Site reliability engineering platform Blameless announced Tuesday it raised $30 million in a Series B funding round, led by Third Point Ventures with participation from Accel, Decibel and Lightspeed Venture Partners, to bring total funding to over $50 million.
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MLOps, a compound of machine learning and information technology operations, sits at the intersection of developer operations (DevOps), data engineering, and machine learning. The goal of MLOps is to get machine learning algorithms into production. Kenny Daniel, founder and CTO of Algorithmia, the company behind the enterprise MLOps platform, spoke about the buzz around MLOps, its benefits, and its challenges.
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One of the primary challenges of any ML/AI project is transitioning it from the hands of data scientists in the develop phase of the data science lifecycle into the hands of engineers in the deploy phase.
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A guide to building an automated MLOps pipeline by leveraging the trusted DevOps toolset. Throughout the past few years, Machine Learning (ML) saw an exponential rise in popularity thanks to the advancements led by companies like Google and Facebook, along with the contributions of the open-source community. And since it can be applied to a very broad range of use-cases, nearly every company in the world started leveraging ML and integrating it in its processes.
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DevOps tools are certain software services that ensure transparency, automation, and collaboration for organizations to stay at the forefront of the value stream. Hence, there are a wide variety of DevOps tools available for every requirement. In this article, we will be exploring two DevOps Tools in particular, Ansible and Docker.
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IBM today announced a new serverless framework called CodeFlare that’s designed to reduce the time developers spend preparing AI models for deployment in hybrid cloud environments. The company says it automates the training, processing, and scaling of models to enable engineers to focus on data insights.
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Feature stores are like data warehouses for data science. Their primary goal is to enable data scientists to short-circuit the time it takes to go from data ingestion to ML model training & inference. As such, they fill an important gap in the MLOps Lifecycle.
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Continuous testing is an essential part of DevOps. To get the maximum value from continuous testing, it is important to use recommended best practices, inclusive of people, processes and technologies. A gap assessment is a great way to efficiently evaluate an organization’s practices for DevOps and determine a strategy for improvement. Gap analysis provides valuable input for formulating a strategy and a roadmap to improve continuous testing.
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Checkly, an active monitoring platform for APIs and web apps, today announced that it closed a $10 million series A funding round led by CRV with participation from Accel, Mango Capital, and Guillermo Rauch. The capital, which brings the company’s total raised to $12.25 million, will be used to expand Checkly’s engineering team and build out its platform, according to CEO Hannes Lenke.
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JFrog, the company best known for a platform that helps developers continuously manage software delivery and updates, is making a deal to help it expand its presence and expertise in an area that has become increasingly connected to DevOps: security. The company is acquiring Vdoo, which has built an AI-based platform that can be used to detect and fix vulnerabilities in the software systems that work with and sit on IoT and connected devices. The deal — in a mix of cash and stock — is valued at approximately $300 million, JFrog confirmed to me.
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omino Data Lab, a pioneer provider of a machine learning operations (MLOps) platform, is making it easier for data scientists to manage code at a time when providers of DevOps platforms are starting to contend AI models as just another software artifact that needs to be managed within the context of any application development project.
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Zero trust as a framework for securing modern enterprises has been around for years, but is drawing renewed attention with the increase in cyberattacks. The United States government is pushing for zero trust implementations across all its agencies, and more vendors are jumping on board the already rolling zero trust product bandwagon. The mix of user need and vendor hype makes zero trust frameworks especially difficult to evaluate. Can a given zero trust solution stand up to close scrutiny? Buyers need to define and test an impartial, balanced set of complex criteria before making their purchase decisions.
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In this world of cross-functional teams and microservice architecture, DevOps skills become increasingly important, and that starts with understanding CI/CD (continuous integration, continuous delivery, and continuous deployment). In this article we’ll talk about best practices for CI/CD and how platforms like Armory can help manage some of the complexity involved.
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Graylog, a platform for collecting, indexing, and analyzing log messages, today announced that it completed an $18 million growth equity round led by new investor Harbert Growth Partners and coinvestor Piper Sandler Merchant Banking. CEO Andy Grolnick says that the funds, which bring Graylog’s total raised to $28.5 million, will support the company’s growth as it looks to become a leader in the log management and analytics market.
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Figuring out what people mean when they say “MLOps” is hard. Figuring out how to properly do MLOps, even for a technical person, is perhaps even more difficult. How difficult must doing MLOps be, then, for a citizen data scientist that knows nothing of web technologies, Kubernetes, monitoring, cloud infrastructure, etc.? Here I continue exploring how to set up an open-source MLOps framework for this purpose: specifically, I outline and show how a combination of Databricks, mlflow, and BentoML can potentially provide a compelling, extensible, and easy-to-use MLOps workflow for end-users.
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The third way of DevOps—continuous improvement—requires an organizational commitment to continuous learning and mastery at the most advanced level.
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The first way of DevOps, continuous flow, is the foundation for DevOps. Once mastered, an organization will naturally want to master the second way of DevOps to achieve greater benefits that continuous feedback offers when engineered well.
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In this article, I will walk through an entire Machine Learning Operation Cycle and show how to establish every step of the way using Azure Data Factory (ADF). Yes, it is possible, easy, and extremely reliable. As a bonus, it also automatically sets you up to receive alerts for any sort of data anomalies occurring throughout the process, so you do not have to worry about monitoring the workflow manually.
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A few really interesting ideas came out of this week’s panel on Enabling Smart Engineering Discussions. There’s a lot of talk these days around how to practice a “blameless culture” in software engineering, and I think it’s important to note the variety of views that make up that idea.
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DevOps is an evolving body of knowledge. There is no one-stop shop that covers all roles and the complete range of skills required for DevOps. A DevOps-competent workforce requires an ongoing commitment to DevOps skills development and mastery. Enterprises must approach DevOps training strategically to leverage training resources to their best advantage.
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