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Each month, ODSC has a few insightful webinars that touch on a range of issues that are important in the data science world, from use cases of machinelearning models, to new techniques/frameworks, and more. So here’s a summary of a few recent webinars that you’ll want to watch. Watch on-demand here.
The Future of the Single Source of Truth is an Open DataLake Organizations that strive for high-performance data systems are increasingly turning towards the ELT (Extract, Load, Transform) model using an open datalake. Register by Friday for 50% off! See them here!
Popular MachineLearning Libraries, Ethical Interactions Between Humans and AI, and 10 AI Startups in APAC to Follow Demystifying MachineLearning: Popular ML Libraries and Tools In this comprehensive guide, we will demystify machinelearning, breaking it down into digestible concepts for beginners, including some popular ML libraries to use.
Choosing a DataLake Format: What to Actually Look For The differences between many datalake products today might not matter as much as you think. When choosing a datalake, here’s something else to consider. Use this guide to get started with your prompt engineering skills!
Accelerating Decisions with Third-Party Data in Financial Services On-Demand Webinar Your ability to make confident decisions based on relevant factors relies on accurate data filled with context. That’s why enriching your analysis with trusted, fit-for-use, third-party data is key to ensuring long-term success.
Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure MachineLearning Studio.
Video of the Week: Open-Source Data Curation and Governance for Large and Growing DataLakes In this talk, we’ll take a deep dive into open-source data curation and governance for large and growing datalakes with Roger Dev, Senior Architect and machinelearning expert at LexisNexis Risk Solutions.
As businesses increasingly turn to cloud solutions, Azure stands out as a leading platform for Data Science, offering powerful tools and services for advanced analytics and MachineLearning. This roadmap aims to guide aspiring Azure Data Scientists through the essential steps to build a successful career.
In a nod to AC/DC, a wink to Gartner’s research report, Data Catalogs Are the New Black in Data Management and Analytics , and inspiration from the inaugural Forrester Wave : MachineLearningData Catalogs , we have temporarily set aside our Alation orange and have been rocking “black” for the Alation MLDC World Tour.
Considering the nature of the time series dataset, Q4 also realized that it would have to continuously perform incremental pre-training as new data came in. This would have required a dedicated cross-disciplinary team with expertise in data science, machinelearning, and domain knowledge.
Intelligence automatically surfaces clues in the data to remove the manual effort otherwise required for discovery; intelligence can also flag sensitive data within the huge volume, variety, and veracity of data facing the modern enterprise. Guided Navigation Guided navigation helps data stewards locate sensitive data.
Building an Effective OSS Management Layer for Your DataLake Ahead of her ODSC West session on OSS management layers, the speaker discusses how datalakes can benefit from this system. Q&A session with NVIDIA Thursday, October 17th, 2024, 01:00 PM Ready to dive into the world of AI innovation?
Data domains group data logically by, for example, business function, product line, geographic region, or any other construct. Alation increases understanding of data Alation leverages machinelearning alongside human curation to speed up data search and understanding. Subscribe to Alation's Blog.
Machinelearning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements. Many organizations store their data in structured formats within data warehouses and datalakes.
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