Netflix Tech

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

Machine Learning 84

Machine Learning Artificial Inteligence Data Systems Review 84

Dzone - DevOps

NOVEMBER 7, 2023

Databricks is a cloud-based platform designed to simplify the process of building data engineering pipelines and developing machine learning models.

Data Engineering 76

Data Engineering Engineering Machine Learning Artificial Inteligence 76

Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

Machine Learning 98

Machine Learning Artificial Inteligence Data Data Engineering 98

CIO

FEBRUARY 14, 2023

By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making.

Artificial Inteligence 290

Artificial Inteligence Strategy Data Machine Learning 290

CIO

JUNE 7, 2022

What is data analytics? Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. What are the four types of data analytics?

Analytics 338

Analytics Data Analysis Business Analytics 338

CIO

JUNE 23, 2023

According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital business objectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.

Strategy 290

Strategy Technical Review Data Software Review 290

Apiumhub

JANUARY 26, 2022

Data engineer roles have gained significant popularity in recent years. Number of studies show that the number of data engineering job listings has increased by 50% over the year. And data science provides us with methods to make use of this data. Who are data engineers?

Data Engineering 52

Data Engineering Engineering Data Machine Learning 52

O'Reilly Media - Data

AUGUST 16, 2018

The O’Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production. In this episode of the Data Show , I spoke with Harish Doddi , co-founder and CEO of Datatron , a startup focused on helping companies deploy and manage machine learning models.

Machine Learning 105

Machine Learning Artificial Inteligence Data Engineering Compliance 105

O'Reilly Media - Data

JULY 18, 2018

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. Economic value of data.

Machine Learning 137

Machine Learning Artificial Inteligence Data Marketing 137

Altexsoft

JUNE 29, 2021

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

O'Reilly Media - Data

MAY 24, 2018

The O’Reilly Data Show Podcast: A special episode to mark the 100th episode. This episode of the Data Show marks our 100th episode. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them.

Data Engineering 117

Data Engineering Engineering Data Machine Learning 117

O'Reilly Media - Data

APRIL 3, 2018

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

Machine Learning 155

Machine Learning Artificial Inteligence Company Case Study 155

Perficient

FEBRUARY 13, 2023

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform Using COPY and SNOWPIPE is the fastest and cheapest way to load data. Especially important is the ability to reload and reprocess the data in the event of an error.

Data Engineering 69

Data Engineering Engineering Data Storage 69

Altexsoft

JULY 23, 2020

When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 81

Machine Learning Artificial Inteligence Weak Development Team Construction 81

Trigent

JUNE 20, 2022

Businesses worldwide are realizing the importance of the considerable volume of data they possess and the need to extract value from it and incorporate it into enhancing Customer Experience or simplifying operational processes for improved results. This is where data engineering services providers come into play.

Data Engineering 52

Data Engineering Engineering Data Strategy 52

Mentormate

DECEMBER 12, 2023

In a previous blog post, we introduced a five-phase framework to plan out Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The Traditional Machine Learning Workflow Initiating a traditional ML project begins with collecting data. Duplicated records are identified and rectified.

Artificial Inteligence 52

Artificial Inteligence Machine Learning Technical Review Development 52

TechCrunch

FEBRUARY 2, 2022

RudderStack , a platform that focuses on helping businesses build their customer data platforms to improve their analytics and marketing efforts, today announced that it has raised a $56 million Series B round led by Insight Partners, with previous investors Kleiner Perkins and S28 Capital also participating. Image Credits: RudderStack.

Data 204

Data Machine Learning Artificial Inteligence Architecture 204

CIO

JULY 10, 2023

For a decade, Edmunds, an online resource for automotive inventory and information, has been struggling to consolidate its data infrastructure. Now, with the infrastructure side of its data house in order, the California-based company is envisioning a bold new future with AI and machine learning (ML) at its core.

Infrastructure 246

Infrastructure Artificial Inteligence Data Generative AI 246

Dataiku

JUNE 15, 2022

Together, Dataiku and Snowflake deliver exactly that to data engineers, data scientists, and developers and now offer a full complement of integrations to improve AI, machine learning (ML), and data and analytic applications.

