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.
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.