RandomTrees

APRIL 12, 2024

Model retraining is a critical component of any robust MLOps stack, playing a fundamental role in ensuring the longevity and effectiveness of machine learning models. What is Model Retraining? This iterative process ensures that the model remains aligned with the evolving patterns and dynamics present in the underlying data.

Machine Learning 52

Machine Learning Datasets Systems Process 52

Knowledge Hut

MARCH 29, 2024

This blog post is all about product prototyping, and I'm goin g to delve deep into this topic as we go along with the discussion on various aspects of it. Product prototyping is a primary model of a product, serving as a real representation of an idea before mass production. What Is a Product Prototype?

Manufacturing 52

Manufacturing Electronics Software Engineer Software Engineering 52

Lyft Engineering

APRIL 24, 2024

Business acumen and domain knowledge on specific product areas is critical as well. Over the years, we have successfully launched lecture series on topics such as experimentation, observational causal inference, structural causal modeling, and reinforcement learning. production-level code. The CS4DS course addresses this gap.

Data Science 58

Data Science Building Computer Science Software Engineer 58

Netflix Tech

MARCH 7, 2024

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

Systems 90

Systems Media Machine Learning Data Warehouse 90

ThoughtSpot

JANUARY 31, 2024

Recent advances in AI and machine learning are not only changing the way we interact with data, but also pushing those of us who build analytics and BI platforms to think critically about how our products can best serve our customers moving forward. Some will always love getting hands-on with data—but that’s no longer the only option.

BI 88

BI Business Intelligence Machine Learning Data 88

LinkedIn Engineering

OCTOBER 19, 2023

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers. The release of Apache Beam in 2016 proved to be a game-changer for LinkedIn.

Process 119

Process Lambda Architecture Kafka Machine Learning 119

dbt Developer Hub

DECEMBER 11, 2023

There’s nothing quite like the feeling of launching a new product. Once the dust settles and the product is in the wild, the next thing the team needs to do is track how the product is doing. Answering these questions is vital to understanding the success of any product launch. How many users do we have?

Finance 64

Finance BI SQL Cloud 64

Engineering at Meta

DECEMBER 19, 2023

On July 5, 2023, Meta launched Threads, the newest product in our family of apps, to an unprecedented success that saw it garner over 100 million sign ups in its first five days. The seamless scale that people experienced as they signed up by the millions came on the shoulders of over a decade of infrastructure and product development.

Programming Language 141

Programming Language Engineering Database Coding 141

KDnuggets

JULY 31, 2023

A user-friendly platform for operating large language models (LLMs) in production, with features such as fine-tuning, serving, deployment, and monitoring of any LLMs.

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Cloudera

MARCH 26, 2024

How do you adapt a foundational model to your specific needs? Cloudera has had an in-house expertise in Machine Learning and has been helping customers deliver machine learning applications, build ML models, and deliver ML ops for the past decade. Companies must act now in order to stay in the AI Race. said Dr.

Machine Learning 78

Machine Learning Banking Professional Services Architecture 78

Engineering at Meta

NOVEMBER 15, 2023

Meta is building for the future of AI at every level – from hardware like MTIA v1 , Meta’s first-generation AI inference accelerator to publicly released models like Llama 2 , Meta’s next-generation large language model, as well as new generative AI (GenAI) tools like Code Llama.

Building 86

Building Engineering Transportation Software Engineer 86

Knowledge Hut

APRIL 25, 2024

Recognizing the difference between big data and machine learning is crucial since big data involves managing and processing extensive datasets, while machine learning revolves around creating algorithms and models to extract valuable information and make data-driven predictions.

Machine Learning 52

Machine Learning Big Data Unstructured Data Data Mining 52

Towards Data Science

DECEMBER 1, 2023

Storage —  Snowflake Snowflake, a cloud-based data warehouse tailored for analytical needs, will serve as our data storage solution. Here you can experiment with your models, but also you can copy my examples for Google Calendar data. As of now, Fivetran offers 14-day free trial —  link. Registration is very straightforward.

