The Netflix TechBlog

FEBRUARY 13, 2023

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. Training Performance Media model training poses multiple system challenges in storage, network, and GPUs.

Media 299

Media Storage Infrastructure Systems 299

The Netflix TechBlog

MARCH 7, 2024

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. There are several ways to provide explainability to models but one way is to train an explainer model based on each trained model.

Systems 238

Systems Media Cache Open Source 238

The Netflix TechBlog

JANUARY 30, 2023

What data would you like to have if you were designing an asset suite? Images for the title “Purple Hearts” Creative Insights To create suites that are relevant, attractive, and authentic, we’ve relied on creative strategists and designers with intimate knowledge of the titles to recommend and create the right art for upcoming titles.

Media 264

Media Design Innovation Strategy 264

The Netflix TechBlog

NOVEMBER 11, 2022

In semi-supervised anomaly detection models, only a set of benign examples are required for training. Data Data Labeling For the task of anomaly detection in streaming platforms, as we have neither an already trained model nor any labeled data samples, we use structural a priori domain-specific rule-based assumptions, for data labeling.

C++ 323

C++ Metrics Tuning Strategy 323

Dynatrace

AUGUST 8, 2024

Employee training in cybersecurity best practices and maintaining up-to-date software and systems are also crucial. High demand Sudden spikes in demand can overwhelm systems that are not designed to handle such loads, leading to outages. This often occurs during major events, promotions, or unexpected surges in usage.

Software 260

Software Software Infrastructure Network 260

The Netflix TechBlog

APRIL 26, 2022

We built Axion primarily to remove any training-serving skew and make offline experimentation faster. We will share how its design has evolved over the years and the lessons learned while building it. To understand Axion’s design, we need to know the various components that interact with it.

Storage 197

Storage Design Scalability Latency 197

Dynatrace

SEPTEMBER 30, 2021

By carving the right AWS certification path, developers can even use their certification and training to advance their careers long term. Not only will they get much more out of the tools they use daily, but they’ll also be able to deliver superior functionality, efficiency, and performance to your customers.

AWS 222

AWS Best Practices Cloud Analytics 222

Dynatrace

SEPTEMBER 20, 2022

DevSecOps best practices provide guidelines to help organizations achieve efficient and secure application design, development, implementation, and management. Some DevSecOps best practices include the following: Security by design. The education of employees about security awareness. Disparate toolsets. Read report now!

Best Practices 275

Best Practices DevOps Education Serverless 275

DZone

FEBRUARY 27, 2021

As a Software Engineer, the mind is trained to seek optimizations in every aspect of development and ooze out every bit of available CPU Resource to deliver a performing application. This begins not only in designing the algorithm or coming out with efficient and robust architecture but right onto the choice of programming language.

Java 214

Java C++ Benchmarking Programming 214

The Netflix TechBlog

MARCH 4, 2024

We have deployed Auto Remediation in production for handling memory configuration errors and unclassified errors of Spark jobs and observed its efficiency and effectiveness (e.g., For efficient error handling, Netflix developed an error classification service, called Pensive, which leverages a rule-based classifier for error classification.

Tuning 219

Tuning Efficiency Big Data Engineering 219

Edge Perspectives

MARCH 26, 2019

In our Big Shift world, we confront the imperative of institutional innovation – shifting from institutional models built on scalable efficiency to institutional models built on scalable learning. I’ve written and spoken about this a lot over the years and one of the most common pushbacks I get is – “so, are you against efficiency?”

Efficiency 89

Efficiency Scalability Innovation Programming 89

The Morning Paper

AUGUST 25, 2019

Snuba: automating weak supervision to label training data Varma & Ré, VLDB 2019. It’s tackling the same fundamental problem: how to gather enough labeled data to train a model, and how to effectively use it in a weak supervision setting (supervised learning with noisy labels). It took me quite a while to get my head around this!

Logistics 76

Logistics Efficiency Design Performance 76

Dynatrace

JUNE 14, 2023

These areas together underpin the new Dynatrace Value Incentive Partner Program (VIPP), which we designed to provide more opportunities, more services, and more benefits to our partners. Accelerate business growth with the latest sales and technical training. Through a two-step approach, partners can become Dynatrace Services Endorsed.

