Kentik

FEBRUARY 27, 2023

The need for speed The Kentik Data Explorer is Kentik’s interface between you as an engineer, whether that’s network, systems, cloud, security, or SRE, and the database of information you’ve collected with the Kentik platform. But the real key here is that the Kentik Data Explorer was purpose-built for querying a massive database.

Network 98

Network Data WAN Metrics 98

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. Feel free to enjoy it. Feel free to enjoy it.

Data 87

Data Data Engineering Big Data Architecture 87

Perficient

JULY 24, 2023

Below is a set of guidelines to mitigate data noise and enhance the quality of training datasets utilized in AI models. Data Preprocessing AI collects the relevant data from various sources. There are several widely used techniques for eliminating data noise.

Data 52

Data Artificial Inteligence Metrics Machine Learning 52

Perficient

JULY 21, 2023

Below is a set of guidelines to mitigate data noise and enhance the quality of training datasets utilized in AI models. Data Preprocessing AI collects the relevant data from various sources. There are several widely used techniques for eliminating data noise.

Data 52

Data Artificial Inteligence Metrics Machine Learning 52

Mentormate

NOVEMBER 29, 2022

Increasingly, many of our clients ask us how they can make their organization data-driven. Why do they want to implement data solutions into their business in the first place? Simply put, actionable insights are the result of being a data-driven organization. What are Actionable Insights? Final Thoughts.

Analytics 98

Analytics Analysis Business Intelligence Data 98

Cloudera

NOVEMBER 16, 2022

There is a clear consensus that data teams should express their goals and results in business value terms and not in technical, tactical descriptions, such as “improving data engineering” and “better master data management.” . Or play defense — manage risk for the organization? Are you playing offense or defense?

Data 77

Data Insurance Metrics eBook 77

O'Reilly Media - Ideas

AUGUST 26, 2019

For example, Figure 1 shows usage across a few select topics related to AI and Data. We measure consumption with Units , a metric tuned specifically for the type of content (e.g., Content usage across a few select AI and Data topics on oreilly.com. page views for books, minutes for videos): Figure 1. Image by Ben Lorica.

Artificial Inteligence 38

Artificial Inteligence Organization Machine Learning Artificial Intelligence 38

Cloudera

JULY 18, 2023

For the Hive service in general, savvy and productive data engineers and data analysts will want to know: How do I detect those laggard queries to spot the slowest-performing queries in the system? Are there any baselines for various metrics about my query? Who are my power users, and which are my famous pools?

Systems Review 72

Systems Review Metrics Trends Performance 72

Kentik

APRIL 12, 2023

Network telemetry is the process of collecting and analyzing data from network devices, including switches, routers, firewalls, and servers, to gain visibility into network traffic and performance. 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

Cloudera

JULY 24, 2023

In their effort to reduce their technology spend, some organizations that leverage open source projects for advanced analytics often consider either building and maintaining their own runtime with the required data processing engines or retaining older, now obsolete, versions of legacy Cloudera runtimes (CDH or HDP).

Open Source 65

Open Source Analytics Software Review Metrics 65

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

DataRobot

OCTOBER 20, 2021

Data literacy is a key component for any organization to be able to scale responsible and trusted artificial intelligence technology. How Can Organizations Cultivate Data Literacy? And beyond uniting diverse stakeholders, data literacy also empowers sectoral regulators to provide industry-specific guidance.

Artificial Inteligence 52

Artificial Inteligence Data Machine Learning Artificial Intelligence 52

Altexsoft

JUNE 29, 2021

MLEs are usually a part of a data science team which includes data engineers , data architects, data and business analysts, and data scientists. Who does what in a data science team. Machine learning engineers are relatively new to data-driven companies. Making business recommendations.

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. Schedule ML Jobs.

Machine Learning 98

Machine Learning Artificial Inteligence Data Data Engineering 98

Cloudera

SEPTEMBER 23, 2021

Actually, that metric has dropped to three seconds since that study was conducted a decade ago. They had to increase the speed of decision-making while improving productivity, using technology and data in new ways, and accelerating the scope and scale of innovation. And it worked.”

Data 80

Data Sport Report Sustainability 80

Altexsoft

JULY 23, 2020

It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development. MLOps lies at the confluence of ML, data engineering, and DevOps.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

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. Know what your organization wishes to achieve with data science. Boosting and Bagging.

Data 200

Data How To Recruiting Machine Learning 200

Netflix Tech

NOVEMBER 14, 2023

This helps overwrite data only when required and minimizes unnecessary reprocessing. As seen above, by chaining these Psyberg workflows, we could automate the catchup for late-arriving data from hours 2 and 6. The Data Engineer does not need to perform any manual intervention in this case and can thus focus on more important things!

Windows 97

Windows Data Performance Data Engineering 97

CIO

NOVEMBER 29, 2022

Increasing ROI for the business requires a strategic understanding of — and the ability to clearly identify — where and how organizations win with data. It’s the only way to drive a strategy to execute at a high level, with speed and scale, and spread that success to other parts of the organization.

Organization 278

Organization Technical Review Artificial Inteligence Data 278

The Accidental Successful CIO

MAY 12, 2021

In IT the term business intelligence often also refers to a range of tools that provide quick, easy-to-digest access to insights about an organization’s current state, based on available data. The challenge that CIOs are facing is how best to make use of these new tools? Understanding Business Intelligence vs. Business Analytics.

