Xebia

DECEMBER 23, 2024

For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources.

Data 130

Data Storage Culture Resources 130

Xebia

DECEMBER 23, 2024

For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources.

Data 130

Data Storage Culture Resources 130

Xebia

OCTOBER 25, 2024

Bo Lemmers, Analytics Engineer here at Xebia, and Mike Kamysz, Data Engineer at The Data Institute kick off the series with: “ Why can’t I just query the raw data? ” When you query raw data, it’s like having a blank canvas. You can create your own metrics, dimensions, and transformations on the fly.

Data 130

Data Metrics Analytics Quality Assurance 130

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

CIO

APRIL 12, 2023

Real-time AI brings together streaming data and machine learning algorithms to make fast and automated decisions; examples include recommendations, fraud detection, security monitoring, and chatbots. What metrics are used to understand the business impact of real-time AI?

Artificial Inteligence 218

Artificial Inteligence Artificial Intelligence Machine Learning Agile 218

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 Artificial Inteligence Machine Learning 180

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

CIO

NOVEMBER 27, 2024

Not cleaning your data enough causes obvious problems, but context is key. “A A lot of organizations spend a lot of time discarding or improving zip codes, but for most data science, the subsection in the zip code doesn’t matter,” says Kashalikar. That’s a classic example of too much good is wasted.”

Data 211

Data Enterprise Weak Development Team Software Review 211

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.),

Artificial Inteligence 126

Artificial Inteligence Machine Learning Hotels Data Engineering 126

AWS Machine Learning - AI

JUNE 20, 2024

The data is stored in a data lake and retrieved by SQL using Amazon Athena. We used a large language model (LLM) with query examples to make the search work using the language used by Imperva internal users (business analysts). Using an LLM with the right examples can make this task less difficult.

Artificial Inteligence 97

Artificial Inteligence UI/UX Generative AI Construction 97

CIO

MARCH 21, 2022

Businesses typically rely on keywords to make sense of unstructured data to pull out relevant data using searchable terms. Semi-structured data falls between the two. It doesn’t conform to a data model but does have associated metadata that can be used to group it. A method for turning data into value.

Analytics 205

Analytics Data Analysis Technical Review 205

Erik Bernhardsson

DECEMBER 6, 2022

We build it super fast — the above example in a couple of seconds, since we built our own container builder and have fast machines in the cloud with super fast internet. I'm deliberately vague about what exact role I mean here: take it to mean data engineers, data scientists, ML engineers, analytics engineers, and maybe more roles.

CTO Coach 242

CTO Coach Fractional CTO Software Engineering Serverless 242

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

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

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

Product Management 135

Product Management Artificial Inteligence Machine Learning Weak Development Team 135

Cloudera

MAY 13, 2021

Performance metrics appear in charts and graphs. . For example, a user identified by “3xksle8z” runs only 3% of the queries, yet consumes far more memory than any other user, consuming about 5.9 For example, we see a large number of joins in these queries: Too many joins and inline views characterize inefficiently written SQL.

Data Engineering 74

Data Engineering Cloud Weak Development Team Resources 74

CIO

NOVEMBER 29, 2022

For data warehouses, it can be a wide column analytical table. Many companies reach a point where the rate of complexity exceeds the ability of data engineers and architects to support the data change management speed required for the business. Data and cloud strategy must align.

Organization 167

Organization Technical Review Data Artificial Inteligence 167

Cloudera

DECEMBER 9, 2024

Autoscaling & Resource Optimization : The platform dynamically adjusts resources with autoscaling based on Requests per Second (RPS) or concurrency metrics, ensuring efficient handling of peak loads. These tools allow businesses to focus on building impactful AI solutions without needing extensive custom data engineering.

Knowledge Base 76

Knowledge Base Artificial Inteligence Scalability Data Engineering 76

Netflix Tech

SEPTEMBER 18, 2020

Full ownership often means building new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations and dashboards?—?always These are only possible through the one-two punch of deep business context ?? and technical excellence ??.

Analytics 97

Analytics Engineering Film Data Engineering 97

AWS Machine Learning - AI

NOVEMBER 15, 2024

Generative AI models (for example, Amazon Titan) hosted on Amazon Bedrock were used for query disambiguation and semantic matching for answer lookups and responses. Model monitoring of key NLP metrics was incorporated and controls were implemented to prevent unsafe, unethical, or off-topic responses.

Generative AI 118

Generative AI AWS Groups Artificial Inteligence 118

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.

Artificial Inteligence 98

Artificial Inteligence Machine Learning Data Data Engineering 98

O'Reilly Media - Data

MARCH 22, 2018

Data Scientist Cathy O’Neil has recently written an entire book filled with examples of poor interpretability as a dire warning of the potential social carnage from misunderstood models—e.g., Analysts and data scientists can possibly use model comparison and evaluation methods to assess the accuracy of the models.

Off-The-Shelf 116

Off-The-Shelf Artificial Inteligence Machine Learning Weak Development Team 116

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 83

Data Insurance Metrics eBook 83

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. This goes beyond data and algorithms.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

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

OCTOBER 3, 2019

People analytics is the analysis of employee-related data using tools and metrics. Dashboard with key metrics on recruiting, workforce composition, diversity, wellbeing, business impact, and learning. Descriptive analytics is used to gather and analyze data that represents the current state of things or historical events.

Analytics 16

Analytics Company Off-The-Shelf How To 16

Altexsoft

AUGUST 21, 2019

Data obsession is all the rage today, as all businesses struggle to get data. But, unlike oil, data itself costs nothing, unless you can make sense of it. Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data.

Development 44

Development Software Engineering Data Engineering Architecture 44

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?

Artificial Inteligence 86

Artificial Inteligence Machine Learning Weak Development Team Construction 86

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

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 102

Engineering Comparison Open Source Scalability 102

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 75

Systems Review Metrics Trends Performance 75

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. Processing.

Data 76

Data Storage Analytics Data Center 76

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.

Artificial Inteligence 85

Artificial Inteligence Machine Learning Enterprise Telecommunications 85

Netflix Tech

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Infrastructure 90

Infrastructure Data Technical Review Systems Review 90

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.

Artificial Inteligence 89

Artificial Inteligence Machine Learning Software Review Conference 89

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 98

Performance Cloud Data Storage 98