ML in Production - Page 3 of 6 - Best practices for building real world machine learning systems

Machine Learning is Forcing Software Development to Evolve

In late 2017 Andrej Karpathy (Director of AI at Tesla, former Research Scientist at OpenAI) published a blog post called Software 2.0 in which he explained that the growth in use of machine learning and neural networks represent a shift… Read More

How I Hire Data Scientists

One of the most rewarding aspects of my role as a Director of Data Science is leading the hiring process for new data scientists. Collaborating with other leaders to craft the interview process is extremely challenging and fascinating at the… Read More

Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning

Companies are investing significant resources into AI development to generate business value. For the most part, this development has focused on using machine learning to optimize operations or to build and improve consumer facing applications. Recently, some organizations have begun… Read More

5 Challenges to Running Machine Learning Systems in Production

As an applied machine learning practitioner, I’m acutely aware that we’re still in the steam-powered days of machine learning. With a few notable exceptions (I’m looking at you FAANMG), most companies have only begun to utilize ML techniques in the… Read More

Interview with Yochay Ettun, Co-founder & CEO of cnvrg.io

Below is an interview I conducted with Yochay Ettun, Co-founder & CEO of cnvrg.io, an end-to-end machine learning platform to build and deploy AI models at scale. Yochay Ettun and his fellow Co-founder Leah Kolben launched cnvrg.io in 2017. I… Read More

Model Registries for ML Deployment (Deployment Series: Guide 06)

This is post 6 in my Ultimate Guide to Deploying Machine Learning Models. You can find the other posts in the series here. In the last several posts of our machine learning deployment series we discussed how to deploy models… Read More

Online Inference for ML Deployment (Deployment Series: Guide 05)

This is post 5 in my Ultimate Guide to Deploying Machine Learning Models. You can find the other posts in the series here. In our previous post on machine learning deployment we discussed the challenges associated with serving ML models… Read More

The Challenges of Online Inference (Deployment Series: Guide 04)

This is post 4 in my Ultimate Guide to Deploying Machine Learning Models. You can find the other posts in the series here. In our previous post on machine learning deployment we discussed deploying models for batch inference. We described… Read More

Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)

This is post 3 in my Ultimate Guide to Deploying Machine Learning Models. You can find the other posts in the series here. In our previous post on machine learning deployment we designed a software interface to simplify deploying models… Read More

Top 30 ML in Production Resources (Based On Engagement Data From 17,148 Emails)

Developing the MLinProduction.com newsletter has been a great way to learn about the topics and content most relevant to the ML engineers and data scientists in my community. To prepare this post, which can also be downloaded as an eBook,… Read More