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. Prospective candidates should be good at collecting, analyzing, and making inferences from data.

Data 200

Data How To Recruiting Machine Learning 200

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

Apiumhub

NOVEMBER 20, 2023

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. His primary responsibility is to integrate sustainability into the engineering roadmap and utilize the company’s portfolio to champion sustainability solutions.

Fractional CTO 81

Fractional CTO Software CTO Sustainability 81

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

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. In that post, Eric shows how he found issues on a group of internal hosts that were impacting a critical microservice. How does it work?

Network 40

Network Performance Metrics Software Review 40

O'Reilly Media - Ideas

MARCH 31, 2020

Otherwise, you will burn money paying external services for labeled data, and that up-front cost–before you can do your first demo–can easily be the most expensive part of the project. Without large amounts of good raw and labeled training data, solving most AI problems is not possible. How do you select what to work on?

Product Management 136

Product Management Artificial Inteligence Machine Learning Software Review 136

Kentik

OCTOBER 2, 2017

Kentik Connect for Grafana ships with Kentik Data Source, a database connector that allows you to read and visualize data directly from our backend, the Kentik Data Engine (KDE). Group By (see screenshot at right): Group by over 30 source and destination dimensions representing NetFlow, BGP, or GeoIP data.

Open Source 40

Open Source Metrics Network System 40

Netflix Tech

MARCH 2, 2021

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Machine Learning 83

Machine Learning Artificial Inteligence Culture Analysis 83

O'Reilly Media - Ideas

APRIL 19, 2021

Finally, 8% of respondents said that their industry was “Other,” and 14% were grouped into “All Others.” “All The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Weights & Biases was the most popular third-party solution (12%).

Enterprise 144

Enterprise Survey Weak Development Team Education 144

Puppies, Flowers, Rainbows and Kittens

MAY 17, 2021

There were obvious, objective metrics that I could use to measure my work. Those metrics and my job defined me. I have also managed testers, data scientists, data engineers, DevOps, Security, designers, data analysts, program managers, product managers, corporate IT teams, and some other roles I don’t even remember anymore.

Weak Development Team 40

Weak Development Team How To Leadership Metrics 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. Prospective candidates should be good at collecting, analyzing, and making inferences from data.

Data 100

Data How To Recruiting Machine Learning 100

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

Cloudera

MAY 22, 2018

With Experiments, data scientists can run a batch job that will: create a snapshot of model code, dependencies, and configuration parameters necessary to train the model. track model metrics, performance, and any model artifacts the user specifies. you can now designate the LDAP and SAML groups for both users and administrators.

Data 41

Data Load Balancer Machine Learning Artificial Inteligence 41

O'Reilly Media - Ideas

DECEMBER 6, 2018

Best practices were passed through internships in a few groups, and a lot of knowledge was shared in the form of “oral tradition.” Progress in research has been made possible by the steady improvement in: (1) data sets, (2) hardware and software tools, and (3) a culture of sharing and openness through conferences and websites like arXiv.

Technology 76

Technology Artificial Inteligence Machine Learning Hardware 76

O'Reilly Media - Ideas

MAY 1, 2019

Inside Unsupervised Learning: Group Segmentation using Clustering , June 13. 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. Unlock Your Potential , July 9.

Course 45

Course Training Artificial Inteligence Machine Learning 45

Kentik

MAY 9, 2017

The combination of those two metrics enables an operator to understand the “cost” for that traffic subset. ingested into the Kentik Data Engine with two new fields that represent the Device (router) and Site (PoP) where that flow will exit the network to an adjacent autonomous system (AS).

Internet 40

Internet Analysis Network Analytics 40

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Types of metadata. There are multiple ways to categorize metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

Netflix Tech

SEPTEMBER 18, 2020

Even within Netflix, we have many groups that do some form of data analysis, including business strategy and consumer insights. But here we are talking about Netflix’s Data Science and Engineering group, which specializes in analytics at scale. But what makes this group really shine are their differences.

Analytics 96

Analytics Engineering Film Data Engineering 96

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. Deployment: They deploy AI models into production environments, often using cloud services, containers, or other deployment tools.

Artificial Inteligence 52

Artificial Inteligence Development How To Artificial Intelligence 52

Kentik

NOVEMBER 16, 2015

KDE handles over 10B flow records/day with a microservice architecture that's optimized using metrics. Here at Kentik, our Kentik Detect service is powered by a multi-tenant big data datastore called Kentik Data Engine. And that leads us to metrics. Health checks and series metrics. The life of a query.

