First of all you must be thinking, what is DevOps? What the hell is DevSecOps? And will we keep adding more acronyms to it? It could look like something like this in the coming years.
Read MoreDevSecOps and DevOps: A Deep Dive
Tag: DevOps
MLOps: Model Monitoring 101
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to model building so that ML models can constantly improve itself under different scenarios.
Read MoreDeloitte: MLOps is about to take off in the enterprise
Deloitte Consulting published a report today that suggests a golden age of AI is in the offing, assuming organizations can implement and maintain a consistent approach to machine learning operations (MLOps). Citing market research conducted by AI-focused Cognilytica, the MLOps: Industrialized AI report from Deloitte notes that the market for MLOps platforms is forecast to generate annual revenues in excess of $4 billion by 2025.
Read MoreDemocratizing access to AI with K3ai (keɪ3ai)
K3ai (kei3ai) is a lightweight infrastructure-in-a-box specifically built to install and configure AI tools and platforms to quickly experiment and/or run in production over edge devices.
Read MoreAmazon launches new AI services for DevOps and business intelligence applications
Amazon today launched SageMaker Data Wrangler, a new AWS service designed to speed up data prep for machine learning and AI applications. Alongside it, the company took the wraps off of SageMaker Feature Store, a purpose-built product for naming, organizing, finding, and sharing features, or the individual independent variables that act as inputs in a machine learning system. Beyond this, Amazon unveiled SageMaker Pipelines, which CEO Andy Jassy described as a CI/CD service for AI.
Read MoreHow to easily deploy ML Models to production
One of the known truths of the Machine Learning(ML) world is that it takes a lot longer to deploy ML models to production than to develop it.¹ The problem of deploying ML models to production is well known. Let’s discuss some different options you have when it comes to deploying ML models. Variants are provided in order from the most general to ML-specific.
Read MoreTechnology in the Oil and Gas industry: An MLOps Perspective
The Oil and gas industry generates an annual revenue that was approximately $3.3 trillion in 2019 and is one of the largest enterprises in the world. Oil and natural gas upstream, midstream and downstream processes constantly generate large amounts of data and is immensely dependent on sophisticated technologies to reveal new insights in the business i.e prevent equipment malfunctioning and improve operational efficiency…
Read MoreA-Z Of DevOps: Managing multiple environments with the help of these tools
In most DevOps settings you’ll find that there are multiple environments in the pipeline. You might have conditions that change the environment based on which branch was merged or when a branch is tagged for release. There are a number of reasons you want to have more than just a production environment, the biggest reason being testing.
Read More7 Best DevOps security practices: DevSecOps and its merits
DevOps has transformed the way operational engineers and software developers reason. Gone are the days when a code was written, implemented, and managed by operations. The DevOps model has remodeled the system of product and application production. As a result, faster results have become the pinnacle of delivering at the speed which the market demands.
Read MoreKey aspects of Machine Learning operations, explained
Until 2015, even professional programmers didn’t consider machine learning has real potential and benefits. However, with innovation the development of AI and computing capabilities build-up, autonomous MLOps platforms began to develop rapidly and became an integral part of computer systems development.
Read MoreDataOps & DevOps
DevOps vs. DataOps. The demand for access to data assets and products forever increasing. To gain competitive advantage, DataOps is indispensable, however is struggling to keep up with the rhythm of the DevOps-equipped teams demand.
Read MoreWhy Data Science is a team sport?
Data Science is a Team Sport. The author covers why he considers data science as a team sport? From data science use-case identification to the deployment of the models in production, so much goes into data science projects.
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