AI automation harnesses advanced AI techniques, including machine learning (ML) algorithms, natural language processing (NLP), and computer vision, to analyze extensive datasets. Through this process, AI applications not only process information but also construct intelligent models capable of making informed decisions based on acquired knowledge. This Refcard aims to equip practitioners with the necessary insights to navigate the complex process of building and implementing AI automations.
There was a time when building machine learning (ML) models and taking them to production was a challenge. There were challenges with sourcing and storing quality data, unstable models, dependency on IT systems, finding the right talent with a mix of Artificial Intelligence Markup Language (AIML) and IT skills, and much more. However, times have changed. Though some of these issues still exist, there has been an increase in the use of ML models amongst enterprises.
This article is an opinionated SRE point of view of an open-source stack to easily request, graph, audit and secure any kind of data access of multiple data sources. This post is the first part of a series of articles dedicated to MLOps topics. So, let’s start with the theory!
What Is Trino?
Trino is an open-source distributed SQL query engine that can be used to run ad hoc and batch queries against multiple types of data sources. Trino is not a database, it is an engine that aims to run fast analytical queries on big data file systems (like Hadoop, AWS S3, Google Cloud Storage, etc), but also on various sources of distributed data (like MySQL, MongoDB, Cassandra, Kafka, Druid, etc). One of the great advantages of Trino is its ability to query different datasets and then join information to facilitate access to data.
There is a crisis in machine learning that is preventing the field from progressing as fast as it could. It stems from a broader predicament surrounding reproducibility that impacts scientific research in general. A Nature survey of 1,500 scientists revealed that 70% of researchers have tried and failed to reproduce another scientist’s experiments, and over 50% have failed to reproduce their own work. Reproducibility, also called replicability, is a core principle of the scientific method and helps ensure the results of a given study aren’t a one-off occurrence but instead represent a replicable observation.
In computer science, reproducibility has a more narrow definition: Any results should be documented by making all data and code available so that the computations can be executed again with the same results. Unfortunately, artificial intelligence (AI) and machine learning (ML) are off to a rocky start when it comes to transparency and reproducibility. For example, take this response published in Nature by 31 scientists that are highly critical of a study from Google Health that documented successful trials of AI that detects signs of breast cancer.
2021 was, quite rightly, touted as “The Year of MLOps”. The MLOps scene exploded with thousands of companies adopting practices and tools aimed at helping them get models into production faster and more efficiently. A multitude of new vendors, consultancies, and open source tooling entered the field making it more important than ever to stay on top of what’s happening.
Throughout January I’ve been asking around to find out the best MLOps events people attended last year. There were loads of great suggestions to go through but a handful kept coming up over and over again. I’ve combined those with my own experiences to create a list of the events and conferences you definitely don’t want to miss:
AI/ML and DevOps are hot buzzwords in the tech industry right now. Moreover, more than 15 million new jobs will be created in AI-related industries. Altogether, many companies are investing heavily in these fields to bring faster, more accurate, and more efficient tech solutions to their existing problems.
Thus, demand for artificial intelligence, machine learning, and DevOps is picking up an increased pace every hour. That's the reason the application of AI/ML in DevOps is the major center of attraction among businesses.
Welcome to the sixth blog post in our “Kubeflow Fundamentals” series specifically designed for folks brand new to the Kubelfow project. The aim of the series is to walk you through a detailed introduction of Kubeflow, a deep-dive into the various components, add-ons, and how they all come together to deliver a complete MLOps platform.
If you missed the previous installments in the “Kubeflow Fundamentals” series, you can find them here:
Companies that have pioneered the application of AI at scale did so using their own in-house ML platforms (uber, LinkedIn, Facebook, Airbnb). Many vendors are now making these capabilities available to purchase as off-the-shelf products. There's also a range of open-source tools addressing MLOps. The rush to the space has created a new problem — too much choice. There are now hundreds of tools and at least 40 platforms available:
(Timeline image from Thoughtworks Guide to Evaluating MLOps Platforms.)
Welcome to the fourth blog post in our “Kubeflow Fundamentals” series specifically designed for folks brand new to the Kubelfow project. The aim of the series is to walk you through a detailed introduction of Kubeflow, a deep-dive into the various components, add-ons, and how they all come together to deliver a complete MLOps platform.
If you missed the previous installments in the “Kubeflow Fundamentals” series, you can find them here:
Welcome to the third blog post in our “Kubeflow Fundamentals” series specifically designed for folks brand new to the Kubelfow project. The aim of the series is to walk you through a detailed introduction of Kubeflow, a deep-dive into the various components, and how they all come together to deliver a complete MLOps platform.
If you missed the previous installments in the “Kubeflow Fundamentals” series, you can find them here:
I recently started a new job at a Machine Learning startup. I’ve given up trying to explain what I do to non-technical friends and family (my mum still just tells people I work with computers). For those of you who at least understand that “AI” is just an overused marketing term for Machine Learning, I can break it down for you using the latest buzzword in the field:
MLOps
The term “MLOps” (a compound of Machine Learning and Operations) refers to the practice of deploying, managing, and monitoring machine learning models in production. It takes the best practices from the field of DevOps and utilizes them for the unique challenges that arise when running machine learning systems in production.
Sometimes Data Scientists use "Confidential" business datasets to perform ML experiments and ultimately train models as per the business problem statement. They have been asked to automate the whole process and create the MLOps pipeline, which runs in a highly secured environment (Managed System Identity) and automates consumption of "Confidential Dataset."
Below is a typical MLOps (Machine Learning Ops) pipeline. Steps in this pipeline can be set up using a YML file and stored in a GIT repository.
Building machine learning (ML) applications is a complex and iterative process. As more companies realize the untapped potential of unstructured data, the demand for AI-powered data processing and analytics will continue to rise. Without effective machine learning operations, or MLOps, most ML application investments will wither on the vine. Research has found that as little as 5% of the AI adoptions companies plan to deploy actually reach deployment. Many organizations incur "model debt," where changes in market conditions, and failure to adapt to them, result in unrealized investments in models that linger unrefreshed (or worse, never get deployed at all).
This article explains MLOps, a systemic approach to AI model life cycle management, and how the open-source vector data management platform Milvus can be used to operationalize AI at scale.
Machine and Operations Learning (MLOps), similar to DevOps, is a combination of practices and tools that use Data Science and Operations teams to improve model deployment through Machine Learning (ML), AI, monitoring, validation, collaboration, and communication.
This is because according to Gartner, many companies develop Machine Learning models, but only 47% of them are published in production. And still, 88% of AI initiatives have difficulty passing the test stage.
