LinkedIn today open-sourced Dagli, a machine learning library for Java (and other JVM languages) that ostensibly makes it easier to write bug-resistant, readable, modifiable, maintainable, and deployable model pipelines without incurring technical debt. Read More
Tag Archives: DevOps
Introducing software fuzzing – part of AI and ML in DevOps
The lines between the real world and the digital world have been consistently blurring for years, and with that, software has bloomed. Physicists are hypothesizing that information can be considered a form of matter, the fifth form of matter in fact.
More and more, software is linked to the quality of our lives. That means the quality of our software will fundamentally direct the quality of our experience, so there’s never been a more important time to seek out ways to improve our DevOps. One of the tools that helps us explore that is ML. Read More
Creating End-to-End MLOps pipelines using Azure ML and Azure Pipelines
In this 7-part series of posts we’ll be creating a minimal, repeatable MLOps Pipeline using Azure ML and Azure Pipelines.
The git repository that accompanies these posts can be found here. Read More
Continuous Delivery for Machine Learning
Automating the end-to-end lifecycle of Machine Learning applications
Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data. Their behaviour is often complex and hard to predict, and they are harder to test, harder to explain, and harder to improve. Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications. Read More
Real Time Machine Learning at Scale using SpaCy, Kafka & Seldon Core
Andrew Ng: Bridging AI’s Proof-of-Concept to Production Gap
AI In Code Series: Finastra – Code assistance for the developer toolkit
We users use Artificial Intelligence (AI) almost every day, often without even realising it i.e. a large amount of the apps and online services we all connect with have a degree of Machine Learning (ML) and AI in them in order to provide predictive intelligence, autonomous internal controls and smart data analytics designed to make the end user User Interface (UI) experience a more fluid and intuitive experience.
That’s great. We’re glad the users are happy and getting some AI-goodness. But what about the developers?
But what has AI ever done for the programming toolsets and coding environments that developers use every day? How can we expect developers to develop AI-enriched applications if they don’t have the AI advantage at hand at the command line, inside their Integrated Development Environments (IDEs) and across the Software Development Kits (SDKs) that they use on a daily basis? Read More: Part 1 … Part 2
Model Lifecycle: From ideas to value
Value scoping, discovery, delivery, and stewardship
In Part 1 of this series we examined the key differences between software and models; in Part 2 we explored the twelve traps of conflating models with software; and in Part 3 we looked at the evolution of models. In this article, we go through the model lifecycle, from the initial conception of the idea to build models to finally delivering the value from these models. Read More
10 MLops platforms to manage the machine learning lifecycle
Machine learning lifecycle management systems rank and track your experiments over time, and sometimes integrate with deployment and monitoring
For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used lifecycle management for their machine learning models. That’s a problem that’s much easier to fix now than it was a few years ago, thanks to the advent of “MLops” environments and frameworks that support machine learning lifecycle management. Read More
Consequences of mistaking models for software
Twelve traps to avoid when building and deploying models
In Part 1 of this series on data scientists are from Mars and software engineers are from Venus we examined the five key dimensions of difference between software and models. The natural follow on question to ask is — So What? Does it really matter if models are conflated with software and data scientists are treated as software engineers? After all for a large cross-section of the population, and more importantly the business world, the similarities between them are far more visible than their differences. In fact, Andrej Karpathy refers to this new way of solving problems using models as Software 2.0. If they are really the next iteration of software are these differences really consequential. Read More
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