🎧In this episode, we explore how Netflix is transforming AI/ML workflows with the introduction of Configurable Metaflow—a powerful enhancement to its machine learning infrastructure. Metaflow, originally designed to simplify ML pipeline development, is now more flexible, scalable, and user-friendly than ever.

We dive into:

• The evolution of Metaflow and why Netflix needed a more configurable approach.
• How Configurable Metaflow enables seamless adaptation across diverse ML workloads.
• The benefits of decoupling configurations from code, allowing teams to scale and iterate faster.
• Key use cases at Netflix, from content recommendations to real-time data processing.
• What this means for the broader ML community and how engineers can leverage it.

Join us as we unpack how Netflix engineers are redefining ML workflow management with Configurable Metaflow—bringing speed, efficiency, and flexibility to AI-driven innovation.

🚀 Tune in and stay ahead in the ML game! 🎧

Blog link - https://netflixtechblog.com/introducing-configurable-metaflow-d2fb8e9ba1c6

In this episode, we explore how Pinterest’s engineering team deciphers metric fluctuations to uncover valuable insights and improve platform performance. We discuss how segmentation analysis helps break down key performance indicators, revealing hidden patterns that drive decision-making.

We dive into the tools and methodologies Pinterest uses to track and analyze metric movements, from data visualization to automated reporting, and share real-world case studies where deep analysis led to meaningful improvements in user engagement.

Finally, we touch on the challenges of metric tracking and what the future holds for enhancing performance analytics at Pinterest. If you’ve ever wondered how large-scale platforms make sense of their data, this episode is for you!

🎧 Tune in to learn:

• How metric segmentation reveals critical insights
• The tools Pinterest engineers use to track performance
• Real-world examples of problem-solving through data
• Challenges and future directions in metric analysis

For more insights, check out the original article on the Pinterest Engineering Blog: The Quest to Understand Metric Movements.

Welcome to today’s episode, where we dive into how Pinterest has revolutionized content retrieval with a large-scale learned retrieval system. With billions of pins and users, delivering relevant content efficiently is no small feat. Traditional search methods, reliant on keyword matching and manual feature engineering, often struggled to capture the complexity of user intent.

In response, Pinterest adopted an embedding-based retrieval system, leveraging deep learning to create high-dimensional vector representations of content and user queries. This shift has enabled faster, more accurate, and highly personalized content recommendations at scale.

In this episode, we’ll explore the challenges Pinterest faced, the architecture behind this system, and the impact it has had on user engagement. Stay tuned as we break down the future of large-scale retrieval systems and what this means for AI-driven recommendations!

Blog Post- https://medium.com/pinterest-engineering/establishing-a-large-scale-learned-retrieval-system-at-pinterest-eb0eaf7b92c5

DeepSeek has taken the AI world by storm, sparking excitement, skepticism, and heated debates. Is this the next big leap in AI reasoning, or is it just another overhyped model? In this episode, we peel back the layers of DeepSeek-R1 and DeepSeek-V3, diving into the technology behind its Mixture of Experts (MoE), Multi-Head Latent Attention (MLA), Multi-Token Prediction (MTP), and Reinforcement Learning (GRPO) approaches. We also take a hard look at the training costs—is it really just $5.6M, or is the actual number closer to $80M-$100M?

Join us as we break down:

• DeepSeek’s novel architecture & how it compares to OpenAI’s models
• Why MoE and MLA matter for AI efficiency
• How DeepSeek trained on 2,048 H800 GPUs in record time
• The real cost of training—did DeepSeek underestimate their numbers?
• What this means for the future of AI models

At the end of the episode, we answer the big question: DeepSeek – WOW or MEH?

Key Topics Discussed:

• DeepSeek-R1 vs. OpenAI’s GPT models
• Reinforcement Learning (GRPO) and why it’s a big deal
• DeepSeek-V3’s 671B parameters and 37B active parameters
• The economics of training large AI models—real vs. reported costs
• The impact of MoE, MLA, and MTP on AI inference & efficiency

References & Further Reading:

• DeepSeek-R1 Official Paper: https://arxiv.org/abs/2501.12948
• Philschmid blog: https://www.philschmid.de/deepseek-r1
• DeepSeek Cost Breakdown: Reddit Discussion
• DeepSeek AI's Official Announcement: DeepSeek AI Homepage

Explore the full engineering blog here: https://research.google/blog/chain-of-agents-large-language-models-collaborating-on-long-context-tasks/

Welcome to Blog Bytes! Today, we're diving into the fascinating world of large language models. While LLMs have wowed us with their abilities in reasoning, knowledge retrieval, and text generation, they often stumble when handling long inputs—making tasks like extended summarization and detailed question answering a real challenge.

At NeurIPS 2024, a breakthrough came with the introduction of the Chain-of-Agents framework. This innovative approach leverages multiple agents working together through natural language to overcome context length limitations, significantly boosting performance on long-context tasks. In our discussion, we'll explore how CoA outperforms traditional methods, achieving up to a 10% improvement over existing baselines.

Stay tuned as we unpack the potential of Chain-of-Agents and what it means for the future of LLMs!

Explore the full engineering blog here: https://medium.com/pinterest-engineering/advancements-in-embedding-based-retrieval-at-pinterest-homefeed-d7d7971a409e

Explore the full engineering blog here: https://www.linkedin.com/blog/engineering/open-source/liger-kernel-open-source-ecosystem-for-efficient-llm-training

Explore the full engineering blog here: https://developer.nvidia.com/blog/mastering-llm-techniques-evaluation/

This NVIDIA technical blog post discusses the challenges and strategies for evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems. It highlights the inadequacy of traditional metrics due to LLMs' diverse and unpredictable outputs, emphasizing the need for robust evaluation techniques. The post introduces NVIDIA NeMo Evaluator, a tool designed to address these challenges by offering customizable evaluation pipelines and various metrics, including both numeric and non-numeric approaches like LLM-as-a-judge. Several academic benchmarks and evaluation strategies are detailed, along with specific metrics for assessing RAG systems' retrieval and generation components. The authors ultimately promote NeMo Evaluator as a solution to streamline the complex process of LLM evaluation.