Data Engineering 75

Data Engineering Data Engineer Project Engineering 75

Tweag

APRIL 17, 2024

In other words, Nix is now known enough as to serve as a conversation starter: the company might not use it, but many people have heard about it and were very interested to hear insights from big contributors like Tweag, the Modus Create OSPO. Cybersecurity Many security products were represented.

Cloud 76

Cloud Consulting Portfolio Programming 76

Snowflake

OCTOBER 18, 2023

Product-led growth (PLG) is a business model that emerged in the last decade with the enormous success of vendors like Slack and Datadog. Unlike traditional sales-led models, PLG models cut out the middlemen (sales reps, for example) and let customers just download and use the product without third-party onboarding.

Designing 78

Designing Accessible Accessibility Systems 78

LinkedIn Engineering

JANUARY 11, 2024

For engineering leaders, the focus in 2024 will be on how to best harness the potential of this technology and navigate its associated challenges - from rewriting their playbooks and establishing the right technical building blocks, to identifying the talent needed to fully leverage AI in their products and processes.

Engineering 91

Engineering Architecture Java Software Engineer 91

Pinterest Engineering

SEPTEMBER 20, 2023

“Everyone” has been the north star for our Inclusive AI and Inclusive Product teams. These teams work together to ensure algorithmic fairness, inclusive design, and representation are an integral part of our platform and product experience. Our commitment is evidenced by our history of building products that champion inclusivity.

Building 84

Building Pipeline-centric Machine Learning Datasets 84

Monte Carlo

JANUARY 3, 2024

But building a generative AI model that actually drives business value is hard. Think about a productivity application you might use everyday to share organizational knowledge. That’s why a RAG model (or sometimes, a fine tuned model) is so important for Gen AI initiatives. Data teams are scrambling to answer the call.

Technology 113

Technology Database Data Governance Data Engineering 113

Data Engineering Weekly

JANUARY 21, 2024

link] DataDog: Scaling Self-Serve Analytics: The Tools Empowering 5,000 Employees Datadog shared insights at the Crunch Conference on scaling self-serve analytics for Datadog's 5,000 employees. It’s built for data practitioners, so models are transparent, and it gives you control to tweak and tune.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

LinkedIn Engineering

APRIL 20, 2023

That’s why developing models for viral spam content detection is critically important. To understand how viral spam content flows through member networks and is detected before it goes viral, it is necessary to stay updated with the latest forms of spam content being uploaded on the platform and modify our models accordingly.

Machine Learning 124

Machine Learning Utilities Designing Accessible 124

Data Engineering Weekly

MARCH 24, 2024

OpenAI has more production deployments, but Google is catching on to it. link] Uber: Model Excellence Scores - A Framework for Enhancing the Quality of Machine Learning Systems at Scale Uber writes about Model Excellence Scores (MES), a framework to improve the quality of machine learning (ML) systems at scale.

Data Engineering 62

Data Engineering Data Engineer Engineering Metadata 62

DoorDash Engineering

NOVEMBER 28, 2023

Figure 2: Phases of machine learning lifecycle Workbench Evolution ML Portal motivation and backstory The ML platform team started by building a simple UI to automate the model testing process through a web application called the ML Portal. We soon started adding more functionality to this UI.

Machine Learning 90

Machine Learning Pipeline-centric Data Science Designing 90

Pinterest Engineering

OCTOBER 31, 2023

PinCompute aligns with the Platform as a Service (PaaS) cloud computing model, in that it abstracts away the undifferentiated heavy lifting of managing infrastructure and Kubernetes and enables users to focus on the unique aspects of their applications. Figure 4: PinCompute Primitives: PinPod, PinApp, and PinScaler.

Architecture 95

Architecture Pipeline-centric Accessible Accessibility 95

Knowledge Hut

JANUARY 23, 2024

The Software Development Life Cycle in software development is something I've come to understand as a structured process that plays a crucial role in producing high-quality, cost-effective software within the shortest production time possible. These models, each with their set of stages, ensure success in software development.

Manufacturing 52

Manufacturing Coding Programming Designing 52

Rockset

SEPTEMBER 26, 2023

Real-time AI is the future, and AI models have demonstrated incredible potential for predicting and generating media in various business domains. For the best results, these models must be informed by relevant data. Often, domain-specific applications work well enough when the models are only periodically retrained.

