The hottest MLOps Substack posts right now

And their main takeaways
Category
Top Technology Topics

Deep Learning Weekly: Issue 336

Deep Learning Weekly • 648 implied HN points • 17 Jan 24
🕹 Technology AI Machine Learning Generative AI MLOps Libraries
  1. This week's deep learning topics include generative AI in enterprises, query pipelines, and closed-loop verifiable code generation.
  2. Updates in MLOps & LLMOps cover CI/CD practices, multi-replica endpoints, and serverless solutions like Pinecone.
  3. Learning insights include generating images from audio, understanding self-attention in LLMs, and fine-tuning models using PyTorch tools.

SwirlAI Table of Contents

SwirlAI Newsletter • 432 implied HN points • 28 Jun 23
🕹 Technology Data Engineering MLOps System Design Systems Thinking
  1. The newsletter provides a Table of Contents with more than 90 topics, making it easier to find the content of interest.
  2. Topics covered include Data Engineering fundamentals, Spark architecture, Kafka use cases, MLOps deployment processes, System Design examples, and more.
  3. If interested, it's recommended to support the author's work by subscribing and sharing the content.

MLOps Basics - For Data Engineers.

Data Engineering Central • 393 implied HN points • 15 May 23
🕹 Technology Machine Learning Data Engineering MLOps Training Automation
  1. Working on Machine Learning as a Data Engineer is not as hard as it seems - it falls somewhere in the middle of difficulty.
  2. Machine Learning work for Data Engineers focuses on MLOps like feature stores, model prediction, automation, and metadata storage.
  3. The key aspects of MLOps include automating tasks, using tools like Apache Airflow, and managing metadata for a stable ML environment.

SAI #22: Decomposing the Data System.

SwirlAI Newsletter • 294 implied HN points • 18 Mar 23
🕹 Technology Data science Data Engineering MLOps Machine Learning Data Systems
  1. Learning to decompose a data system is crucial for better reasoning and understanding of large infrastructure
  2. Decomposing a data system allows for scalability, identification of bottlenecks, and total event processing latency optimization
  3. The different layers in a data system include data ingestion, transformation, and serving layers, each with specific functions and technologies
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Deep Learning Weekly: Issue 339

Deep Learning Weekly • 137 implied HN points • 07 Feb 24
🕹 Technology Industry MLOps Learning Libraries & Code Papers & Publications
  1. Google introduced a new model for time-series forecasting called TimesFM, pre-trained on a large time-series corpus.
  2. Adept introduced a new multimodal model called Adept Fuyu-Heavy designed for digital agents.
  3. There are various articles and papers covering topics like LLM platforms, code implementation like LoRA, and new generation AI models.

Deep Learning Weekly: Issue #319

Deep Learning Weekly • 275 implied HN points • 20 Sep 23
🕹 Technology Industry MLOps Learning Libraries & Code Papers & Publications
  1. Google's Bard Extension now scans Gmail, Docs, and Drive for answers with its AI chatbot.
  2. Optimizing LLMs from a dataset perspective involves strategies for finetuning using existing datasets.
  3. Influence Functions offer insights into large language model generalization, improving accuracy and robustness through dataset reinforcement.

Deep Learning Weekly: Issue #308

Deep Learning Weekly • 255 implied HN points • 05 Jul 23
🕹 Technology AI Machine Learning MLOps Learning Libraries & Code
  1. This week's issue of Deep Learning Weekly covers Meta's AI system cards, real-time machine learning foundations at Lyft, and a local code generator tool using Microsoft's guidance library.
  2. Industry news includes Inflection AI's $1.3B investment, Meta AI sharing 22 system cards on AI experiences, and Unity launching new AI platforms for real-time 3D creation.
  3. In the MLOps and Learning sections, topics range from dealing with train-serve skew in ML models to using LLMs for data extraction and building local code generators.

Deep Learning Weekly: Issue #293

Deep Learning Weekly • 216 implied HN points • 22 Mar 23
🕹 Technology Industry MLOps Learning Libraries & Code Papers & Publications
  1. This week in deep learning includes Microsoft 365 Copilot and emergent abilities of large language models.
  2. Learn about industry trends like signed parameters for ML model deployments and new AI startups.
  3. Explore informative posts on prompt engineering, computer vision in robotics, and papers on video generation and symbolic regression.

Deep Learning Weekly : Issue #309

Deep Learning Weekly • 216 implied HN points • 12 Jul 23
🕹 Technology AI ML MLOps Libraries Papers
  1. Deep Learning Weekly Issue #309 covers topics like Code Interpreter on ChatGPT Plus and ML system design with 200 case studies.
  2. Industry innovations include AI-generated chart captions and Nvidia's AI approach to carbon capture.
  3. Learning section highlights topics like Tiny Audio Diffusion and Swin Transformer for object recognition.

Confidential Computing; DataOps and MLOps

Gradient Flow • 99 implied HN points • 23 Nov 21
🕹 Technology DataOps MLOps Data Exchange Podcasts
  1. Confidential Computing involves protecting data in all states: at rest, in use, and in transit.
  2. Confidential Computing tools focus on safeguarding data while being used, a difficult task due to the need for data to be unencrypted for computation.
  3. DataOps and MLOps are important for modern data governance and management, emphasizing the need for strong metadata platforms and strategies to avoid MLOps mistakes.

A Brief History of MLOps in Three Acts

Laszlo’s Newsletter • 54 implied HN points • 20 Feb 23
🕹 Technology MLOps Data science Machine Learning
  1. The evolution of MLOps tools started from handling big data and SQL to deployment, feature stores, model monitoring, and more
  2. The increasing complexity of ML models led to the development of tools like XGBoost, TensorFlow, PyTorch, and the need for distributed computing
  3. Machine Learning Engineers play a crucial role in navigating the ever-changing landscape of MLOps tools and technologies

Gradient Flow #41: What’s New in Data Engineering; MLOps Anti-Patterns

Gradient Flow • 19 implied HN points • 12 Aug 21
🕹 Technology Data Engineering MLOps Cloud Computing Open Source Machine Learning
  1. The podcast discusses changes in the data science role and tools, along with insights on new data engineering trends.
  2. An overview of new developments in tools and infrastructure, including a chatbot, recommendation system, and MLOps anti-patterns to avoid mistakes.
  3. Recommendations cover topics like the evolution of PyTorch, guidelines for open datasets stewardship, and insights into the analytical application stack.

MLOps Hype, Open Source Risks & AI Research Fallacies

Sector 6 | The Newsletter of AIM • 0 implied HN points • 18 Jul 21
🕹 Technology AI Data science Research Open Source MLOps
  1. MLOps is gaining popularity, but we should be careful not to get too caught up in the hype. It's important to evaluate its real benefits before jumping in.
  2. Open source tools in AI can be risky, as they may have hidden vulnerabilities. It's wise to properly assess security and reliability before using them.
  3. There are common fallacies in AI research that can mislead people. Being aware of these misconceptions can help in making better-informed decisions and understanding the field better.