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Why automation = acceleration
Why automation = acceleration
Why automation = acceleration
Hi Md Abul
Automation doesn’t mean losing control – it means empowering developers to innovate securely.
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Find out in our free API governance guide.
Let’s explore how automation can unlock time, creativity, and compliance for your teams.
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Tyk, Huckletree 199 Bishopsgate, Broadgate, London, City of London EC2M 3TY, United Kingdom, +44 (0)20 3409 1911
by "Carol from Tyk" <communities@tyk.io> - 11:18 - 2 Jul 2025 -
AI leadership: Ready or not, it’s yours
AI leadership: Ready or not, it’s yours
Hi MD Abul,
With the Fourth of July this week, most people are thinking about fireworks, cookouts, and a much-needed day off.
But for a lot of CIOs and IT leaders I’ve talked to lately, this week feels less like a break and more like a pressure cooker.
Whether they’re ready or not, they’re being asked to take full ownership of their company’s AI strategy.
Not just implementation. Not just infrastructure.
The whole thing.One CIO put it to me bluntly last week:
“We’ve gone from running systems to reinventing the business, and I’m not sure everyone realizes that.”
AI is being seen as the next great leap forward. But behind the hype, here’s what’s really happening:
- CIOs are being asked to lead cross-functional innovation
- Leadership wants fast answers to complex questions
- Vendors are pushing AI-heavy roadmaps
- Meanwhile, core systems still need to be stable
The problem? Most organizations are still playing catch-up just to align on a strategy, let alone execute one.
At Digital Stratosphere this August, we’re dedicating time and space to help leaders make sense of all this.
From AI adoption roadmaps and governance frameworks to practical conversations about alignment, change, and risk, we’re pulling it all out of the weeds and into reality.
Join us in Denver! August 25th-27th
Use the code "eric50" for 50% off your ticket(s)!
And if you want to talk through where your organization stands, we’re offering private advisory sessions during the event, 15 minutes or less, tailored to your questions. Just reply and we’ll connect you with the right person.
Enjoy the holiday. Rest up.
Because when the fireworks fade, the real work picks back up.Best regards,
Eric Kimberling
by "Eric Kimberling" <eric.kimberling@thirdstage-consulting.com> - 02:13 - 2 Jul 2025 -
How can multinationals adapt to geopolitical shifts?
On McKinsey Perspectives
by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:18 - 2 Jul 2025 -
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by "Anne" <Anne@telsto-cn.com> - 11:09 - 1 Jul 2025 -
Invitation To Tender - Supply and Installation of Gypsum - Bay Views Residential Buildings , Plot 7-27 & 7-28 , Mina Al Arab , Ras Al Khaimah, UAE
Dear TeamRAK Properties is pleased to invite you to participate in the tender for Supply and Installation of Gypsum Residential building works - Bay Views Residential Buildings , Plot 7-27 & 7-28 , Mina Al Arab , Ras Al Khaimah, UAE
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Tender Submission Deadline:
Duly completed tender offers, prepared in line with this invitation and the instructions provided, must be submitted in soft copy via email.
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RAK PROPERTIES PJSC
Julphar Towers, Ras Al Khaimah, UAE.
P.O. Box 31113.
Think before you print.Disclaimer :The information contained in this communication from the sender is confidential. It is intended solely for use by the recipient. If you are not the recipient, you are hereby notified that any disclosure, copying, distribution or taking action in relation of the contents of this information is strictly prohibited and may be unlawful. If you received this message by mistake, please reply to this message and follow with its deletion, so that we can ensure you don’t receive another email in the future. Thank you for your cooperation and understanding.
by " RAKP Tenders tenders@rakproperties.ae" <anish.sales@a2zqatar.net> - 07:27 - 1 Jul 2025 -
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by "Suore Stedile" <suorestedile596@gmail.com> - 07:17 - 1 Jul 2025 -
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by "sales02" <sales02@koocut-tools.com> - 06:44 - 1 Jul 2025 -
How Spotify Uses GenAI and ML to Annotate a Hundred Million Tracks
How Spotify Uses GenAI and ML to Annotate a Hundred Million Tracks
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Disclaimer: The details in this post have been derived from the articles shared online by the Spotify Engineering Team. All credit for the technical details goes to the Spotify Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Spotify applies machine learning across its catalog to support key features.
One set of models assigns tracks and albums to the correct artist pages, handling cases where metadata is missing, inconsistent, or duplicated. Another set analyzes podcasts to detect platform policy violations. These models review audio, video, and metadata to flag restricted content before it reaches listeners.