Programming 192

Programming Best Practices Innovation Analytics 192

Dynatrace

MARCH 13, 2024

The following are the general requirements for reporting for the NIS2 directive: Risk management: Organizations are obligated to execute a comprehensive set of critical measures designed to effectively mitigate cyber risks. Address the root cause of cybersecurity incidents & fix vulnerabilities promptly.

Best Practices 240

Best Practices Education Strategy Analytics 240

All Things Distributed

MARCH 19, 2018

For example, training on more data means more accurate models. Last re:Invent, to make the problem of authoring, training, and hosting ML models easier, faster, and more reliable, we launched Amazon SageMaker. Machine learning models are usually trained tens or hundreds of times. In machine learning, more is usually more.

Scalability 132

Scalability Hardware AWS Tuning 132

The Netflix TechBlog

MAY 21, 2019

The device ecosystem is rich with partners ranging from Silicon-on-Chip (SoC) manufacturers, Original Design Manufacturer (ODM) and Original Equipment Manufacturer (OEM) vendors. Solving the above problems could help Netflix and our Partners save time and money during the entire lifecycle of device design, build, test, and certification.

Testing 164

Testing AWS Lambda Network 164

High Scalability

DECEMBER 16, 2022

This model of computing has become increasingly popular in recent years, as it offers a number of benefits, including cost savings, flexibility, scalability, and increased efficiency. I'm sorry, but as a large language model trained by OpenAI, I don't have the ability to browse the internet or keep up-to-date with current events.

Cloud 201

Cloud Serverless Internet Internet 201

Dynatrace

NOVEMBER 2, 2023

The result is smarter, data-driven solutions designed to manage cloud spend. FinOps becomes more critical as organizations grow The value of FinOps lies in its potential to move organizations toward financial success with smart, cost-efficient cloud spend implemented from day one. What is FinOps? FinOps behavioral change management.

Cloud 208

Cloud Strategy Retail Energy 208

Dynatrace

OCTOBER 14, 2021

Traditional AIOps: Traditional AIOps approaches are designed to reduce alerts and utilize machine-learning models to deliver correlation-based dashboards. They require extensive training, and real-user must spend valuable time filtering any false positives. training data) that the algorithm can then learn from.

Artificial Intelligence 224

Artificial Intelligence DevOps Innovation Metrics 224

Dynatrace

OCTOBER 5, 2022

Owning the responsibility and effort to build good cyber security practices now will improve your DevSecOps team’s overall productivity and efficiency in the future. Teams should be trained on security concerns in addition to their typical responsibilities. What is Cybersecurity Awareness Month all about? What does that mean?

Best Practices 206

Best Practices Education Monitoring Government 206

DZone

JUNE 24, 2024

High-performance storage systems can significantly reduce AI model training time. Xinnor partnered with DELTA Computer Products GMBH, a leading system integrator in Germany, to build a high-performance solution designed specifically for AI and HPC tasks.

Systems 130

Systems Software Software Storage 130

The Netflix TechBlog

DECEMBER 11, 2020

This gives us access to Netflix’s Java ecosystem, while also giving us the robust language features such as coroutines for efficient parallel fetches, and an expressive type system with null safety. We partnered with Netflix’s Developer Experience (DevEx) team to build out documentation, training materials, and tutorials for developers.

Architecture 257

Architecture Best Practices Engineering Open Source 257

The Netflix TechBlog

OCTOBER 23, 2019

Feature Overview Reproducibility Polynote promotes notebook reproducibility by design. for example, researchers might use Scala and Spark to generate training data (cleaning, subsampling, etc), while actual training might be done with popular Python ML libraries like tensorflow or scikit-learn.

Open Source 235

Open Source Code Website Availability 235

Dynatrace

MAY 8, 2023

Some of the benefits organizations seek from digital transformation journeys include the following: Increased DevOps automation and efficiency. Digital tools and technologies provide a more efficient way of doing things. Improved customer experience. federal agency.

Strategy 186

Strategy Retail DevOps Traffic 186

Dynatrace

MAY 2, 2024

As organizations train generative AI systems with critical data, they must be aware of the security and compliance risks. Security by design enhanced by unified observability and security – blog Business spend management company Soldo uses unified observability and security to implement efficient security-by-design and DevSecOps practices.

