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Balancing performance and cost is critical in the UAV industry. At Micro Magic Inc, we provide high-precision inertial sensors that deliver exceptional value without compromising on quality. Our cost-effective solutions are trusted by leading drone manufacturers worldwide.
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by "Karaeski Martirosov" <martirosovkaraeski804@gmail.com> - 12:55 - 10 Jun 2025 -
How Lyft Uses ML to Make 100 Million Predictions A Day
How Lyft Uses ML to Make 100 Million Predictions A Day
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Disclaimer: The details in this post have been derived from the articles/videos shared online by the Lyft Engineering Team. All credit for the technical details goes to the Lyft Engineering Team. The links to the original articles and videos 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.
Hundreds of millions of machine learning inferences power decisions at Lyft every day. These aren’t back-office batch jobs. They’re live, high-stakes predictions driving every corner of the experience from pricing a ride to flagging fraud, predicting ETAs to deciding which driver gets which incentive.
Each inference runs under pressure and a single-digit millisecond budget. This translates to millions of requests per second. Dozens of teams, each with different needs and models, are pushing updates on their schedules. The challenge is staying flexible without falling apart.
Real-time ML at scale breaks into two kinds of problems:
Data Plane pressure: Everything that happens in the hot path. This includes CPU and memory usage, network bottlenecks, inference latency, and throughput ceilings.
Control Plane complexity: This is everything around the model. Think of aspects like deployment and rollback, versioning, retraining, backward compatibility, experimentation, ownership, and isolation.
Early on, Lyft leaned on a shared monolithic service to serve ML models across the company. However, the monolith created more friction than flexibility. Teams couldn’t upgrade libraries independently. Deployments clashed and ownership blurred. Small changes in one model risked breaking another, and incident investigation turned into detective work.
The need was clear: build a serving platform that makes model deployment feel as natural as writing the model itself. It had to be fast, flexible, and team-friendly without hiding the messy realities of inference at scale.
In this article, we’ll look at how Lyft built an architecture to accomplish this requirement and the challenges they faced.
Architecture and System Components
LyftLearn Serving doesn’t reinvent the wheel. It slots neatly into the microservices foundation already powering the rest of Lyft. The goal wasn’t to build a bespoke ML serving engine from scratch. It was to extend proven infrastructure with just enough intelligence to handle real-time inference, without bloating the system or boxing teams.
At the core is a dedicated microservice: lightweight, composable, and self-contained. Each team runs its instance, backed by Lyft's service mesh and container orchestration stack. The result: fast deploys, predictable behavior, and clean ownership boundaries.
Let’s break down this architecture flow diagram:
HTTP Serving Layer
Every request to a LyftLearn Serving service hits an HTTP endpoint first. This interface is built using Flask, a minimalist Python web framework. While Flask alone wouldn’t scale to production workloads, it’s paired with Gunicorn, a pre-fork WSGI server designed for high concurrency.
To make this stack production-grade, Lyft optimized the setup to align with Envoy, the service mesh that sits in front of all internal microservices. These optimizations ensure:
Low tail latency under high request volume.
Smooth connection handling across Envoy-Gunicorn boundaries.
Resilience to transient network blips.
This layer keeps the HTTP interface thin and efficient, just enough to route requests and parse payloads.
Core Serving Library
This is where the real logic lives. The LyftLearn Serving library handles the heavy lifting:
Model loading and unloading: Dynamically brings models into memory from saved artifacts
Versioning: Tracks and manages different model versions cleanly
Shadowing: Enables safe testing by running inference on new models in parallel, without affecting live results
Monitoring and logging: Emits structured logs, metrics, and tracing for each inference request
Prediction logging: Captures outputs for later audit, analytics, or model debugging
This library is the common runtime used across all teams. It centralizes the “platform contract” so individual teams don’t need to re-implement the basics. But it doesn’t restrict customization.
Custom ML/Predict Code
The core library is dependency-injected with team-owned inference logic. Every team provides two Python functions:
def load(self, file: str) -> Any: # Custom deserialization logic for the trained model ... def predict(self, features: Any) -> Any: # Custom inference logic using the loaded model ...Source: Lyft Engineering Blog
This design keeps the platform flexible. A team can use any model structure, feature format, or business logic, as long as it adheres to the basic interface. This works because the predicted path is decoupled from the transport and orchestration layers.
