Hyperparameter Optimization, Achieving Responsible AI, and How to Hire Data Scientists
Many Models Training with Hyperparameter Optimization
This article presents an approach for you to train multiple machine learning models, optimizing the hyperparameters of each model in an automated way with Azure Machine Learning.
Optimizing Product Rankings for Competing Business Objectives
Here’s an overview of the problem of training “business-aware” recommender systems, what makes it difficult, and some lessons learned.
Responsible AI is Not an Option. Here’s How You Can Achieve it
You can easily achieve responsible AI by building a diverse team, ensuring explainability, and other easy tasks.
Machine Learning in the Geospatial Industry: A Beginners Guide
What is geospatial work, how can you use machine learning in it, and what are the benefits?
How to Choose the Best GPU Optimized VM Sizes for Your Project on Azure
A common problem that data scientists face when training and deploying their machine learning models is choosing of the right type and size of hardware. The goal of this blog post is to provide some guidelines that could help in this non-trivial task.
Daniel Voigt Godoy on Deep Learning and Starting PyTorch
Following his recent Ai+ Training session, we spoke to Daniel Voigt Godoy about PyTorch and why it’s popular for deep learning.
What Data Science Role Should You Hire For?
Your business is ready to dive into your data, but you’re not sure what you need. Learn more about what data science role is best for your organization here!
ODSC West Instructor Spotlight — Daniel Lenton. Join us at West and learn about IVY — The unified machine learning framework, and its mission to unify all machine learning frameworks.
Meta’s Blenderbot 3 AI Chatbot is Already Spewing Controversial Statements
In a short time, Meta’s BlenderBot 3 AI chatbot has already proven to make some controversial statements about politics, race, and Meta itself.
Data Validation for Enterprise ML using Great Expectations and Hopsworks Feature Store
August 31st, 7:00 pm-7:30 pm CEST
Join this webinar to learn more about the core concepts of Great Expectations and how Hopsworks made them available on their platform to be used within any feature pipelines.
8 More Methods for Better Machine Learning at ODSC West
At ODSC West 2022, you’ll be able to learn about trending machine learning topics, such as real-time feedback, poisoned data, and new tools.
Video of the Week: Acquiring and Exploiting the Semantics of Data
In this talk, Craig Knoblock, PhD will present his work on machine learning techniques and graph algorithms for automatically understanding the semantics of data sources. He will also describe how the resulting semantic descriptions can be used to locate relevant data, integrate the data with other sources, and find errors and out-of-date information in existing sources.
Upcoming Webinars:
How far can we take the democratization of Data and AI?
Thurs, Aug 25, 2022, 5.30 PM IST
Tune in for this upcoming Lightning Interview with Akira Shibata, PhD, Chief Operating Officer at Qosmo, Inc.
The Evolution of AI use-cases and data science skill sets over the next decade
Thurs, Sept 1, 2022, 7 PM IST
These are the questions that will be covered in this free webinar: What kind of use-cases and focus areas do you see evolving in AI in the next two decades? Why has AI adoption picked up well in certain areas like Search, Marketing, and Ads, but not in others like Healthcare, Agriculture, and Education? And more!
Microsoft’s Accelerator for MLOps
Thu, Sep 15, 2022, 12:00 PM — 1:00 PM EDT
Microsoft has developed an accelerator to do exactly what the word suggests — accelerate our customer’s journey to production. Simplicity and segregation of duties are key pillars of this accelerator, which means that our intention is that Data Scientists, ML Engineers, and IT teams don’t need significant upskilling before they can do MLOps.
Leveraging External Data to Create a Competitive Go-To-Market Strategy
Tue, Sep 27, 2022, 12:00 PM — 1:00 PM EDT
External data can help organizations find a competitive edge through sales and marketing activities. By adding important context not found in internal data, you deepen your understanding of potential customers, get a more realistic estimate of your total addressable market (TAM), and improve KPIs across a number of sales and marketing activities.
