and scale, it has become essential to collect and analyze data from various sources to gain insights into network performance, security, and availability. DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Mentormate

OCTOBER 31, 2023

In our example, obvious stakeholders include healthcare providers, patients, and insurers. These are parties indirectly affected by the project, such as local communities, adjacent industries, regulatory bodies, or, in the example of healthcare, even medical researchers. Could more data enhance performance?

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Netflix Tech

MARCH 7, 2024

In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training. Occasionally, these use cases involve terabytes of data, so we have to pay attention to performance. For many of our applications, model explainability matters.

System 90

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Cloudera

JULY 24, 2023

data engineering pipelines, machine learning models). In addition to LTS releases, Cloudera provides regular maintenance releases called Service Packs that also include security updates, hotfixes, performance and minor updates that guarantee the security posture and reliability of the platform.

Open Source 66

Open Source Analytics Software Review Metrics 66

Altexsoft

FEBRUARY 11, 2023

Data architect and other data science roles compared Data architect vs data engineer Data engineer is an IT specialist that develops, tests, and maintains data pipelines to bring together data from various sources and make it available for data scientists and other specialists.

Data 87

Data Data Engineering Big Data Architecture 87

Hacker Earth Developers Blog

AUGUST 16, 2022

Could you share a few examples of this discrimination and how it has affected your work in the DEI space? Even if you are using YouTube and you search for a video data engineer you will most likely see men, mostly white, and a few men of color. Have you seen this discrimination on other social sites, too?

Technical Review 246

Technical Review Recruiting Systems Review Education 246

Netflix Tech

NOVEMBER 14, 2023

Audit Run various quality checks on the staged data. Several audits, such as verifying source and target counts, are performed on this batch of data. Publish If the audits are successful, cherry-pick the staging snapshot to publish the data to production.

Windows 97

Windows Data Performance Data Engineering 97

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. Each user associated with a project performs work via a session.

Machine Learning 82

Machine Learning Artificial Inteligence Weak Development Team Construction 82

Kentik

DECEMBER 5, 2016

The ability of Kentik Detect to offer cloud-friendly network performance monitoring — Kentik NPM — was key to the recognition we earned. In a recent blog post by Kentik Solutions Engineer Eric Graham we explained how we “dog food” our own NPM solution to troubleshoot network performance issues within our own cloud-based application.

Network 40

Network Performance Metrics Software Review 40

Mentormate

DECEMBER 12, 2023

This data then undergoes manual cleaning to address inconsistencies, from measurement outliers to data entry mistakes. Afterward, the data is labeled to create training and testing datasets. Subsequently, data scientists evaluate the model’s accuracy, precision, and recall metrics to pinpoint high-risk patients.

Artificial Inteligence 52

Artificial Inteligence Machine Learning Technical Review Development 52

Altexsoft

JUNE 29, 2021

For example, Netflix takes advantage of ML algorithms to personalize and recommend movies for clients, saving the tech giant billions. MLEs are usually a part of a data science team which includes data engineers , data architects, data and business analysts, and data scientists.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

Cloudera

APRIL 9, 2021

You may recall from the previous blogs in this series that ECC is leveraging the Cloudera Data Platform (CDP) to cover all the stages of its data life cycle. Data Collection – streaming data. Data Enrichment – data engineering. Reporting – data warehousing & dashboarding. Train ML Models.

Machine Learning 98

Machine Learning Artificial Inteligence Data Data Engineering 98

AWS Machine Learning - AI

JANUARY 26, 2024

Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries. We then discuss how building on a secure foundation is essential for generative AI.

Artificial Inteligence 118

Artificial Inteligence Generative AI Security Applications 118

Cloudera

FEBRUARY 25, 2021

They need strong data exploration and visualization skills, as well as sufficient data engineering chops to fix the gaps they find in their initial study. This AMP benchmarks multiple state of the art algorithms, with a front end for comparing their performance. Built By Experts At The Leading Edge Of ML innovation.

Machine Learning 81

Machine Learning Artificial Inteligence Telecommunications Enterprise 81

Hacker Earth Developers Blog

JUNE 26, 2019

Also, the candidate should have knowledge of the different metrics used to evaluate the performance of a model. . The candidate should have a basic understanding of business or the industry in which he is applying as a data scientist. You could have a bunch of data and very little idea on what to do with it.

Data 200

Data How To Recruiting Machine Learning 200

O'Reilly Media - Ideas

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. In some cases, that simple model may be all you ever need.

Product Management 138

Product Management Artificial Inteligence Machine Learning Software Review 138

DataRobot

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Altexsoft

MAY 27, 2020

But, in any case, the pipeline would provide data engineers with means of managing data for training, orchestrating models, and managing them on production. One of the key requirements to the ML pipeline is to have control over the models, their performance, and updates. To monitor the model performance and throughput.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

Kentik

APRIL 3, 2018

In this post, we dig a little deeper with real-world examples of how Kentik helps our financial industry customers solve these challenges. With these things in mind, let’s take a look at a few examples of how our FinServ customers use Kentik Detect to solve real-world problems. Getting that data has historically been a huge challenge.

