
Explore the evolving role of ML engineers amid the rise of large language models. Learn to evaluate API suitability, address privacy concerns, and adapt skills for future projects.

Explore efficient scaling and deployment of LLMs, focusing on decentralized approaches, custom model training, and practical insights for building and implementing AI solutions.

Explore the latest trends in Generative AI, gaining practical insights for startups to leverage advancements and enhance products in this dynamic field.

Challenge traditional ML principles and explore how LLMs are reshaping methodology. Discover which rules still apply and which are becoming obsolete in the era of self-supervising, extrapolating models.

Explore test-driven development's crucial role in prompt engineering for AI systems. Learn to create reliable, predictable AI products through effective TDD practices.

Explore LLM-based feature extraction for operational optimization in AI applications. Learn how to leverage GPT-3.5 for efficient content understanding and improved accuracy in product development.

Explore key differences between using 100M vs 100B parameter models in production, covering MLOps processes, prompt testing, automated training, and real-time inference for conversational AI.

Discover strategies for integrating LLM features into existing products, focusing on practical use cases beyond chatbots. Learn to plan, roadmap, and measure success of LLM implementations in various industries.

Explore Tecton's journey in developing a Python-based platform for powering large-scale real-time AI applications. Learn about reliable solutions for productionizing AI using only Python skills.

Explore the transition to open source ML models for production, focusing on security, performance, and scalability. Learn how AI companies leverage these models for core workloads.

Explore fine-tuning models for health insurance appeals, cloud resource utilization, and on-premises Kubernetes deployment. Learn about unexpected challenges in server setup and graphics card installation.

Explore MLflow Projects to create execution pipelines locally and leverage Databricks for tracking and artifact storage in machine learning workflows.

Learn to log and load quantized LLama.cpp models in MLflow, create Python classes, and perform inference using Microsoft Phi3 mini 128k in GGUF format.

Fine-tune Mistral 7B using PEFT, QLora, and MLFlow techniques. Master advanced LLM optimization for improved performance and efficiency.

Explore Databricks Secrets and Azure KeyVault integration for secure ML & MLOps workflows in data science and machine learning projects.

Explore MLOps practices and how software operators enhance ML workload performance, addressing challenges like infrastructure costs in this insightful talk by Andreea Munteanu.

Explore DataOps, MLOps, DevOps, and AIOps with Michelle and Andreea. Discover how these practices optimize enterprise data, machine learning, and AI projects for improved efficiency and quality.

Explore ML operations and the Continuous Delivery for Machine Learning Model in this concise introduction to MLOps.

Discover how Kedro framework enhances ML project management, focusing on reproducibility, flexibility, and production readiness. Learn to integrate Kedro for improved efficiency in Python-based projects.

Explore MLOps evolution in e-commerce, focusing on LLMs' impact, key investments, and real-world examples from Booking.com's AI implementation and scaling challenges.

Explore Accenture's approach to evolving data and AI roles, circular pathways, and the changing landscape of data platforms. Learn about new skills needed for data architects and governance experts in the era of generative AI.