Metrics 40

Metrics Microservices Linux Architecture 40

Altexsoft

NOVEMBER 9, 2021

Procurement metrics and KPIs. In procurement, there are several main groups of KPIs that are worth monitoring to get a better understanding of the effectiveness of your operations. For a fuller picture, you can also monitor communication time lags, price competitiveness, frequency of price changes and other performance-related metrics.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

CIO

AUGUST 5, 2022

Hierarchical: These visualizations show how groups relate to one another. Tree diagrams are an example of a hierarchical visualization that shows how larger groups encompass sets of smaller groups. Network: Network visualizations show how data sets are related to one another in a network. It also has a mobile app.

Data 358

Data Analytics Travel Business Intelligence 358

Altexsoft

AUGUST 19, 2022

bubble charts, grouped bars), data over time (e.g., You can personalize dashboards and interfaces, create custom reports and visualizations, and even set up alerts on specific KPIs to notify your team of important metrics updates. There are several main categories of visuals according to their purpose: comparison, (e.g.,

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

TechCrunch

SEPTEMBER 29, 2022

The startup, built by Stiglitz, Sourabh Bajaj , and Jacob Samuelson , pairs students who want to learn and improve on highly technical skills, such as devops or data science, with experts. Edtech’s search for the magic metric. Some classes, like this SQL crash course , are even taught by CoRise employees. It has a 68 NPS score.

Course 180

Course Technical Review Machine Learning Artificial Inteligence 180

Altexsoft

MARCH 31, 2020

When it comes to building and managing infrastructure for data movement and its strategic usage, that’s the duty of data engineers. Data pipeline components. To understand how the data pipeline works in general, let’s see what a pipeline usually consists of. Data pipeline components.

Data 76

Data Storage Analytics Data Center 76

O'Reilly Media - Ideas

JANUARY 25, 2024

Just a few notes on methodology: This report is based on O’Reilly’s internal “Units Viewed” metric. The data used in this report covers January through November in 2022 and 2023. LangChain is a framework for building generative AI applications around groups of models and databases. More on MLOps when we discuss operations below.)

Trends 115

Trends Technical Review Technology Artificial Inteligence 115

TechCrunch

MAY 7, 2021

Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. After that, we’ll discuss valuations and which venture capital groups are set to do well in its flotation.

Fintech 246

Fintech Windows Healthcare Analytics 246

StubHub

AUGUST 8, 2019

The group of 20 is a diverse mix of college, grad school and PhD students who hail from a variety of disciplines: computer science, data science, business, software engineering, design, informatics, applied mathematics and economics. It will be a one-stop solution for metric prediction and can be used by various departments.

Technical Review 8

Technical Review Software Review Software Engineering Sport 8

Netflix Tech

NOVEMBER 20, 2023

The need for backfilling could be due to a variety of factors, e.g. (1) upstream data sets got repopulated due to changes in business logic of its data pipeline, (2) business logic was changed in a data pipeline, (3) anew metric was created that needs to be populated for historical time ranges, (4) historical data was found missing, etc.

Windows 84

Windows Software Review Data Engineering 84

Altexsoft

FEBRUARY 8, 2021

It includes a broad range of tightly interrelated activities that we can arrange into several major groups. The data that your procurement management software generates can help you analyze potential suppliers’ performance by comparing their KPIs, prices, compliance, and other variables. Supply chain management process. Establish KPIs.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

JULY 19, 2022

As the picture above clearly shows, organizations have data producers and operational data on the left side and data consumers and analytical data on the right side. Data producers lack ownership over the information they generate which means they are not in charge of its quality. It works like this.

Architecture 52

Architecture Data Analytics Data Engineering 52

O'Reilly Media - Ideas

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

Government 79

Government Artificial Inteligence Machine Learning Testing 79

Abhishek Tiwari

JULY 20, 2019

For instance, if you are fast-growing VC funded e-commerce startup and your number one business priority is multiplying current growth and performing exceptionally well on key financial metrics charted out by your investors. Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.

Engineering 61

Engineering Weak Development Team Software Review Technical Review 61

Netflix Tech

OCTOBER 30, 2020

I’m a Senior Analytics Engineer on the Content and Marketing Analytics Research team. My team focuses on innovating and maintaining the metrics Netflix uses to understand performance of our shows and films on the service. One of my favorite things about being an Analytics Engineer is the variety.

Analytics 54

Analytics Engineering Film Apparel 54

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. What does the high-performance data project have to do with the real Franz Kafka’s heritage? process data in real time and run streaming analytics. How Apache Kafka streams relate to Franz Kafka’s books.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

O'Reilly Media - Ideas

MARCH 1, 2023

Methodology This report is based on our internal “units viewed” metric, which is a single metric across all the media types included in our platform: ebooks, of course, but also videos and live training courses. It’s the smallest topic category in this group but showed a 24% year-over-year gain. year over year.

Trends 133

Trends Technical Review Technology Software Review 133

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52