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Tag: MLOps

How MLOps is Redefining the AI Industry

Building a predictive model to be able to forecast the future from historical data is standard in today’s business environment. But deploying, scaling, and managing predictive models across an enterprise is far from a simple undertaking.Enterprises hire data scientists to develop end-to-end machine learning (ML) solutions, requiring those data scientists to bridge the gap between scientific methods and engineering processes.There are some challenges with this approach. Most data scientists are not trained in distributed computing, big data or software…

A ‘Glut’ of Innovation Spotted in Data Science and ML Platforms

(Blue Planet Studio/Shutterstock)

These are heady days in data science and machine learning (DSML) according to Gartner, which identified a “glut” of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or model governance to startups focusing on MLops or explainable AI, a plethora of vendors are simultaneously moving in all directions with their products as they seek to differentiate themselves amid a very diverse audience.
“The DSML market is simultaneously more vibrant and messier than ever,” a gaggle of Gartner analysts led by…

10 AI tech trends data scientists should know

AI adoption is accelerating across industries, driven by a combination of concrete results, high expectations and a lot of money. Among the many new AI concepts and techniques launching almost daily, 10 AI tech trends in particular grab data scientists’ attention.

1. MLOps
Machine learning operations (MLOps) isn’t a new concept, but it’s a relatively new “Ops” practice which operationalizes machine learning models. MLOps seeks to understand what works and doesn’t work in a model in order to create more reliable models in the future.
It’s the last mile of machine learning model…

Accelerating AI with MLOps

Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential.

MLOps on AWS using MLflow

In our earlier articles, we covered installation and implementation of MLOps using MLflow.
For any business, seamless deployment of ML models into production is the key to success of its live analytics use cases. In this article, we will learn about deploying ML models on AWS (Amazon Web Services) using MLflow and also look at different ways to productionize them. Subsequently, we will explore the same process on the two other popular platforms: Azure and GCP. Let’s begin.

Deploying an ML model on AWS: Pre-requisites
AWS command line interface (CLI) installed and credentials…

MLOps vs. DevOps: Why data makes it different

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting…

Improving Your Odds of ML Success with MLOps

In this special guest feature, Harish Doddi, CEO, Datatron, discusses what CEOs need to understand about using MLOps. He also shares insights on how to use MLOps to gain competitive advantage and provide tips on how to implement it. Over the past decade, Harish has focused on AI and data science. Before Datatron, he worked on the surge pricing model for Lyft, the backend for Snapchat Stories, the photo storage platform for Twitter, and designing and developing human workflow components for Oracle. Harish completed his master’s degree in computer science at Stanford, where he focused…

Five Ways AI Can Help States Solve Their Hardest Problems (Part 5): Putting AI into Action with MLOps

Many organizations, including state and local governments, are dipping their toes into machine learning (ML) and artificial intelligence (AI). As we’ve discussed in this blog series, some are already reaping the rewards of AI through increased productivity, cost savings, etc. However, for most embarking on this transformational journey, the results are yet to be seen and for those who are already underway, scaling their results appears as completely uncharted waters. According to a recent study by NewVantage Partners, only 15 percent of organizations surveyed have deployed AI…

7 of The Coolest Machine Learning Topics of 2021 at ODSC West

At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning topics, deep learning, NLP, MLOps, and so on. You can register now for 20% off all ticket types, or register for a free AI Expo Pass to see what some big names in AI are doing now.

ML and MLOps at a Reasonable Scale

MLOps without too much Ops — Episode 2With Andrea Polonioli and Jacopo TagliabuePhoto by Stephanie LeBlanc via UnsplashWhile the number of Machine Learning (ML) applications used in production is growing, not a day goes by when we don’t read something about how most enterprises still struggle to see positive ROI (see here and here).One thing you might notice: nobody ever talks about how Big Tech is struggling to reap the benefits of ML in production. That’s because they are not….

MLflow Installation

In this article, we cover How to install MLflow. Before we dive into the process, let’s begin with introducing MLOps
By definition, MLOps is a cross-functional, collaborative, and continuous process that focuses on operationalizing data science use cases by managing statistical, machine learning models as reusable, highly available software artifacts via repeatable deployment process.
MLOps covers aspects such as model inference, scalability, maintenance, auditing, monitoring, and…

Artificial Intelligence (AI) Newsletter by Towards AI #15

Source: UnsplashNEWS, NEWSLETTERThe Artificial Intelligence (AI) Newsletter by Towards AIIf you have trouble reading this email, see it on a web browser.Hey everyone. I hope you are well. In this issue, we dive into the maturation of the artificial intelligence (AI) ecosystem and its landscape for 2021, updated and curated resources for MLOps, how deep learning is accurately predicting traffic crashes before they occur, a complete data science cheat sheet with resources for stats,…

AI Model Deployment Made Easy

Organizations using Artificial Intelligence (AI) and Machine Learning (ML) solutions face a challenging problem: deploying these capabilities into production systems. The last component of ModelOps and MLOps pipelines is the production deployment stage. This stage occurs after a model is trained and reaches a suitable level of performance and is ready to make predictions against live data. See the previous posts in our ModelOps pipeline series to learn more.
So why is this a challenge? On…

MLOps and ModelOps: What’s the Difference and Why it Matters – KDnuggets

By Stu Bailey, Co-founder and Chief AI Architect at ModelOp.