Network 40

Network WAN LAN Policies 40

O'Reilly Media - Ideas

NOVEMBER 13, 2018

Over the last 12-18 months, companies that use a lot of ML and employ teams of data scientists have been describing their internal data science platforms (see, for example, Uber , Netflix , Twitter , and Facebook ). There are also many important considerations that go beyond optimizing a statistical or quantitative metric.

Machine Learning 91

Machine Learning Artificial Inteligence Software Review Conference 91

Cloudera

SEPTEMBER 17, 2020

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

Data Engineering 102

Data Engineering Engineering Data Tools 102

Hacker Earth Developers Blog

JUNE 26, 2019

Also, the candidate should have knowledge of the different metrics used to evaluate the performance of a model. . The candidate should have a basic understanding of business or the industry in which he is applying as a data scientist. You could have a bunch of data and very little idea on what to do with it.

Data 100

Data How To Recruiting Machine Learning 100

Altexsoft

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Netflix Tech

NOVEMBER 22, 2019

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

NOVEMBER 22, 2019

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Altexsoft

SEPTEMBER 9, 2022

Another example is labeling and categorizing objects in any scientific field, be it archeological finds, chemical samples, or living things in biology. Metadata is basically information that describes other data. It helps us understand the origin, structure, nature, and context of data. Metadata examples. date modified.

Tools 59

Tools Technical Review Software Review Systems Review 59

Existek

SEPTEMBER 28, 2023

Using prepared data, AI software developers can implement techniques to evaluate and optimize model performance. It can often involve feature engineering to support relevant functionality. For example, healthcare AI developers should understand medical terminology and practices.

Artificial Inteligence 52

Artificial Inteligence Development How To Artificial Intelligence 52

TIBCO - Connected Intelligence

APRIL 15, 2021

Data and analytics investments pay off. According to McKinsey, “High-performing organizations are three times more likely than others to say their data and analytics initiatives have contributed at least 20 percent to EBIT.” Twenty percent of earnings before interest and taxes from data and analytics alone!

Analytics 41

Analytics Development Team Review Technical Review Data 41

Mobilunity

DECEMBER 21, 2022

Tech companies use data science to enhance user experience, create personalized recommendation systems, develop innovative solutions, and more. Data science in agriculture can help businesses develop data pipelines specifically for automation and fast scalability. Data Preparation. Data Modeling. Agriculture.

Data 52

Data How To Machine Learning Artificial Inteligence 52

O'Reilly Media - Ideas

DECEMBER 6, 2018

You have hardware for the data center, where training on large models and data sets usually takes place. We are also entering an age where billions of edge devices will be expected to perform inference tasks, like image recognition. For example, in certain domains (including health and finance) systems need to be explainable.

Technology 76

Technology Artificial Inteligence Machine Learning Hardware 76

Apiumhub

APRIL 1, 2021

Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. Important note: It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.

Artificial Inteligence 52

Artificial Inteligence Data Handbook Machine Learning 52

Altexsoft

AUGUST 19, 2022

That’s why any business in any industry can benefit from using the platform – from small mom-and-pop style ones that only need data visualization and basic reporting to enterprise corporations that process large masses of data across multiple departments. Plus, there are tons of customization opportunities. ML capabilities.

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

O'Reilly Media - Ideas

MAY 1, 2019

Introduction to Employee Performance Management , June 10. 60 Minutes to Better Product Metrics , July 10. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Software Architecture by Example , June 18.

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Course Training Artificial Inteligence Machine Learning 45

Cloudera

MAY 13, 2021

Performance metrics appear in charts and graphs. . WM compares the current and previous jobs by creating baselines for identifying and addressing performance problems. Establishing performance baselines between CDH/HDP and CDP. We can drill down to see performance issues related to the SQL statement.

Data Engineering 69

Data Engineering Cloud Weak Development Team Resources 69

Cloudera

NOVEMBER 17, 2021

You know the one, the mathematician / statistician / computer scientist / data engineer / industry expert. Some companies are starting to segregate the responsibilities of the unicorn data scientist into multiple roles (data engineer, ML engineer, ML architect, visualization developer, etc.),

Machine Learning 122

Machine Learning Artificial Inteligence Hotels Data Engineering 122

Altexsoft

NOVEMBER 9, 2021

Traditional statistical methods use mainly internal, historical data to predict trends within relatively stable markets. For example, there’s always demand for major raw materials such as wood, coal, wheat, or steel — and the fluctuations are mostly rather moderate. Price forecast with Beroe.

Analytics 52

Analytics Software Review Systems Review Technical Review 52