TL;DR Feedback integration is crucial for ML models to meet user needs. A robust ML infrastructure gives teams a competitive advantage. Technical projects must be aligned with business objectives. Human involvement in MLOps and AI is as crucial as the technology itself.  I started my ML journey as an analyst back in 2016. Since then, I’ve worked as a data scientist for a multinational company and an MLOps engineer for an early-stage startup before moving to Mailchimp in May 2021. I joined just before its $12 billion acquisition by Intuit. It was an exciting time to be th ..read more

Training and evaluating models is just the first step toward machine-learning success. To generate value from your model, it should make many predictions, and these predictions should improve a product or lead to better decisions. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. This can seem daunting. Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce ..read more

This article was originally an episode of the ML Platform Podcast, a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. In this episode, Mikiko Bazeley shares her learnings from building the ML Platform at Mailchimp. You can watch it on YouTube: Or Listen to it as a podcast on:  Spotify  Apple Podcasts  But if you prefer a written version, here you have it!  In this episode, you will learn about:&n ..read more

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. But some of these queries are still recurrent an ..read more

This article was originally an episode of the ML Platform Podcast, a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. In this episode, Stefan Krawczyk shares his learnings from building the ML Platform at Stitch Fix. You can watch it on YouTube: Or Listen to it as a podcast on:  Spotify  Apple Podcasts  But if you prefer a written version, here you have it!  In this episode, you will learn about ..read more

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use.  Experiment tracking is one such capability. And since you are reading this article, the data scientists you support have probably reached out for help. The experiments they run are scaling and becoming increasingly complex; keeping track of their experiments and ensuring they are reproducible have gotten harder.  Building a tool for managing experiments can help your data scientists ..read more

This article was originally an episode of the MLOps Live, an interactive Q&A session where ML practitioners answer questions from other ML practitioners.  Every episode is focused on one specific ML topic, and during this one, we talked to Jason Falks about deploying conversational AI products to production. You can watch it on YouTube: Or Listen to it as a podcast on:  Spotify  Apple Podcasts  But if you prefer a written version, here you have it!  In this episode, you will learn about:  1 How to develop products with conversational AI  2 The requir ..read more

This article was originally an episode of the MLOps Live, an interactive Q&A session where ML practitioners answer questions from other ML practitioners.  Every episode is focused on one specific ML topic, and during this one, we talked to David Hershey about GPT-3 and the feature of MLOps. You can watch it on YouTube: Or Listen to it as a podcast on:  Spotify  Apple Podcasts  But if you prefer a written version, here you have it!  In this episode, you will learn about:  1 What is GPT-3 all about?  2 What is GPT-3’s impact on the MLOps field and how ..read more

Getting machine learning to solve some of the hardest problems in an organization is great. And eCommerce companies have a ton of use cases where ML can help. The problem is, with more ML models and systems in production, you need to set up more infrastructure to reliably manage everything. And because of that, many companies decide to centralize this effort in an internal ML platform.  But how to build it? In this article, I will share my learnings of how successful ML platforms work in an eCommerce and what are the best practices a Team needs to follow during the course of building it ..read more

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. It is crucial to save, store, and package these models for their future use and deployment to production. These practices are needed for a number of reasons: Backup: A trained model can be saved as a backup in case the original data is damaged or destroyed.  Reusability & reproducibility: Building ML models is time-consuming by nature. To save cost and time, it becomes essential that your model gets you the same results every time you run it. Saving ..read more