All of these activities depend on large volumes of high-quality annotations. These annotations act as the ground truth for model training and evaluation. Without them, model accuracy drops, feedback loops fail, and feature development slows down. As the number of use cases increased, the existing annotation workflows at Spotify became a bottleneck. Each team built isolated tools, managed their reviewers, and shipped data through manual processes that didn’t scale or integrate with machine learning pipelines.
The problem was structural. Annotation was treated as an isolated task instead of a core part of the machine learning workflow. There was no shared tooling, no centralized workforce model, and no infrastructure to automate annotation at scale.
This article explains how Spotify addressed these challenges by building an annotation platform designed to scale with its machine learning needs. It covers:
How was human expertise organized into a structured and scalable workflow?
What tools were built to support complex annotation tasks across different data types?
How was the infrastructure designed to integrate annotation directly into ML pipelines?
The trade-offs involved in balancing quality, cost, and speed.
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Moving from Manual Workflow to Scalable Annotation
The starting point was a straightforward machine learning (ML) classification task. The team needed annotations to evaluate model predictions and improve training quality, so they built a minimal pipeline to collect them.
They began by sampling model outputs and serving them to human annotators through simple scripts. Each annotation was reviewed, captured, and passed back into the system. The annotated data was then integrated directly into model training and evaluation workflows. There was no full-fledged platform yet, but just a focused attempt to connect annotations to something real and measurable.
Even with this basic setup, the results were significant:
The annotation corpus grew by a factor of ten.
Annotator throughput tripled compared to previous manual efforts.
This early success wasn’t just about volume. It showed that when annotation is directly tied into the model lifecycle, feedback loops become more useful and productivity improves. The outcome was enough to justify further investment.
From here, the focus shifted from running isolated tasks to building a dedicated platform that could generalize the workflow and support many ML use cases in parallel.
Platform Architecture
The overall platform architecture consists of three pillars. See the diagram below for reference:
Let’s look at each pillar in more detail.
1 - Scaling Human Expertise
To support large-scale annotation work, Spotify first focused on organizing its human annotation resources. Instead of treating annotators as a generic pool, the team defined clear roles with distinct responsibilities and escalation paths.
The annotation workforce was structured into three levels:
Core annotators handled the initial pass across most annotation cases. These were domain experts trained to apply consistent standards across large datasets.
Quality analysts acted as the escalation layer for complex or ambiguous cases. These were top-level reviewers who ensured that edge cases were resolved with precision, especially when annotations involved subjectivity or context-specific judgment.
Project managers worked across teams to coordinate annotation efforts. They connected ML engineers and product stakeholders with the annotation workforce, maintained training materials, gathered feedback, and adjusted data collection strategies as projects evolved.
In parallel with the human effort, Spotify also developed a configurable system powered by large language models.
This system operates in conjunction with human annotators and is designed to generate high-quality labels for cases that follow predictable patterns. It is not a full replacement but a complement that handles clear-cut examples, allowing humans to focus on harder problems.
See the diagram below:
This hybrid model significantly increased annotation throughput. By assigning the right cases to the right annotator (human or machine), Spotify was able to expand its dataset coverage at a lower cost and with higher consistency.
2 - Building Annotation Tooling for Complex Tasks
As annotation needs grew beyond simple classification, Spotify expanded its tooling to support a wide range of complex, multimodal tasks.
Early projects focused on basic question-answer formats, but new use cases required more flexible and interactive workflows. These included:
Annotating specific segments in audio and video streams.
Handling natural language processing tasks involving context-sensitive labeling.
Supporting multi-label classification where multiple attributes must be tagged per item.
To support these varied requirements, the team invested in several core areas of tooling:
Custom interfaces were built to allow fast setup of new annotation tasks. This made it possible to launch new projects without writing custom code for each use case.
Back-office systems managed user access, assigned annotation tasks across multiple experts, and tracked the status of each project. This was critical for scaling to dozens of projects running simultaneously.
Project dashboards were introduced to give teams real-time visibility into key metrics such as:
Completion rates for each task.
Total annotation volume.
Per-annotator productivity.
See the diagram below for annotating tooling capabilities:
In cases where annotation tasks involved subjective interpretation or fine-grained distinctions, such as identifying background music layered into a podcast, different experts could produce conflicting results. To handle this, the system computed an agreement score across annotators. Items with low agreement were automatically escalated to the quality analysts for resolution.
This structure allowed multiple annotation projects to run in parallel with consistent oversight, predictable output quality, and tight feedback loops between engineers, annotators, and reviewers. It turned what was once a manual process into a managed and observable workflow.
3 - Foundational Infrastructure and Integration
To support annotation at Spotify scale, the platform infrastructure was designed to be flexible and tool-agnostic. No single tool can serve all annotation needs, so the team focused on building the right abstractions. These abstractions make it possible to integrate a variety of tools depending on the task, while keeping the core system consistent and maintainable.
