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O’Reilly AI Conference London 2019

October 9, 2019 by Thomas Henson Leave a Comment

The Big Data Big Data Questions show is heading to London for the O’Reilly AI Conference October 15 – 17 2019. I’m excited to be a part of the O’Reilly AI Conference series. In fact, this will be my third O’Reilly AI conference in the past year. Let’s look back at those events and forward to London.

San Jose & New York

 

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Late night packing my conference gear for my trip to O’Reilly AI Conference this week. Most important items: 1️⃣ Stickers 2️⃣ 🎧 3️⃣ 💻 4️⃣ Bandages? (I’ll explain later) 5️⃣ 📚 (this weeks its my Neural Networking) What’s your list of must have gear for tech conferences? #programming #coding #AI #conference #techconference

A post shared by Thomas Henson (@thomas_henson) on Sep 5, 2018 at 5:09am PDT


First in 2018 I attended the San Jose conference where I spent a good portion of the time in the Dell EMC booth talking with Data Engineers and Data Scientist. One of the major themes I heard from Data professionals was they were attending to learn how to incorporate Tensorflow into their workflows. In my opinion Tensorflow was talked about in every aspect of the conference. We had a blast learning from attendees and discussing how to Scale Deep Learning Workloads. Also this was my first time attending a conference with 14 stitches in my left hand (trouble on the pull up bar)!

Oreilly AI Conference

Next was O’Reilly AI New York. Forever this conference will be known in my head as the Sofia the Robot trip. During this conference I worked with Sofia the Robot not only at the conference but in a Dell EMC event at Time Square Studios (part of the Dell Technologies Magic of AI Series). Before the Magic of AI event, Sofia and I spent the day recording with O’Reilly TV about the current state of AI and what’s driving the widespread adoption. After a day of recording, I had a keynote for day two of the O’Reilly AI Conference where I discussed how AI is impacting future generations already. Then there was a whirlwind of activity as Sofia the Robot took questions at the Dell Technologies booth. The last thing of the day was the Magic of AI event in Time Square Studio where we had 100 people taking part in a questions and answer session with Sofia the Robot.

Keynote O’Reilly AI Conference New York

Coffee with Sofia the Robot

On To London

Next up is O’Reilly AI London. To say I’m excited is an understatement. During this trip I will accomplish many first time moments.

To begin with it’s my first international conference along with my first time in London. So many things to see and so little time to do it. Feel free to give me suggestions about visit locations in the comment section below. 

Second at O’Reilly AI London I will give my first breakout session at an O’Reilly Conference. While I’ve been on O’Reilly TV and given a keynote I’ve yet to have a breakout session.  My session is titled AI Growing Pains: Platform Considerations for Moving from POC to Large-Scale Deployments. The world is changing to innovate and incorporate Artificial Intelligence in many applications and services. However, with all this excitement many Data Engineers are still struggling with how to get projects past the Proof-of-Concept phase (POC) and into Production. Production environments present a list of challenges. The 3 biggest challenges I see when moving from POC to Production are the following:

  • The gravity of data is just as real as the gravity in the physical world. As Deep Learning workloads continue grow so does the amount of data stored to train these models. The data has gravity that will attract services and applications to the data. The trouble here making sure you have correct Data pipelines Strategy on place.
  • Once I had dinner with one of the Co-founders of Hortonworks, during which he said “Everything as Scale is exponentially harder. Have you ever moved around photos on your desktop? For the most part this is an easy task except when you accidentally move a large set of photos. Instantly after moving these large folders you are endlessly waiting for the hour glass to finish. Image doing this with 10 PBs of data. I think you get the picture here.
  • The talent pool today compared to early days of “Big Data” is much larger. However, the demand for skills in Deep Learning, Machine Learning, and Data Engineering is stressing the system. Which still leaves a skills gap for experienced engineers with Deep Learning and Machine Learning skills. The skills gap is one huge factor for why many projects get stuck in the POC phase instead into production.

If you would like to know more about moving projects from POC to Production make sure to checkout my session if you are attending O’Reilly AI Conference in London. AI Growing Pains: Platform Considerations for Moving from POC to Large-Scale Deployments @ 11:55 on October 16, 2019.

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Filed Under: Data Engineers, Data Science Tagged With: AI, Conference, Data Engineers, Data Science, Deep Learning

DataWorks Summit: Future Architecture of Streaming Analytics

June 5, 2017 by Thomas Henson 1 Comment

Ready to learn about the Future Architecture of Streaming Analytics?

Next week I will be heading to the DataWorks Summit in San Jose (formerly Hadoop Summit). The DataWorks summit is one of the top conferences for the Hadoop Ecosystem. Last year was first DataWorks Summit I’ve missed the past 3 years, but this year I’m back. I’m happy to announce this year I have a breakout sessions.

My session will focus on the Future Architectures of Streaming Analytics. I will cover how these architectures will support the future of Streaming Analytics. In the past few years the Hadoop community has focused on the processing of data from streaming data sources with Storm, Spark, Flink, Beam and other projects. Now as we enter in era of massive streams of data it’s time to focus on how we store and scale theses systems. Gartner predicts that by 2020 we will reach 20.4 billion connected devices. Now more than ever we are going to need systems with auto-scaling and unlimited retention. Projects like Pravega emerging to abstract away the storage layer in massive data analytics architectures. Stop by my session to learn about Pravega and architecture recommendations for Streaming Analytics.

Future Architecture of Streaming Analytics

Information on my session

 Future Architecture of Streaming Analytics: Capitalizing on the Analytics of Things (AoT)

The proliferation of connected devices and sensors is leading the Digital Transformation. By 2020 there will be over 20 billion connected devices. Data from these devices need to be ingested at extreme speeds in order to be analyzed before the data decays. The life cycle of the data is critical in revealing what insight can be revealed and how quickly they can be acted upon.

In this session we will look at the past, present and future architecture trends of streaming analytics. Next we will look at how to turn all the data from these devices into actionable insights. We will also dive into recommendations for streaming architecture depending on the data streams and time factor of the data. Finally, we will discuss how to manage all the sensor data, understand the life cycle cost of the data, and how to scale capacity and capability easily with a modern infrastructure strategy.

When: Tuesday June 13th 3:00 PM 

Where: Ballroom C

Filed Under: Streaming Analytics Tagged With: Conference, DataWorks Summit, Streaming Analytics

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