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

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

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.” . As an example, specialty insurance underwriting was highlighted.

Data 77

Data Insurance Metrics eBook 77

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

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

Kentik

APRIL 12, 2023

DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable. These roles include data specialists, data engineers, and principal data engineers. They work closely with data engineers to ensure the pipeline is robust and scalable.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

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? It’s that simple.

Technical Review 246

Technical Review Recruiting Systems Review Education 246

Mentormate

NOVEMBER 29, 2022

Exploring your data to target a valuable question you want to find an answer to. Starting with the data engineering and management work to generate a clean and coherent data set from the source systems, data experts will then move into activities to explore the data, build models, and evaluate them to test for insights.

Analytics 98

Analytics Analysis Business Intelligence Data 98

Netflix Tech

MARCH 7, 2024

Instead, in the style of nanoarrow , our Fast Data library only relies on the stable Arrow C data interface , producing a hermetically sealed library with no external dependencies. Example use case: Building model explainers Here’s a fascinating example of the usefulness of portable execution environments.

System 90

System Machine Learning Artificial Inteligence Training 90

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

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. classification problem. Figure 01: Is this a chihuahua or a muffin?

Machine Learning 81

Machine Learning Artificial Inteligence Weak Development Team Construction 81

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

JULY 24, 2023

data engineering pipelines, machine learning models). A great example of the capabilities of Cloudera Manager not available by any other open-source or commercial-off-the-shelf software is Kerberos Authentication. Those metrics include not just specific variables and metrics collected by each service (e.g.,

Open Source 65

Open Source Analytics Software Review Metrics 65

Netflix Tech

NOVEMBER 14, 2023

The Setup: Automated end-to-end catchup Let’s go back to our customer lifecycle example. 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.

Windows 97

Windows Data Performance Data Engineering 97

Cloudera

MAY 13, 2021

There are five factors in all, and once you have a better understanding of what they are, you’ll be that much more prepared when it comes time to choose your engine. Each stream processing engine comes with its own set of functional aspects. This centers around what the engine was designed to accomplish.

Engineering 101

Engineering Comparison Open Source Scalability 101

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. And you, as the product manager, are caught between them.

Product Management 138

Product Management Artificial Inteligence Machine Learning Software Review 138

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. Build a scikit-learn model to predict churn using customer telco data, and interpret each prediction with LIME. 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. Neural Networks .

Data 200

Data How To Recruiting Machine Learning 200

Cloudera

JANUARY 15, 2021

Some examples of recent optimizations in Impala include: New multithreading model (see dedicated blog post ). Remote read optimizations: IMPALA-8341: Data cache for remote reads. In addition to this aggregate metric, it’s interesting to look at results for individual queries from the benchmark. Results Drill Down.

Performance 90

Performance Cloud Data Storage 90

TIBCO - Connected Intelligence

MAY 20, 2020

While data science and ML processes are focused on building models, Model Ops focuses on operationalizing the entire data science pipeline within a business system. In fact, many organizations have a dedicated Model Ops Engineer to facilitate this process. The model needs to remain accurate even as the underlying data changes.

Data 72

Data Machine Learning Artificial Inteligence How To 72

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 101

Data Engineering Engineering Data Tools 101

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

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

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., More precisely, it provides total usage across all content types in this subset of topics. page views for books, minutes for videos): Figure 1.

Artificial Inteligence 38

Artificial Inteligence Organization Machine Learning Artificial Intelligence 38

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. While data is received from the client side, some additional features can also be stored in a dedicated database, a feature store. Source: retentionscience.com.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

Kentik

OCTOBER 2, 2017

For an easy-to-understand example, take the “Skills” that are available for Amazon’s Alexa. One example of using Kentik APIs in a provider setting is to enable customer portals, which is explained in this blog post. Metrics : Display data in metrics including bits, packets, or unique IPs.

Open Source 40

Open Source Metrics Network System 40

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 117

Artificial Inteligence Generative AI Security Applications 117

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. The Big Picture.

Network 40

Network WAN LAN Policies 40

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. Neural Networks .

Data 100

Data How To Recruiting Machine Learning 100

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

Kentik

MAY 16, 2016

Using the Data Explorer API for Added-value Content. Kentik Detect™ is a powerful solution that ingests and stores large volumes of network data on a per device, per customer basis. The data is stored in the Kentik Data Engine™, a timeseries database that unifies flow records (NetFlow v5/9, IPFIX, sFlow) with BGP, Geo-IP, and SNMP.

PHP 40

PHP Metrics Network Data 40

Kentik

DECEMBER 5, 2016

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. Using the performance metrics from nProbe agents running on each host, we were able to identify a particular switch as the root cause.

Network 40

Network Performance Metrics Software Review 40

TIBCO - Connected Intelligence

APRIL 15, 2021

Let me give you an example. The moment when a customer moves their mouse to the abandon shopping cart icon is the moment when you need to react—not the next day when you get the website metrics report. Compressing your data capture to analyze to get insight to decide to act cycle is key. Let’s not forget the human dimension.

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. The Difference Between Data Scientists and Data Analysts.

Data 52

Data How To Machine Learning Artificial Inteligence 52

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

Netflix Tech

NOVEMBER 22, 2019

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. The talk also includes examples of using these tools in the Amazon Elastic Compute Cloud (Amazon EC2) cloud. Thursday?—?December

AWS 40

AWS Open Source Linux Off-The-Shelf 40