Did you know that most AI projects never get fully deployed? In fact, a recent survey by NewVantage Partners revealed that only 15% of leading enterprises have gotten any AI into production at all. Unfortunately, many models get built and trained, but never make it to business scenarios where they can provide insights and value. This gap – deemed the production gap – leaves models unable to be used, wastes resources and stops AI ROI in its tracks. But it’s not the technology that is holding things back. In most cases, the barriers to businesses and organizations becoming data-driven can be reduced to three things: people, process and culture. So, the question is, how can we overcome these challenges and start getting real value from AI? To overcome this production gap and finally get ROI from their AI, enterprises must consider formalizing an MLOps strategy.
MLOps, or machine learning operations, refers to the culmination of people, processes, practices and underpinning technologies that automate the deployment, monitoring and management of machine learning models into production in a scalable, fully governed way. Read More
Tag Archives: DevOps
Machine Learning Metadata (MLMD) : A Library To Track Full Lineage Of Machine Learning Workflow
Version control is used to keep track of modifications made in a software code. Similarly, when building machine learning (ML) systems, it is essential to track things, such as the datasets used to train the model, the hyperparameters and pipeline used, the version of tensorflow used to create the model, and many more.
ML artifacts’ history and lineage are very complicated than a simple, linear log. Git can be used to track the code to one extent, but we need something to track your models, datasets, and more. The complexity of ML code and artifacts like models, datasets, and much more requires a similar approach.
Therefore, the researchers have introduced Machine Learning Metadata (MLMD), a standalone library to track one’s entire ML workflow’s full lineage from data ingestion, data preprocessing, validation, training, evaluation, deployment, etc. MLMD also comes integrated with TensorFlow Extended. Read More
Conway’s Law: Critical For Efficient Team Design In Tech
Conway’s law is critical to understanding the forces at play when organizing teams amidst the long-lasting, unattended impact they can have on our software systems, as the latter have become larger and more interconnected than ever before. But you might wonder if a law from 1968 about software architecture has stood the test of time.
We’ve come a long way after all: microservices, the cloud, containers, serverless. Such novelties can help teams improve locally, but the larger the organization, the harder it becomes to reap the full benefits. The way teams are set up and interact is often based on past projects and/or legacy technologies (reflecting the latest org-chart design, which might be years old, if not decades).
This quote from Ruth Malan provides what could be seen as the modern version of Conway’s law: “If the architecture of the system and the architecture of the organization are at odds, the architecture of the organization wins.” Read More
LinkedIn open-sources Dagli, a machine learning library for Java
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
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