The foundation includes:
Generic APIs and data models that support multiple types of annotation tools. This allows teams to choose the right interface for each task without being locked into a specific implementation.
Interoperable interfaces that let engineers swap tools, layer them together, or use specialized tools for audio, video, text, or metadata tasks without rewriting pipelines.
See the diagram below:
Integration was built across two levels of ML development:
For early-stage and experimental work, lightweight command-line tools and simple UIs were created. These allow teams to run ad hoc annotation projects with minimal overhead.
For production-grade workflows, the platform connects directly to Spotify’s internal batch orchestration systems and workflow infrastructure. This supports large-scale, long-running annotation jobs that need reliability, tracking, and throughput guarantees.
The result is a system that supports both fast iteration in research environments and stable operation in production pipelines.
Engineers can move between these modes without changing how they define tasks or access results. The infrastructure sits behind the tooling, but it is what allows the annotation platform to scale efficiently across diverse use cases.
Impact on Annotation Velocity
The shift from manual, fragmented workflows to a unified annotation platform resulted in a sharp increase in annotation throughput.
Internal metrics showed a sustained acceleration in annotation volume over time, driven by both improved tooling and more efficient workforce coordination. See the figure below that shows the rate of annotations over time.
Source: Spotify Engineering Blog This increase in velocity directly reduced the time required to develop and iterate on machine learning models. Teams were able to move faster across several dimensions:
Training cycles became shorter, as labeled data could be collected, reviewed, and integrated into models with fewer handoffs and delays.
Ground-truth quality improved, thanks to structured escalation paths, agreement scoring, and the ability to resolve edge cases consistently.
GenAI experimentation became more efficient since the platform supported high-volume annotation needs with less setup overhead and more reliable output.
As a result, ML teams could test hypotheses, refine models, and ship features faster. The annotation platform became a core enabler for iterative, data-driven development at scale.
Conclusion
Spotify’s annotation platform is built on a clear principle: scaling machine learning requires more than just more data or larger models.
It depends on structured, high-quality annotations delivered through systems that are efficient, adaptable, and integrated into the full model development lifecycle. Relying entirely on human labor can create bottlenecks. On the other hand, full automation without oversight can lead to quality drift. Real leverage comes from combining both, with humans providing context and judgment and automation handling volume and repeatability.
By moving from isolated workflows to a unified platform, Spotify turned annotation into a shared capability rather than a one-time cost. The implementation of standardized roles, modular tools, and consistent infrastructure allowed ML teams to build and iterate faster without rebuilding pipelines from scratch.
This approach supports fast experimentation and scaling across a wide range of use cases.
References:
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 1 Jul 2025 -
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by "Zimmerle Steagall" <zimmerlesteagall749@gmail.com> - 05:18 - 1 Jul 2025 -
Excelling as a leader: How to evolve
Email 5 of 7
How to evolve
In the previous session, we looked at the potential impact of gen AI and how leaders can leverage the technology to outcompete in an era of perpetual change. In this fifth email, we’ll explore how to evolve as a leader—from aspirations through actualization to passing the torch. Here’s where to start.
Hitting your marks is enough to succeed as an average leader. But if you’re reading this, we’re guessing you don’t want to be just an average leader. Excellent leadership—the kind that takes you to a C-suite office—takes hard work, perseverance, and the kind of difficult self-reflection that leads to personal growth.
“Yes, CEO is an extraordinary role with a lot of responsibility,” says McKinsey senior partner Carolyn Dewar. “But it’s a role that you can learn, and it’s a role that you can experience and live into as opposed to just being a secret club.”
The best leaders know that learning leads to evolution, at all stages of the leadership journey. Here are five steps to get started.This email contains information about McKinsey’s research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy.
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by "McKinsey Publishing Guides" <publishing@email.mckinsey.com> - 03:09 - 1 Jul 2025 -
High - Quality Hydraulic Products to Enhance Your Machinery Performance
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by "Halama Pedeare" <halamapedeare@gmail.com> - 01:34 - 1 Jul 2025 -
How are GLP-1s fueling demand for medical aesthetics?
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Changes in appearance. The rapid adoption of GLP-1 drugs is transforming the field of life sciences, with ripple effects that extend well beyond cardiometabolic and obesity care, McKinsey Senior Partners Nils Peters and Olivier Leclerc and their coauthors highlight. In the medical aesthetics industry, for example, the effects are already noticeable. GLP-1 medications are not only improving patients’ health but also altering their appearance, which is fueling demand for aesthetic products and services.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:24 - 1 Jul 2025 -
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