Third-Party ML Library Support
LyftLearn Serving makes no assumptions about the ML framework. Whether the model uses TensorFlow, PyTorch, LightGBM, XGBoost, or a home-grown solution, it doesn’t matter.
As long as the model loads and predicts through Python, it’s compatible. This lets teams:
Upgrade to newer framework versions without coordination.
Use niche or experimental libraries.
Avoid vendor lock-in or rigid SDKs.
Framework choice becomes a modeler’s decision, not a platform constraint.
Integration with Lyft Infrastructure
The microservice integrates deeply with Lyft’s existing production stack:
Metrics and tracing plug into the company-wide observability pipeline.
Logs and prediction events feed into central analytics systems.
Kubernetes handles service orchestration and autoscaling.
Envoy service mesh provides secure, discoverable network communication.
This alignment avoids duplicating effort. Teams inherit baseline reliability, visibility, and security from the rest of Lyft’s infrastructure, without needing to configure it themselves.
Isolation and Ownership Principles
When dozens of teams deploy and serve ML models independently, shared infrastructure quickly becomes shared pain. One broken deploy can block five others. A single library upgrade triggers weeks of coordination and debugging turns into blame-shifting. That’s what LyftLearn Serving was built to avoid.
The foundation of its design is hard isolation by repository, not as a policy, but as a technical boundary enforced at every layer of the stack.
One Repo, One Service, One Owner
Every team using LyftLearn Serving gets its own GitHub repository. This repo isn’t just for code, but it defines the entire model-serving lifecycle:
The service code includes custom load() and predict() logic.
The configuration files for deployment and orchestration.
The integration hooks for CI/CD, monitoring, and metrics.
There’s no central repository to manage and no shared runtime to coordinate. If a team needs five models, they can choose to host them in one repo or split them across five.
Independent Deploy Pipelines
Each repo comes with its deploy pipeline, fully decoupled from others. This includes:
Staging and production environments.
CI jobs that run model self-tests and linting.
Version tagging and release promotion.
If one team pushes broken code, it doesn’t affect anyone else. If another needs to hotfix a bug, they can deploy instantly. Isolation removes the need for cross-team coordination during high-stakes production changes.
Runtime Isolation via Kubernetes and Envoy
LyftLearn Serving runs on top of Lyft’s Kubernetes and Envoy infrastructure. The platform assigns each team:
A dedicated namespace in the Envoy service mesh
Isolated Kubernetes resources (pods, services, config maps)
Customizable CPU, memory, and replica settings
Team-specific autoscaling and alerting configs
This ensures that runtime faults, whether it’s a memory leaks, high CPU usage, or bad deployment. A surge in traffic to one team’s model won’t starve resources for another. A crash in one container doesn’t bring down the serving infrastructure.
Tooling: Config Generator
Getting a model into production shouldn’t mean learning multiple configuration formats, wiring up runtime secrets, or debugging broken deploys caused by missing database entries.
To streamline this, LyftLearn Serving includes a Config Generator: a bootstrapping tool that wires up everything needed to go from zero to a working ML serving microservice. Spinning up a new LyftLearn Serving instance involves stitching together pieces from across the infrastructure stack:
Terraform for provisioning cloud infrastructure.
YAML and Salt for Kubernetes and service mesh configuration.
Python is used for defining the serving interface.
Runtime secrets for secure credential access.
Database entries for model versioning or feature lookups.
Envoy mesh registration for service discovery.
Expecting every ML team to hand-craft this setup would be a recipe for drift, duplication, and onboarding delays. The config generator collapses that complexity into a few guided inputs.
The generator runs on Yeoman, a scaffolding framework commonly used for bootstrapping web projects, but customized here for Lyft’s internal systems.
A new team running the generator walks through a short interactive session:
Define the team name.
Specify the service namespace.
Choose optional integrations (for example, logging pipelines and model shadowing).
The tool then emits a fully-formed GitHub repo with:
Working example code for the load() and predict() functions.
Pre-wired deployment scripts and infrastructure configs.
Built-in test data scaffolds and CI setup.
Hooks into monitoring and observability.
Once the repo is generated and the code is committed, the team gets a functioning microservice, ready to accept models, run inference, and serve real traffic inside Lyft’s mesh. Teams can iterate on the model logic immediately, without first untangling infrastructure.
Model Self-Testing System
Model serving can often drift when a new dependency sneaks in, slightly changing output behavior. For example, a training script gets updated, but no one notices the prediction shift. Or, a container upgrade silently breaks deserialization. By the time someone spots the drop in performance, millions of bad inferences have already shipped.