Business Intelligence 90

Business Intelligence Business Analytics Analytics Off-The-Shelf 90

Cloudera

MAY 13, 2021

Importing data from one or multiple systems to apply transformations and then export results to another system is becoming increasingly common—which means these kinds of activities must become more automated and easily repetitive. When evaluating a stream processing engine, consider its processing abstraction capabilities.

Engineering 101

Engineering Comparison Open Source Scalability 101

Cloudera

MAY 17, 2021

Yet for organizations that only want to get their toes wet and perhaps just evaluate the capability, the 16 cores, 128 GB RAM, and 600 GB of storage prevented them from doing just that. Highly security-conscious organizations (e.g. Additional database metrics were added and alerts were improved. With Private Cloud 1.2,

Cloud 88

Cloud Machine Learning Artificial Inteligence Storage 88

Netflix Tech

MARCH 7, 2024

Data: Fast Data Our main data lake is hosted on S3, organized as Apache Iceberg tables. For ETL and other heavy lifting of data, we mainly rely on Apache Spark. flows managed by other teams), using a protocol shared by the whole organization, as exemplified by the example use case below.

System 90

System Machine Learning Artificial Inteligence Training 90

TIBCO - Connected Intelligence

MAY 20, 2020

Model Ops (aka ML Ops) ensures that models continue to deliver value to the organization. Organizations need to realize the value of data science and machine learning models holistically, rather than as simply a process of developing models. Realize the value of data science through Model Ops. Deploy/Integrate.

Data 72

Data Machine Learning Artificial Inteligence How To 72

Hacker Earth Developers Blog

AUGUST 16, 2022

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. HackerEarth: What are 3–5 pieces of advice you have for organizations looking to improve the impact of their D&I strategies? These are retention methods you should be considering.

Technical Review 246

Technical Review Recruiting Systems Review Education 246

O'Reilly Media - Ideas

MARCH 31, 2020

The biggest problems arise from taking shortcuts and assuming that a machine learning model that works for one application will perform well in a different context without looking at the underlying data. If you want to help customers organize pictures on their phones, spend some time on your phone, organizing pictures.

Product Management 136

Product Management Artificial Inteligence Machine Learning Software Review 136

Apiumhub

NOVEMBER 20, 2023

As the organizers of the Global Software Architecture Summit , we recognized the significance of introducing this subject in the forthcoming edition. With 16 years of professional experience in software engineering, including roles as CTO and CEO, he has become a prominent speaker at Green Software events in Germany.

Fractional CTO 81

Fractional CTO Software CTO Sustainability 81

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. These are core skills for any data scientist or model developer. But as many organizations can attest, developing good models is only half the battle.

Machine Learning 81

Machine Learning Artificial Inteligence Telecommunications Enterprise 81

AWS Machine Learning - AI

JANUARY 26, 2024

What operational and technical best practices can I integrate into how my organization builds generative AI LLM applications to manage risk and increase confidence in generative AI applications using LLMs? What are some ways to implement security and privacy controls in the development lifecycle for generative AI LLM applications on AWS?

Artificial Inteligence 120

Artificial Inteligence Generative AI Security Applications 120

Cloudera

FEBRUARY 17, 2021

As a matter of fact, these new challenges gave way to tremendous opportunities for businesses to optimize their key success metrics by leveraging technology. Streaming data (or data-in-motion) is one such technology space that thrived during these times.

Part-Time VPE 59

Part-Time VPE Analytics Leadership eBook 59

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

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. Know what your organization wishes to achieve with data science. Boosting and Bagging.

Data 100

Data How To Recruiting Machine Learning 100

O'Reilly Media - Ideas

NOVEMBER 13, 2018

There are also many important considerations that go beyond optimizing a statistical or quantitative metric. What is needed are data scientists who can interrogate the data and understand the underlying distributions, working alongside domain experts who can evaluate models holistically. Culture and organization.

Machine Learning 90

Machine Learning Artificial Inteligence Software Review Conference 90

RapidValue

JULY 21, 2019

In a world, which demands consumable data immediately but plagued by constant change and data drift, the traditional approach of designing, deploying and operating data pipelines as siloed activities is expensive and risky. There is a mass data movement to the cloud, taking big steps to consolidate analytics-ready data.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Altexsoft

JUNE 25, 2019

Being at the top of data science capabilities, machine learning and artificial intelligence are buzzing technologies many organizations are eager to adopt. However, they often forget about the fundamental work – data literacy, collection, and infrastructure – that must be done prior to building intelligent data products.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Trigent

NOVEMBER 3, 2022

The future of the global supply chain market lies in IoT, integrated solutions, data, and mobility. Connected logistics devices generate a massive amount of data. The ultimate goal of any organization dealing with a pool of connected devices and sensors is to leverage this data by learning the trends and patterns.

Analytics 52

Analytics Data IoT Trends 52

TechCrunch

JUNE 17, 2021

Now, three alums that worked with data in the world of Big Tech have founded a startup that aims to build a “metrics store” so that the rest of the enterprise world — much of which lacks the resources to build tools like this from scratch — can easily use metrics to figure things out like this, too.

Metrics 247

Metrics Tools Data Big Data 247