Data Engineering 59

Data Engineering Engineering Data Development 59

InfoQ Culture Methods

FEBRUARY 25, 2021

Machine learning is a powerful new tool, but how does it fit in your agile development? Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. By Jay Palat.

Machine Learning 136

Machine Learning Artificial Inteligence Agile Development 136

CIO

SEPTEMBER 2, 2022

With IT leaders increasingly needing data scientists to gain game-changing insights from a growing deluge of data, hiring and retaining those key data personnel is taking on greater importance. Data scientists have the alchemy to turn data into insights. Gartner reported that a data scientist in Washington, D.C.,

Data 246

Data Machine Learning Artificial Inteligence Training 246

CIO

SEPTEMBER 1, 2022

With IT leaders increasingly needing data scientists to gain game-changing insights from a growing deluge of data, hiring and retaining those key data personnel is taking on greater importance. Data scientists have the alchemy to turn data into insights. Gartner reported that a data scientist in Washington, D.C.,

Data 246

Data Machine Learning Artificial Inteligence Training 246

TechCrunch

JULY 12, 2022

Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. At a high level, Tecton automates the process of building features using real-time data sources.

Artificial Inteligence 249

Artificial Inteligence Machine Learning Marketing Data Engineering 249

TechCrunch

NOVEMBER 14, 2022

The complexity of streaming data technologies – not just streaming video but any kind of streaming data – has created a headache around dealing with that high speed data processing. Real-time data startup Quix raises a $12.9M Accordingly, companies like Spark, Flink have spring up to address this ksqlDB.

Data 208

Data B2B Machine Learning Artificial Inteligence 208

Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Continuous Operations for Production Machine Learning (COPML) helps companies think about the entire life cycle of an ML model.

Machine Learning 119

Machine Learning Artificial Inteligence eBook Sustainability 119

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

CIO

SEPTEMBER 29, 2023

Upwork analyzed data from its platform, examining the types of projects companies hired freelancers for thus far this year, to identify the most sought-after AI skills on the market. Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer.

Generative AI 357

Generative AI ChatGPT Machine Learning Artificial Inteligence 357

Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle.

Machine Learning 131

Machine Learning Artificial Inteligence Data Applications 131

TechCrunch

MAY 18, 2022

Heartex, a startup that bills itself as an “open source” platform for data labeling, today announced that it landed $25 million in a Series A funding round led by Redpoint Ventures. We agreed that the only viable solution was to have internal teams with domain expertise be responsible for annotating and curating training data.

Open Source 203

Open Source Weak Development Team Artificial Inteligence Data 203

CIO

APRIL 12, 2023

By George Trujillo, Principal Data Strategist, DataStax I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. Real-time AI involves processing data for making decisions within a given time frame.

Artificial Inteligence 363

Artificial Inteligence Artificial Intelligence Machine Learning Agile 363

TechCrunch

NOVEMBER 18, 2021

In the early 2000s, most business-critical software was hosted on privately run data centers. DevOps fueled this shift to the cloud, as it gave decision-makers a sense of control over business-critical applications hosted outside their own data centers.

Enterprise 344

Enterprise Artificial Inteligence Data Engineering Data Center 344

TechCrunch

JANUARY 10, 2023

The need for data observability, or the ability to understand, diagnose and orchestrate data health across various IT tools, continues to grow as organizations adopt more apps and services. Other observability vendors with substantial backing behind them include Manta , Observe , Better Stack , Coralogix and Unravel Data.

Data 241

Data Software Review Technical Review Systems Review 241

InfoQ Culture Methods

JULY 28, 2020

To become a machine learning engineer, you have to interview. You have to gain relevant skills from books, courses, conferences, and projects. Include technologies, frameworks, and projects on your CV. In an interview, expect that you will be asked technical questions, insight questions, and programming questions.

Machine Learning 141

Machine Learning Artificial Inteligence Engineering How To 141

Netflix Tech

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. For example?—?clinical

Data Engineering 52

Data Engineering Engineering Data Culture 52

d2iq

FEBRUARY 19, 2021

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

Dataiku

FEBRUARY 24, 2022

Incorporating automation into ML projects can help data scientists or data engineers deploy (and keep) more work in production while reducing the amount of time spent on maintaining those projects once they’re in production.

Machine Learning 59

Machine Learning Artificial Inteligence Data Engineering Engineering 59