To fight this, LyftLearn Serving introduces a built-in Model Self-Testing System. It’s a contract embedded inside the model itself, designed to verify behavior at the two points that matter most: before merge and after deploy.
Every model class defines a test_data property: structured sample inputs with expected outputs:
class SampleModel(TrainableModel): @property def test_data(self) -> pd.DataFrame: return pd.DataFrame([ [[1, 0, 0], 1], # Input should predict close to 1 [[1, 1, 0], 1] ], columns=["input", "score"])Source: Lyft Engineering Blog
This isn’t a full dataset. It’s a minimal set of hand-picked examples that act as canaries. If a change breaks expected behavior on these inputs, something deeper is likely wrong. The test data travels with the model binary and becomes part of the serving lifecycle.
Two checkpoints that matter are as follows:
During Deployment Runtime
After a model loads inside a LyftLearn Serving instance, it immediately runs predictions on its test_data. The results:
Get logged and surfaced in metrics dashboards.
Trigger alerts if predictions drift too far from expected.
Provide an immediate signal of runtime integrity.
This catches subtle breakages caused by environment mismatches. For example, a model trained in Python 3.8 but deployed into a Python 3.10 container with incompatible dependencies.
During Pull Requests
When a developer opens a PR in the model repo, CI kicks in. It performs the following activities:
Loads the new model artifacts.
Runs predictions on the stored test_data.
Compares outputs against previously known-good results.
If the outputs shift beyond an acceptable delta, the PR fails, even if the code compiles and the service builds cleanly. The diagram below shows a typical development flow:
Inference Request Lifecycle
A real-time inference system lives in the milliseconds between an HTTP request and a JSON response. That tiny window holds a lot more than model math. It’s where routing, validation, prediction, logging, and monitoring converge.
LyftLearn Serving keeps the inference path slim but structured. Every request follows a predictable, hardened lifecycle that allows flexibility without sacrificing control.
Here’s a step-by-step on how the request gets served:
Request hits the Flask endpoint: The service exposes an /infer route via Flask, backed by Gunicorn workers. Envoy handles the upstream routing. The incoming payload is parsed and handed off to the core serving library.
The core library retrieves the model: Using the model_id, the runtime looks up the corresponding model. If the model isn’t already loaded into memory, the system calls the team-supplied load() function and loads it on demand. This is where versioning logic, caching, and model lifecycle controls kick in. There’s no global registry and no shared memory. Each service owns and manages its models.
Input validation and preprocessing: Before running inference, the platform performs sanity checks on the features object, such as type or shape validation, required field presence, and optional model-specific hooks. This step guards against malformed inputs and prevents undefined behavior in downstream logic.
User-defined predict() runs the inference: Once the inputs are deemed valid, the system hands control to the custom predict() function written by the modeler. This function converts inputs into the expected format and calls the underlying ML framework (for example, LightGBM, TensorFlow) to return the prediction output. The predict() function is hot-path code. It runs millions of times per day. Its performance and correctness directly affect latency and downstream decisions.
Logs, metrics, and analytics: After the output is generated, the platform automatically logs the request and response for debugging and audit trails. It also emits latency, throughput, and error rate metrics and triggers analytics events that flow into dashboards or real-time monitoring. This observability layer ensures every inference can be traced and every service behavior can be measured.
Response returned to the caller: Finally, the result is packaged into a JSON response and returned via Flask. From request to response, the entire path is optimized for speed, traceability, and safety.
Conclusion
Serving machine learning models in real time isn’t just about throughput or latency. It’s about creating systems that teams can trust, evolve, and debug without friction.
LyftLearn Serving didn’t emerge from a clean slate or greenfield design. It was built under pressure to scale, to isolate, and to keep dozens of teams moving fast without stepping on each other’s toes.
Several lessons surfaced along the way, and they’re worth understanding:
“Model” means different things to different people. A serialized object, a training script, and a prediction endpoint all fall under the same label. Without clear definitions across tooling and teams, confusion spreads fast.
Documentation is part of the product. If teams can’t onboard, debug, or extend without asking the platform team, the system doesn’t scale. LyftLearn Serving treats docs as first-class citizens.
Once a model is deployed behind an API, someone, somewhere, will keep calling it. Therefore, stability is a requirement for any serving system that expects to live in production.
