TWIL: August 29, 2022
This week most of my learning was through podcasts and videos. Find below a set of podcast around data science, namely one on data2vec and another on why AI makes sense in the blockchain, a few videos on Data Lake and Databricks which I found very interesting, and an article on MLOps maturity stages. Finally the global availability announcement for Microsoft Entra Verified ID. Really cool!
Episode 1808: Twenty Years of .NET Rocks!
Twenty years ago, before the word podcast existed, there was .NET Rocks! While at CodePaLOUsa in Louisville, Carl and Richard celebrated the publication of the first episode of .NET Rocks twenty years ago in August 2002. Doc Norton joined the conversation to talk about how agile has evolved and the challenges of making good software today. And a big thanks to all the listeners of the show – we couldn’t have done it without you!
Episode 854: Learning via Personal Challenges with Lisi Hocke
Lisi Hocke commits to a personal challenge every year, publicly! This is a great way to encourage accountability and learn and improve public. She commits to one challenge per year and a continuing learning journey. Scott talks to Lisi about getting out of our personal, professional, and technical comfort zones and getting better through accountability and learning partners!
Episode 855: Growing your career via technical content creation with Mohammed Osman
Mohammed Osman strongly believes that blogging greatly improved his my career and encourages everyone to give it a try. Things like content research, learning SEO, hosting your own sites and blogs and lead to job opportunities, speaking opportunities and more! He talks to Scott about how he’s used this blog to teach (and learn) Azure and help get folks all over the world Azure Cloud certified!
Episode 121: data2vec and the future of multimodal learning
If the name data2vec sounds familiar, that’s probably because it made quite a splash on social and even traditional media when it came out, about two months ago. It’s an important entry in what is now a growing list of strategies that are focused on creating individual machine learning architectures that handle many different data types, like text, image and speech.
Episode 122: Trends in data science
As you might know if you follow the podcast, we usually talk about the world of cutting-edge AI capabilities, and some of the emerging safety risks and other challenges that the future of AI might bring. But I thought that for today’s episode, it would be fun to change things up a bit and talk about the applied side of data science, and how the field has evolved over the last year or two.
Episode 123: AI on the blockchain (it actually might just make sense)
Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “AI on the blockchain” sounds like a winning entry in some kind of startup buzzword bingo. But what I discovered talking to Jacob and Ala was that they actually have good reasons to combine those two ingredients together.
Episode 474: The last technical interview you’ll ever take
Since the day a hiring manager first wheeled a whiteboard into a conference room, software engineers have dreaded the technical interview, which can be an all-day process (or multi-day homework assignment). If you’re interviewing for multiple roles, you can expect to write out a bubble sort in pseudocode for each one. These technical interviews do no favors for hiring companies, either, because the investment needed from both parties limits the number of candidates a company can consider. In this age of data-driven decisions, perhaps there’s a way that AI and ML can help candidates and companies find each other.
Episode 476: Why AI is having an on-prem moment
The home team discusses how Instagram’s evolving platform has alienated some creators, why AI and machine learning are moving on-premises, and why Amazon’s acquisition of the company behind the Roomba is striking from a privacy perspective.
Episode 477: The luckiest guy in AI
Serial entrepreneur Varun Ganapathi joins the home team for a conversation about the intersection of physics, machine learning, and AI. He offers some recommendations for developers looking to get started in the ML/AI space and shares his own path from academia to entrepreneurship. Plus: How an early lack of Nintendo motivated Varun to learn to program.
Video: Getting Started with Databricks SQL – Simon Whiteley
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool? This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
Video: The Azure Spark Showdown – Databricks VS Synapse Analytics – Simon Whiteley
Azure now has two slick, platform-as-a-service spark offerings, but which one should you choose? A separate specialist tools or a one-size-fits-all solution? Join Simon Whiteley as he compares and contrasts the spark offerings.
Video: Databricks, Delta Lake and You – Simon Whiteley
Databricks, Lakes & Parquet are a match made in heaven, but explode with extra power when using Delta Lake. This session will dive into the details of how Databricks Delta works and how to make the most of it.
The Seven Stages of MLOps Maturity
In this post, I take a moment to look at what I’ve learned from working with large companies and share common themes that emerge from their MLOps journeys. My goal in doing this is to provide a framework by which executives and leaders can measure the progress of their journey towards AI excellence.
Machine Learning with Cosmos DB and Synapse Link
Synapse Link bridges an important scenario when dealing with Cosmos DB: the efficient processing of analytical workloads, without risking the integrity of transactional applications supplying the data. To put this feature to the test, we used an accelerometer dataset for activity recognition. Our goal is to perform analytical workloads – in this case, training an ML classification model, over data stored in Cosmos DB.
Azure Log Analytics
Understanding Azure Log Analytics query auditing
In this post, I will talk about Azure Log Analytics and query auditing capabilities. I may cover the use cases in an article later, expanding on why this functionality matters. There are many common use cases in legislation, regulatory compliance, and monitoring, but that’s for another time.
Microsoft Entra Verified ID now generally available
Customers rely on Azure AD to secure access to corporate resources. However, enabling use of credentials for utility beyond the company (e.g. prove employment for bank loan) is complex and comes with compliance risk. In contrast, identity documents from our everyday lives, like a driver’s license or passport, are well suited for utility beyond travel (e.g. age or residency). We believe an open standards-based Decentralized Identity system can unlock a new set of experiences that give users and organizations greater control over their data—and deliver a higher degree of trust and security for apps, devices, and service providers.
Have a great week!