Deloitte: 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 More

Amazon 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 More

How 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 More

Technology 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 More

A-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 More

7 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 More

Key aspects of Machine Learning operations, explained

Machine Learning Operations

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 More