Trade-offs aren’t optional. Seamless UX conflicts with flexible composability. Structured pipelines clash with custom workflows. Every decision makes something easier and something else harder. The trick is knowing who the system is really for and being honest about what’s being optimized.
Power users shape the platform. Build for the most advanced, most demanding teams first. If the platform meets their needs, it’ll likely meet everyone else's. If not, it won’t scale past the first few adopters.
Prefer boring tech when it works. Stability, debuggability, and operational maturity are key aspects to consider.
LyftLearn Serving is still evolving, but its foundations hold. It doesn’t try to hide complexity, but it isolates it. Also, it enforces a contract around how the models behave in production.
References:
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by "Mamaclay Kalua" <mamaclaykalua49@gmail.com> - 07:47 - 9 Jun 2025 -
¡Tu equipo esta invitado a una clase gratuita de ventas!
Hola, buen día:
Fue un gusto platicar con tu equipo que muy amablemente me compartió este correo porque vio que podría aportar mucho para su empresa, les agradezco mucho la apertura.
Me permito escribirte porque estoy convencido de que lo que hacemos puede aportar muchísimo valor al área comercial de la empresa.Seré breve:
Me gustaría coordinar una demo gratuita, una clase virtual en vivo de unos 25 minutos, pensada para equipos comerciales que quieren mejorar su tasa de cierres con técnicas prácticas y de impacto inmediato.Durante esta sesión, además de mostrar lo que hacemos, los participantes se llevan ideas que pueden aplicar desde el primer día. No es una presentación de ventas, es una experiencia real de lo que brindamos.
Entendí que no estaba en sus manos el tomar esta decisión, agradecería mucho si pudieras ayudarme a canalizar esta propuesta con la persona adecuada (tal vez el responsable del área comercial o quien lidere al equipo de ventas).
Te dejo aquí un enlace con más información y algunos casos reales de empresas con las que ya estamos colaborando:
Sitio web: https://factorstudios.io/
Pagina de historias de éxito: https://www.campamentodeventa.com/casos-de-exito
Instagram César (CEO del área de capacitaciones corporativas): https://www.instagram.com/cesarjorqueraq/
Instagram Tino (Fundador de la universidad del closer): https://www.instagram.com/tino.mossu/
Instagram de Teo (Fundador del área de implementación de vendedores en empresas):
https://www.instagram.com/teotinivelli/
by "FactorStudios" <ventasfactorstudios@gmail.com> - 02:12 - 9 Jun 2025 -
Our New Book, Mobile System Design Interview, Is Now Available
Our New Book, Mobile System Design Interview, Is Now Available
Forwarded this email? Subscribe here for moreOur new book, Mobile System Design Interview, is available on Amazon!
Book author: Manuel Vicente Vivo
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Chapter 2: A Framework for Mobile System Design Interviews
Chapter 3: Design a News Feed App
Chapter 4: Design a Chat App
Chapter 5: Design a Stock Trading App
Chapter 6: Design a Pagination Library
Chapter 7: Design a Hotel Reservation App
Chapter 8: Design the Google Drive App
Chapter 9: Design the YouTube app
Chapter 10: Mobile System Design Building Blocks
Quick Reference Cheat Sheet for MSD Interview
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by "ByteByteGo" <bytebytego@substack.com> - 11:37 - 9 Jun 2025 -
Managing yourself: How to learn new skills
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How to learn new skills
In the previous session, we looked at the importance of both individuals and organizations getting clear about their purpose. Now we’ll hone in on the significance of learning new skills to get yourself closer to meeting your personal and professional goals.
Feeling stuck at work, and in life, is not uncommon. But it can become particularly bothersome when you’re in the position of managing others who rely on your guidance and wisdom. Fortunately, there’s a way to get unstuck: Learning new skills is key to greasing the wheels of creativity, personal development, and productivity.
Today’s fast-paced, tech-driven environment makes workplace learning even more crucial. “Sustainable prosperity will likely hinge on how well societies prepare the workforces of tomorrow,” say McKinsey’s Stephen Hall, Dirk Schmautzer, and coauthors.
Here’s how you—and your organization—can get intentional about learning.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.
You received this email because you subscribed to “The McKinsey Publishing Guide to managing yourself and others.”