Did you know approximately half of the AI models that are developed never actually make it into production? If you want to understand why and prevent the waste of data scientist time and other resources from happening at your organization, then it is important to understand the difference between MLOps and ModelOps. They aren’t the same, but the terms are often used interchangeably. That lack of understanding about the specific roles and value of MLOps and ModelOps undermines the value of enterprise AI programs. It is important to know the difference between MLOps and ModelOps because neither is a substitute for the other.

MLops Vs Modelops, bottom part

This blog addresses the following questions:

  • What is the difference between MLOps and ModelOps
  • What is each used for?

Trust in Your Production Models with Bias Monitoring

Evaluating bias is an important part of developing a model. Deploying a model that’s biased can lead to unfair outcomes for individuals and repercussions for organizations. DataRobot offers robust tools to test if your models are behaving in a biased manner and diagnose the root cause of biased behavior. However, this is only part of the story. Just because your model was bias-free at the time of training doesn’t mean biased behavior won’t emerge over time. To that end, we’ve extended our Bias and Fairness capabilities to include bias monitoring in our MLOps platform. In this post, we’ll walk you through an example of how to use DataRobot to monitor a deployed model for biased behavior.

The Data

We’ll be using a dataset that contains job applications and training a model to predict if the candidates were hired or not.… Read more...

MLOps without much Ops

If you do not work for Big Tech— the Googles, Facebooks, Amazons of this world — , chances are that you work for a “reasonable scale” company.

Reasonable scale companies aren’t like Google. They can’t hire all the people they dream of and they don’t serve billions of users per day from a cloud infrastructure they own. Reasonable scale companies process millions of data points, not billions; they can hire dozens of data scientists, not hundreds, and they have to optimize for their computing costs.

At the same time, reasonable scale companies have plenty of interesting business problems that could be addressed by using Machine Learning. Actually, it would make total sense to address them with Machine Learning and maybe they are already trying.


Data Engineering Technologies 2021


By Tech Ninja, OpenSource, Analytics & Cloud enthusiast.

A partial list of top engineering technologies, image created by KDnuggets.

Complete curated list of emerging technologies in Data Engineering

  • Abacus AI, enterprise AI with AutoML, similar space to DataRobot.
  • Algorithmia, enterprise MLOps.
  • Amundsen, an open-sourced data discovery and metadata engine.
  • Anodot, monitors all your data in real-time for lightning-fast detection of incidents.
  • Apache Arrow, essential because of non-JVM, in-memory, columnar format and vectorized.
  • Apache Calcite, framework for building SQL databases and data management systems without owning data. Hive, Flink, and others use Calcite.
  • Apache HOP, facilitates all aspects of data and metadata orchestration.

Adventures in MLOps with Github Actions, Iterative.ai, Label Studio and NBDEV

By Aaron Soellinger & Will Kunz

When designing the MLOps stack for our project, we needed a solution that allowed for a high degree of customization and flexibility to evolve as our experimentation dictated. We considered large platforms that encompassed many functions, but found it limiting in some key areas. Ultimately we decided on an approach where separate specialized tools were implemented for labeling, data versioning, and continuous integration. This article documents our experience building this custom MLOps approach.

Photo by Finding Dan | Dan Grinwis on Unsplash



(Taken from https://github.com/fastai/nbdev)

The classic problem using Jupyter for development was moving from prototype to production required copy/pasting code from a notebook to a python module.


MLOps: Building a Feature Store? Here are the top things to keep in mind

A Feature Store should have 3 major building blocks and offer 10 major functionalities. These enable the Feature Store to improve the way raw data is processed and cataloged leading to a faster turnaround time for data scientists.

Fig 1: Feature Store Data Flow (Image by Author)

IIT-Roorkee to offer online PG certificate programmes in data science, AI, and MLOps

The Indian Institute of Technology, Roorkee (IIT-R) will offer three online postgraduate certificate programmes in association with CloudxLab. These courses will help students get equipped with skills in the fields of data science, machine learning, deep learning, and MLOps.

These three PG certificate courses aim to equip learners to master deep technologies through comprehensive hands-on oriented learning. Anybody who has completed their undergraduate degree is eligible to enrol in these courses. As part of the course, learners will also be able to experience a one-week-long on-campus immersion programme at the IIT Roorkee campus.

Learners enrolling in the course will be getting access to a fully equipped online cloud lab so that they can perform hands-on exercises included in the course very efficiently.