Metadata 64

Metadata Kafka Cloud Database 64

Ripple Engineering

MARCH 30, 2023

Summary Managing the entire lifecycle of a machine learning (ML) model from inception to deployment in production can be a daunting task involving multiple systems and lots of moving parts. This blog outlines Ripple’s general design and approach for machine learning model lifecycle management using Databricks.

Machine Learning 98

Machine Learning AWS Metadata Data Collection 98

Engineering at Meta

SEPTEMBER 7, 2023

Given that training contemporary ML models often requires thousands of graphics processing units (GPUs), these benchmarks should ideally be executed on a similarly vast number of GPUs. Recognizing this, Chakra introduces a standardized schema for performance modeling, termed the Chakra execution trace.

Metadata 101

Metadata Utilities Systems Designing 101

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Serving Data – operational database. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift. Here are the key stages: .

Machine Learning 98

Machine Learning Manufacturing Data Collection Data Science 98

Cloudera

DECEMBER 19, 2023

In fact, most of the public servants I spoke with were predominantly cautious about the current limitations of generative AI, and underscored the need to ensure that models are used responsibly and ethically. As also expected, most had experimented on their own with large language models (LLM) and image generators. Trust your data.

Government 85

Government Education Unstructured Data Datasets 85

Scott Logic

NOVEMBER 22, 2023

In this post, I’ll provide an overview of the capability models that are out there, summarise the distinct capabilities required for AI/ML projects, and make the case for what I believe to be the most important capability your organisation will require. How do they sit alongside other capabilities, such as DevOps and Data Capability models?

Software Engineer 52

Software Engineer Software Engineering Project Engineering 52

Cloudera

MARCH 22, 2024

This evolution underscores our commitment to not just meet, but exceed the expectations of the enterprises and organizations we serve. Cloudera’s commitment to customer success remains a cornerstone of our service model. For those transitioning from CentOS Linux (or other distributions), Cloudera will offer an easy migration path.

Cloud 110

Cloud Certification Systems Architecture 110

Data Engineering Weekly

FEBRUARY 18, 2024

Benn Stancil: The problem was the product - How the modern data stack got lost Generated via DallE-3 The week is full of retrospect for the Modern Data Stack :-) The author shares a unique point of view on how the lack of collaboration among the MDS companies leads to its loss of popularity. Please add your thoughts if you live in Europe.

Data Engineering 62

Data Engineering Data Engineer Engineering Data 62

Towards Data Science

FEBRUARY 6, 2024

This generalisation makes their data models complex and cryptic and require domain expertise. Searching for data Imagine being a data engineer/analyst tasked with identifying the top-selling products within your company. Accessibility : I could easily request access to these data products.

Systems 85

Systems Raw Data Data Cleanse Metadata 85

Knowledge Hut

DECEMBER 22, 2023

This will lead to two major changes in the way businesses and professionals work: automation of routine tasks and use of data analysis to serve both current and future industry trends. In 2024, ITIL ® will help businesses customize their products and services so that they can live up to the specific expectations of their customers.

Certification 98

Certification Government Data Analysis Technology 98

Edureka

APRIL 24, 2024

Generative AI models reshape the boundaries of creativity and functionality in technology, mimicking human-like inventive abilities with progressive tools and algorithms. trillion by 2032, it is essential to learn gen AI capabilities from creating an AI model to data augmentation.

Entertainment 40

Entertainment Healthcare Pharmaceutical Datasets 40

Snowflake

SEPTEMBER 5, 2023

Snowflake’s fully managed platform helps minimize TCO by achieving faster time to insights and production, decreasing unplanned downtime and operational risks, and reducing business costs through customers paying only for actual usage. And so TFS looked to Snowflake to serve as a core part of this new data platform, which it called EDP 2.0.

Retail 78

Retail Finance Data Pipeline SQL 78

Snowflake

SEPTEMBER 13, 2023

Building your data product is only the beginning. Once you’ve determined your first “P” (the product), it’s time to think about the remaining three “Ps” of the famous four Ps of marketing : place, promotion and pricing. Pricing has stumped product teams since the dawn of time (well, almost). The price is right—or is it?

Food 90

Food Retail Hospitality Data 90