Copyright © 2025 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "McKinsey Publishing Guides" <publishing@email.mckinsey.com> - 03:05 - 9 Jun 2025 -
Is there turbulence ahead for airlines? A leader’s guide
Leading Off
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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 Leading Off. 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
What’s the next destination for the aviation industry? After a postpandemic upswing in demand for air travel and improvements in the sector’s financial performance, airlines now face a more uncertain trajectory. A new McKinsey report on the state of aviation explores how economic and geopolitical tensions, consumer behavior, aircraft shortages, and other forces may shape the industry’s direction of travel. This week, we examine trends in the aviation sector and how these developments could affect travel planning for organizations—and their leaders—across industries.
The airline industry has a long record of mixed financial performance but recently saw its largest share of value-creating carriers in nearly 30 years. The airlines that reported detailed financial results recorded a collective $5 billion economic loss in 2024—a “remarkable feat” compared with the industry’s $30 billion collective economic loss in 2019, according to McKinsey’s Steve Saxon and Jaap Bouwer. They observe that recent improvements can largely be traced to better execution in six areas of value creation, among them: balancing capacity and demand, generating ancillary revenue (such as fees for premium seats and extra baggage), earning a reputation for reliability, and creating strong organizational health. In 2025, aviation leaders may need to navigate several developments that could affect economic performance, such as changes in business and consumer sentiments, a potential recession, and tariffs. “Healthier organizations will meet challenges with the cross-functional nimbleness that’s required to rapidly respond to changing external conditions,” the authors say.
That’s the number of myths about airline retailing that carriers should bear in mind as they refine their strategies. According to McKinsey’s Alex Cosmas and Nina Lind and their coauthors, these myths include the notions that more personalized offers always create better travel experiences; that travelers prefer to separately book flights, hotels, and other aspects of their trips; and that digital booking convenience has sped up travelers’ decision-making.
That’s McKinsey Senior Partner Vik Krishnan and coauthors on how airlines can modernize their planning to respond to emerging challenges. Airlines’ planning efforts are complicated by numerous factors—many of which are out of their control—including travel demand, weather, IT failures, and global crises. The authors suggest that airlines expand pandemic-era efforts that helped improve coordination between their commercial and operations functions, such as creating cross-functional planning teams, adopting agile processes, and improving tech capabilities. “While there have been green shoots, many airlines continue to plan in much the same way they have for decades,” they say. “Turning a critical eye on the current state of planning could help airlines imagine a more efficient and effective future.”
AI and analytics can be important tools to help the airline industry understand and satisfy travelers’ evolving preferences. That’s the view of British Airways (BA) CEO and chairman Sean Doyle, who is leading the largest modernization effort in the company’s history. In an interview with McKinsey Senior Partner Stephanie Hauser, Doyle notes that improving BA’s data infrastructure and employing machine learning will help improve its decision-making in areas ranging from aircraft maintenance to passenger meals. “There are ways the whole industry can use its data more effectively,” he adds. “For example, a customer flying on an airline will create about 58 different touchpoints of data. There are customers on some long-haul flights for nine or ten hours, which means there are many opportunities to give customers a more personalized experience through better use of data.”
With global uncertainty in the air, some companies are tightening travel budgets while weighing the potential business impact of geopolitical shifts. Still, overall demand for business travel has been rising after a few years of instability. McKinsey Partner Ryan Mann cites a key reason for this upturn: “People have found that there is no substitute for face-to-face engagement with their leaders and colleagues. Employees craving in-person encounters bodes extremely well for business travel.” Corporate travelers’ habits have changed in other ways. Some are extending their stays in certain destinations, alternating leisure days with workdays while they’re on the road. “It's important to remember, obvious as it may be, that business travelers have identities outside of work,” Mann says. “Many of the same elements that can inspire and energize them when engaging in leisure travel might also enhance their business outings.”
Lead by keeping tabs on aviation trends.
— Edited by Eric Quiñones, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 02:17 - 9 Jun 2025 -
How can CIOs address rising geopolitical risks?
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
•
Under pressure. In the past decade, geopolitical developments have put the global IT operating model under immense pressure—and corporate CIOs are on the front line. They are confronting a patchwork of data laws, increased costs and complexity, and escalating cyberattacks. But current IT policies and models generally aren’t up to the task of addressing the range of geopolitical risks, McKinsey Partner James Kaplan and his coauthors explain. They say that CIOs will need to revise their risk practices, create new ones, and adjust their global operations—by, for example, partnering with business leaders to help shape decisions regarding geopolitical risk.
—Edited by Belinda Yu, editor, Atlanta
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Copyright © 2025 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:48 - 9 Jun 2025
