Read about the transformative potential of LLMs through a case study on holiday movie trends, interactive data visualizations, SQL analysis, and the current arbitrage opportunities in data analytics.
It is a story with 4 chapters that illustrates the journey from an entrepreneurial startup with a “one-man data team” to a scale-up with a data platform team.
This post covers:
- 2018: Batch BI
- 2019-2021: Streaming ingest & data activation
The text digs into the ever-evolving world of artificial intelligence, honing in on NLP and GAI, collectively known as Textual AI. It breaks down their definitions, goals, and applications, showing how NLP lays the groundwork for GAI's creative side.
Essential skills, from programming and containerization to Kubernetes, and exploring MLOps components like version control, CI/CD pipelines, orchestration and feature stores.
"LLMs cannot find reasoning errors, but can correct them!" examines LLMs by breaking down self-correction into mistake finding and output correction. Using the BIG-Bench Mistake dataset, it explores the LLMs' ability to identify logical mistakes, the utility of mistake-finding as a correctness proxy, and the generalizability of mistake-finding skills to unseen tasks.
In this one, João introduces two open-source solutions, Sentence Transformers, and Qdrant, aiming to address key questions about generating, storing, and querying these representations, illustrated through their application to the NPR News Portal Recommendation dataset.
This one explores disaster recovery for Amazon MWAA, offering solutions to protect against disruptions and integrate risk management into your business continuity plan. This post focuses on designing the DR architecture, with a future installment covering implementing components using AWS services.
This tutorial explores the use of LLMs in converting unstructured text into Knowledge Graphs, focusing on four methods that adhere to specific ontologies. It enhances data analysis and query formulation by structuring vast unstructured data efficiently.
This article delves into five distinct frontend libraries, each offering unique features and trade-offs. It guides through the strengths and drawbacks of Streamlit, Solara, Trame, ReactPy, and PyQt, providing a comprehensive overview to aid in selecting the ideal framework for specific projects.
Syft is OpenMined's open source stack that provides secure and private Data Science in Python. Syft decouples private data from model training, using techniques like Federated Learning, Differential Privacy, and Encrypted Computation.
Delve deep into the world of DevOps, focusing on Continuous Integration (CI) and Continuous Deployment (CD) within the realm of modern data engineering.
You’ll find:
- Azure Infrastructure as Code Automation with Terraform
- Storage account module with Terraform
- Automating Azure Data Factory with Terraform
- Testing and Validation of Results
Giovanni touches on valuable considerations for integrating AI into L&D systems, providing a practical guide for managers and the workforce. His journey from the early days of AI to the present underscores the transformative impact of information.
The formula is similar to previous editions three, about 30-minute practical talks followed by question-and-answer sessions with networking afterward.
Presentations:
- Scalable, secure, and sustainable NatWest Group’s MLOps platform
- MLflow iceberg: from basics to hidden depths
- Building a vector search engine