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Algorithmia at Big Data London

People walking on Millennium Bridge in London. Bridge built by Architect Norman Foster

Recently, Algorithmia ventured from Seattle to London to discover what was happening at the Big Data London (BDL) conference in Kensington. We had great conversations with data engineers, data analysts, and business leaders about how Algorithmia makes it easy to deploy machine learning models into production. Our platform handles the MLOps portion of the data science pipeline, so data scientists can focus instead on solving business problems with data.

Highlights from the booth

At BDL, we got the opportunity to talk with many companies about where they are in their ML journeys. While some are just starting to evaluate use cases and consider infrastructure requirements, it’s very encouraging to hear about how they are planning to put their models into production. This is an important step and it is often overlooked. You don’t want to choose a training platform, for instance, that locks you into a specific ecosystem. It’s better to use the best possible platforms and services for each stage of your data science pipeline rather than get locked into one that tries to do everything, without excelling at any portion of it.

We also talked to many data scientists who are at the stage where they have several models sitting on laptops waiting to be utilized in production but don’t know where to go from there. This is a very common scenario, and Algorithmia has white glove customer support to help you get models off laptops and into operation.

Of course, there are also engineers and business owners who are experiencing the same friction points that Algorithmia helps address in the MLOps workflow. This includes: versioning, model updating, centralized repositories, and of course dependency management and scaling.

If any of these stages of the ML roadmap resonate with you, come talk to us at AWS re:Invent where we can go into more detail about getting your models deployed to a scalable, reliable infrastructure today.

Special topics in big data

There were several core themes at the conference, and ones that turned out to be very popular were: Data Governance, Self-Service Analytics, DataOps, Customer Experience Analytics, and of course Machine Learning and AI. 

Some crowd favorites included A GDPR Retrospective: Implementation by a Large-Scale Data Organization in Reality which covered GDPR compliance from a technical standpoint, rather than a business point of view like some of the other talks within that track. Another popular talk within Data Governance, focused on how data management is a customer service story, not just a technical one in Data Governance as a Customer Service. Here at Algorithmia we feel the same way about model management!

To be expected, there were some standout talks in the Keynote Theater. One of our favorites was from EY’s Chief Scientist Harvey Lewis, a leader in applied ML in business, who talked about the need for humans in the loop in the AI pipeline. Lewis covered use cases that showed how important it is to combine humans with machine learning algorithms to ensure that inferences are accurate when it comes to safety, compliance, and risk in the realm of accounting, auditing, and consultancy firms

Another big hit in the Keynote Theater was Making Everyone A Data Person At Lloyd’s. This talk focused on empowering all users across various teams within an organization to be more data-informed. The speakers talked about their initiative called the Data Lab within Lloyd’s Data, which focuses on making everyone within their company data-literate through mentorship and training.

Our tracks of interest

One of the tracks with the longest queues was the Self-Service Analytics tracks. We know because the Algorithmia booth was right near it so we got a chance to chat with many folks waiting in line. A crowd favorite came from our friends at Tableau, who served up a great talk on how to explore data and gain actionable insights with natural language processing

And of course, our favorite track: AILab, which hosted talks on everything from ethics in AI, to extracting actionable insights from machine learning. It also covered infrastructure and scaling modern machine learning systems. 

We’ve thought a lot about these subjects to, so be sure to read up on racial bias in AI,, gaining insights on customer churn data and scaling your machine learning models.

What was missing from the talks, was substance surrounding the difficulty in the deployment cycle. While scaling is important, making sure you can automate your ML deployment lifecycle is crucial. We’ve covered everything from shortening your deployment time to what makes cloud infrastructure crucial to machine learning

That wraps up our take on our first experience at the Big Data London data conference. And if you’re going to re:Invent next month, check out Diego Oppenheimer’s talk on continuous deployment, and don’t forget to set up a meeting to see how Algorithmia can enable your model deployment, serving, and management at scale.  

Continuous deployment for machine learning—a re:Invent talk by Diego Oppenheimer

Algorithmia coming to AWS re:Invent

Meet with us!

AWS re:Invent is next month, and we are pleased to announce that Algorithmia CEO, Diego Oppenheimer, will be speaking on the new software development lifecycle (SDLC) for machine learning. Often we get variations on this question: how can we adapt our infrastructure, operations, staffing, and training to meet the challenges of ML without throwing away everything that already works? Diego is prepared with answers. His talk will cover how machine learning (ML) will fundamentally change the way we build and maintain applications.

Currently, many data science and ML deployment teams are struggling to fit an ML workflow into tools that don’t make sense for the job. This session will help clarify the differences between traditional and ML-driven SDLCs, cover common challenges that need to be overcome to derive value from ML, and provide answers to questions about current technological trends in ML software. Finally, Diego will outline how to build a process and tech stack to bring efficiency to your company’s ML development. 

Diego’s talk will be on 4 December at 1:40pm in the Nuvola Theater in the Aria. 

Coming soon: the 2020 State of Enterprise Machine Learning Report

Additionally, Diego will share insights from our upcoming 2020 State of Enterprise Machine Learning survey report, which will be an open-source guide for how the ML landscape is evolving. The report will focus on these findings:

  1. Shifts in the number of data scientists employed at companies in all industries and what that portends for the future of ML
  2. Use case complexity and customer-centric applications in smaller organizations
  3. ML operationalization (having a deployed ML lifecycle) capabilities (and struggles) across all industries
  4. Trends in ML challenges: scale, version-control, model reproducibility, and aligning a company for ML goals
  5. Time to model deployment and wasted time
  6. What determines ML success at the producer level (data scientist and engineer) and at the director and VP level

Pick up a copy of the report at Algorithmia’s booth.

Diego and his team will be available throughout the week to answer questions about infrastructure specifics, ML solutions, and new use cases at Booth 311

Meet with our team

If you or your team will be in Las Vegas for re:Invent this year, we want to meet with you. Our sales engineers would love to cater a demo of Algorithmia’s product for your specific needs and demonstrate our latest features. Book some time with us!

Read the full press report here.

Chatbot Workshop at Seattle’s Building Intelligent Applications Meetup

Recently Jon Peck, who is a Software Engineer and Developer Evangelist at Algorithmia, wrote a fun post on how to get an emotionally aware chatbot up and running in about 15 minutes.

In this hands-on micro workshop, Jon will show you how to create a chatbot using Dexter, a company that makes building chatbots easy and accessible. Then he’ll show you how to make the chatbot emotionally aware using Algorithmia. Our open marketplace that hosts over 4,000 algorithms and microservices that are all available via a scalable API endpoint.

Jon will also go through some use cases covering why you would need a chatbot, especially one enabled with machine learning and provide some examples of other machine learning algorithms that work well in chatbots, but aren’t covered in the demo.

Please join us for a fun evening of food and drinks provided by Algorithmia and learn how to build an emotionally intelligent chatbot!

For more information or to RSVP check out the Seattle Building Intelligent Applications Meetup.

Meet the Algorithmia Team at the Strata Data Conference in NYC, September 26-28, 2017

Location: Javits Center in New York City
Date: September 26-28, 2017

The theme of Strata is “turning data into advantage”—here at Algorithmia, we help your team get the models from your data science efforts into production. Our team will be available to talk through the challenges you’re facing in productionizing your models or finding the right models from our marketplace to meet your project’s needs. Whatever your questions are, we’re here to help.

Quick Facts:

  • Meet us at the Algorithmia Booth #P33
  • We’ll be giving away Algorithmia swag—so stop by and say hi.
  • Get 20% off your conference pass by entering the Algorithmia20 discount code at the registration