Artificial Intelligence 242

Artificial Intelligence Cloud Best Practices DevOps 242

The Netflix TechBlog

NOVEMBER 20, 2023

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. The incremental processing solution (IPS) described here has been designed to address the above problems. In this blog post, we talk about the landscape and the challenges in workflows at Netflix.

Processing 221

Processing Big Data Efficiency Engineering 221

The Netflix TechBlog

OCTOBER 18, 2022

These include ETL pipelines, ML model training workflows, batch jobs, etc. Similarly, ML model training workflows usually consist of tens of thousands of training jobs within a single workflow. But sometimes, it is not efficient. A large number of batch workflows run daily to serve various business needs.

Java 214

Java Scalability Traffic Architecture 214

The Netflix TechBlog

NOVEMBER 29, 2023

A vital aspect of such development is subjective testing with HDR encodes in order to generate training data. Bitrate versus quality comparison HDR-VMAF is designed to be format-agnostic — it measures the perceptual quality of HDR video signal regardless of its container format, for example, Dolby Vision or HDR10.

Open Source 262

Open Source Software Engineering Internet Internet 262

The Netflix TechBlog

JANUARY 10, 2024

Moving away from the use of dedicated instances that were constrained in quantity, we tapped into Netflix’s internal trough created due to autoscaling microservices, leading to significant improvements in computation elasticity as well as resource utilization efficiency. This drove the approach of the “release train”.

Processing 240

Processing Media Latency Innovation 240

The Netflix TechBlog

APRIL 29, 2019

Various software systems are needed to design, build, and operate this CDN infrastructure, and a significant number of them are written in Python. Demand Engineering Demand Engineering is responsible for Regional Failovers , Traffic Distribution, Capacity Operations and Fleet Efficiency of the Netflix cloud.

Open Source 280

Open Source Network Infrastructure Big Data 280

The Netflix TechBlog

DECEMBER 23, 2020

everything from the frontend API for netflix.com, to machine learning training workloads, to video encoders. This abstraction allows the compute team to influence the reliability, efficiency, and operability of the fleet via the scheduler. We do this for reliability, scalability, and efficiency reasons.

Media 297

Media Metrics Systems Processing 297

The Netflix TechBlog

MAY 28, 2021

Along the way, I discovered three things that would have a significant impact on my future trajectory: No lab for me : I did not like being in a lab, and I did not like the idea of spending a ton of time trying to improve the efficiency of some engineering part/system. Those steps can be generated in any number of ways (e.g.,

Healthcare 211

Healthcare Entertainment Analytics Programming 211

The Morning Paper

OCTOBER 18, 2020

What really blew me away, is that this result includes the time taken to train the model used! Radix Sort is carefully designed to make effective use of the L2 cache and sequential memory accesses, whereas Learned Sort is making random accesses all over the destination array. How can learned sort be adapted to make it cache-efficient?

Cache 137

Cache C++ Java Tuning 137

Scalegrid

JANUARY 12, 2024

This article analyzes cloud workloads, delving into their forms, functions, and how they influence the cost and efficiency of your cloud infrastructure. Executing cutting-edge intelligent apps’ deployment after successful training becomes much easier thanks primarily to this functionality made possible! Additionally.

Cloud 130

Cloud Virtualization Storage Efficiency 130

O'Reilly

JUNE 11, 2024

At O’Reilly, we’re not just building training materials about AI. It would have been very difficult to develop the expertise to build and train a model, and much more effective to work with a company that already has that expertise. This design decision was simple, but surprisingly important. Ours can and will.

Design 106

Design Open Source Database Programming 106

Dynatrace

NOVEMBER 15, 2021

There is nothing worse than your well-designed digital touchpoint simply falling offline. Having this real-time outlook enables your business to operate with efficiency by proactively identifying and altering for both hard and soft failures. What ruins a digital experience? Here are a few reasons user experiences can fail.

Mobile 229

Mobile Social Media Monitoring Metrics 229

O'Reilly

MAY 25, 2021

GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet. Indeed, Google recently announced LaMDA , a model at GPT-3 scale that is designed to participate in conversations. And the final paragraph of section 3.9.4

Social Media 140

Social Media Media Internet Internet 140