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7 DevOps skills for Machine Learning Operations

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MLOps has been a hot topic in 2021, with many people talking about and companies aiming at implementing it. The reason is clear: MLOps enables the application of agile principles to machine learning projects, which means shorter release cycles and higher quality standards. From the technology standpoint, I would say the main pieces for successful MLOps implementation are available: the ability to train and serve ML models using containers, plenty of data pipeline orchestration tools, automated testing frameworks, and mature DevOps practices. Having the technology pieces in hand does not mean success, though. Building MLOps teams is challenging due to the roles typically involved: Data Scientists, Machine Learning Engineers, Data Engineers, DevOps Engineers, and management staff. Experience shows that people in these roles do not necessarily speak the same language and, from my point of view, someone should take the responsibility of connecting the dots.


MLOps for Conversational AI with Rasa, DVC, and CML (Part I)

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This is the first part of a series of blog posts that describe how to use Data Version Control (DVC), and Continuous Machine Learning (CML) when developing conversational AI assistants using the Rasa framework. This post is mostly an introduction to these three components, in the next post I'll delve into the code, and how to get everything connected for Rasa MLOps bliss. If you've not heard of Data Version Control (DVC), you've been missing out. DVC is an exciting tool from iterative.ai DVC extends git's functionality to cover your data wherever you want to store it, whether that is locally, on a cloud platform like AWS S3, or a Hadoop File System. Like git, DVC is language agnostic.


Scaling AI like a tech native: The CEO's role

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What if a company built each component of its product from scratch with every order, without any standardized or consistent parts, processes, and quality-assurance protocols? Chances are that any CEO would view such an approach as a major red flag preventing economies of scale and introducing unacceptable levels of risk--and would seek to address it immediately. Yet every day this is how many organizations approach the development and management of artificial intelligence (AI) and analytics in general, putting themselves at a tremendous competitive disadvantage. Significant risk and inefficiencies are introduced as teams scattered across an enterprise regularly start efforts from the ground up, working manually without enterprise mechanisms for effectively and consistently deploying and monitoring the performance of live AI models. Ultimately, for AI to make a sizable contribution to a company's bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations daily.


7 DevOps skills for Machine Learning Operations

#artificialintelligence

MLOps has been a hot topic in 2021, with many people talking about and companies aiming at implementing it. The reason is clear: MLOps enables the application of agile principles to machine learning projects, which means shorter release cycles and higher quality standards. From the technology standpoint, I would say the main pieces for successful MLOps implementation are available: the ability to train and serve ML models using containers, plenty of data pipeline orchestration tools, automated testing frameworks, and mature DevOps practices. Having the technology pieces in hand does not mean success, though. Building MLOps teams is challenging due to the roles typically involved: Data Scientists, Machine Learning Engineers, Data Engineers, DevOps Engineers, and management staff. Experience shows that people in these roles do not necessarily speak the same language and, from my point of view, someone should take the responsibility of connecting the dots.


Here's where MLOps is accelerating enterprise AI adoption – TechCrunch

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In the early 2000s, most business-critical software was hosted on privately run data centers. But with time, enterprises overcame their skepticism and moved critical applications to the cloud. DevOps fueled this shift to the cloud, as it gave decision-makers a sense of control over business-critical applications hosted outside their own data centers. Today, enterprises are in a similar phase of trying out and accepting machine learning (ML) in their production environments, and one of the accelerating factors behind this change is MLOps. Similar to cloud-native startups, many startups today are ML native and offer differentiated products to their customers.


Get used to hearing about machine learnings operations (MLOps) startups – TechCrunch

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Welcome to The TechCrunch Exchange, a weekly startups-and-markets newsletter. It's inspired by the daily TechCrunch column where it gets its name. If you aren't in the United States, it's a little hard to explain. In short, certain deficiencies in our policing and judicial systems flared brightly as the week came to a close. So, today's Exchange newsletter will be shorter than intended. Hug the people you love, and everyone else.


MLOps Explained

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MLOps (Machine Learning Operations) is one of the emerging job roles in recent times. According to the LinkedIn report, in the last four years, the demand for machine learning roles and artificial intelligence roles has spiked by 74% annually. Before the advancement of hardware, data technologies the AI field was handled by a small group of experts where they mostly worked with a limited set of data including academic datasets for research. And the data was specifically collected or prepared for specific research. Hence, the flow was smooth and easily manageable.


MLflow Installation

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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 governance of models in an order that they deliver positive value even as underlying conditions (variables) change. MLOps has grown into prominence to help organizations reduce the risk associated with Data Science, AI, and ML initiatives and maximize returns on analytics. Running ML models and managing its lifecycle needs continuous comparison of the performance of model versions and detection of model drifts, as and when they occur.


Is MLOps Leaving the Software Engineer Behind?

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A few years ago, Venture beat reported that only 13% of data science projects make it into production. Companies hired data scientists but neglected to put proper supports in place to productionize their efforts. They siloed the data scientists from the rest of the organization, gave them some API keys, and asked them to weave gold. This failed terribly and, thus, in the mists of a deep learning revolution and new AI summer, the enterprise was left wondering how and why they couldn't get a piece of the pie. Through a combination of process and tooling, MLOps promises to make your data science teams efficient by enabling them to build, test, ship, and measure models faster.


AWS re:Invent 2021 AI/ML Session Guide for Builders and Architects

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Listen to Dr. Swami Sivasubramanian, Vice President, Amazon Machine earning, and other speakers on the latest key development and innovations in AWS AI & ML. There are new product & service launches, customer stories, demos, and more in this 2-hour Machine Learning keynote session. If you're interested to find out more on the past re: Invent Machine Learning keynote, the full video session and blogs are available below. Hugging Face is a fast-growing, popular, open-source AI/ML community hub for Natural Language Processing (NLP) models, datasets, as well as community ML apps, demo spaces. I am very keen to learn how I can quickly train a Hugging Face transformer NLP model on Amazon SageMaker with just a few lines of code using PyTorch or TensorFlow with SageMaker's distributed training libraries in this workshop.