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Come and be part of ODSC West’s AI Expo & Demo Hall ! There you’ll hear from Ivan Nardini, Developer Relations Engineer at Google Cloud and discover the latest advancements in AI and learn how to leverage Google Cloud’s powerful tools and infrastructure to drive innovation in your organization.
Mini-Bootcamp and VIP Pass holders will have access to four live virtual sessions on data science fundamentals. Confirmed sessions include: An Introduction to Data Wrangling with SQL with Sheamus McGovern, Software Architect, DataEngineer, and AI expert Programming with Data: Python and Pandas with Daniel Gerlanc, Sr.
Join us there for 3 full days of instruction in machine learning, deep learning, NLP and LLMs, dataengineering, and more! Here are our picks for a few can’t-miss sessions from Keynote speakers and data science rockstars. There’s less than a week to go until ODSC East 2023. Register by Friday to save 20%.
We will kick the conference off with a virtual Keynote talk from Henk Boelman, Senior Cloud Advocate at Microsoft, Build and Deploy PyTorch models with Azure Machine Learning. Day 2 also marks the last day you can meet with the organizations and startups shaping the future of AI and data science at the AI Expo and Demo Hall.
Since data is left in its raw form within the data lake, it’s easier for data teams to experiment with models and analysis techniques with greater flexibility. So let’s take a look at a few of the leading industry examples of data lakes. Snowflake Snowflake is a cross-cloud platform that looks to break down data silos.
DataEngineering Summit The DataEngineering Summit , co-located with ODSC West, is your ticket to optimizing efficiency, enhancing scalability, and successfully tackling the toughest data challenges. Every participant walks away with something, and you can compete in-person or virtually. Sign me up!
The AI Expo and Demo Hall At the AI Expo and Demo Hall you’ll have the opportunity to connect one-on-one with representatives from industry-leading organizations in MLOps, Machine Learning, NLP, Time Series Data, and much more. Included in your open pass, you’ll get access to.
Enterprise data architects, dataengineers, and business leaders from around the globe gathered in New York last week for the 3-day Strata Data Conference , which featured new technologies, innovations, and many collaborative ideas. Industry’s first self-service information platform for Microsoft Azure. free trial.
At the AI Expo and Demo Hall as part of ODSC West in a few weeks, you’ll have the opportunity to meet one-on-one with representatives from industry-leading organizations like Microsoft Azure, Hewlett Packard, Iguazio, neo4j, Tangent Works, Qwak, Cloudera, and others.
Video of the Week: Getting Into DataEngineering In this video, Joe Reis CEO of Ternary Data provides valuable insights on navigating the current economic climate and what it means for aspiring dataengineers. Don’t let old market data point you in the wrong direction. Check out a few of them below.
While a data analyst isn’t expected to know more nuanced skills like deep learning or NLP, a data analyst should know basic data science, machine learning algorithms, automation, and data mining as additional techniques to help further analytics. Cloud Services: Google Cloud Platform, AWS, Azure.
For a short demo on Snowpark, be sure to check out the video below. Utilizing Streamlit as a Front-End At this point, we have all of our data processing, model training, inference, and model evaluation steps set up with Snowpark. The marketplace serves as a source of third-party data to supplement your internal datasets.
AI Cloud brings together any type of data, from any source, giving you a unique, global view of insights that drive your business. All of this is part of a unified, integrated platform spanning dataengineering, machine learning, decision intelligence, and continuous AI – the entire AI lifecycle.
Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, dataengineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for dataengineering and MLOps workflows.
This approach incorporates relevant data from a data store into prompts, providing large language models with additional context to help answer queries. To provide an example, traditional structured data such as a user’s demographic information can be provided to an AI application to create a more personable experience.
How to use the Codex models to work with code - Azure OpenAI Service Codex is the model powering Github Copilot. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. The article has good points with any LLM Use prompt to guide.
I switched from analytics to data science, then to machine learning, then to dataengineering, then to MLOps. For me, it was a little bit of a longer journey because I kind of had dataengineering and cloud engineering and DevOps engineering in between. Quite fun, quite chaotic at times.
An ML platform standardizes the technology stack for your data team around best practices to reduce incidental complexities with machine learning and better enable teams across projects and workflows. We ask this during product demos, user and support calls, and on our MLOps LIVE podcast. Dataengineers are mostly in charge of it.
These AI & DataEngineering Sessions Are a Must-Attend at ODSC East2025 Whether youre navigating AI decision support, technical debt in dataengineering, or the future of autonomous agents, these sessions provide actionable strategies, real-world case studies, and cutting-edge frameworks to help you stayahead.
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