DevOps Archives - Liwaiwai

2022 Predictions : AI/ML To Disrupt SNVC Space As DevOps Specialization And Metaverse Play Their Part

The drive for progress in Information Technology (IT) for the year 2022 will emerge from the anxieties churned up by 2020 and 2021. No matter what plans and vision accompanied us as we entered this decade, things have uniformly aligned themselves to a single human goal – Sustainability. This is the umbrella under which our experts from various technology fields have predicted the course for 2022. The preamble is majorly centered on – Security, Decision Intelligence, Global accessibility, and Work-strength. Storage Storage Sustenance to Find Data Protection and Storage Security at its Core If there was one lesson that 2021…

Intel Advances AI Inferencing For Developers

What’s New: Since OpenVINO™ launched in 2018, Intel has enabled hundreds of thousands of developers to dramatically accelerate AI inferencing performance, starting at the edge and extending to the enterprise and the client. Today, ahead of MWC Barcelona 2022, the company launched a new version of the Intel® Distribution of OpenVINO Toolkit. New features are built upon three-and-a-half years of developer feedback and include a greater selection of deep learning models, more device portability choices and higher inferencing performance with fewer code changes. “The latest release of OpenVINO 2022.1 builds on more than three years of learnings from hundreds of thousands of…

Key Requirements For An MLOps Foundation

AI-driven organizations are using data and machine learning to solve their hardest problems and are reaping the rewards. “Companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with +120% cash flow growth,”1 according to McKinsey Global Institute. But it’s not easy right now. Machine learning (ML) systems have a special capacity for creating technical debt if not managed well. They have all of the maintenance problems of traditional code plus an additional set of ML-specific issues: ML systems have unique hardware and software dependencies, require testing and validation of data as well as…

Advancing Azure Service Quality With Artificial Intelligence: AIOps

“In the era of big data, insights collected from cloud services running at the scale of Azure quickly exceed the attention span of humans. It’s critical to identify the right steps to maintain the highest possible quality of service based on the large volume of data collected. In applying this to Azure, we envision infusing AI into our cloud platform and DevOps process, becoming AIOps, to enable the Azure platform to become more self-adaptive, resilient, and efficient. AIOps will also support our engineers to take the right actions more effectively and in a timely manner to continue improving service quality…

Microsoft Build 2019 | Breaking the Wall between Data Scientists and App Developers with MLOps

Microsoft Build 2019 | Breaking the Wall between Data Scientists and App Developers with MLOps Session ID: BRK2004 MLOps is DevOps for machine learning and brings continuous integration, delivery and learning to the machine learning lifecycle. This session will show you how to get to market faster with better models, and operationalize more reliably with greater governance. See a real-world fraud detection scenario where online retailers used MLOps to track and identify model production, efficiently validate models and determine best practices. Learn how to store and version models, collect continuous feedback on model behavior. Maintain quality models to keep…

Microsoft Build 2019 | MLOps: How to Bring Your Data Science to Production

Microsoft Build 2019 | MLOps: How to Bring Your Data Science to Production Session ID: BDL2026 While many enterprises have already adopted DevOps and GitOps strategies to improve their developers’ efficiency, most have yet to employ the same techniques for their machine learning pipelines. In this panel, we will discuss the practice of MLOps, and how to use the portfolio of services, tools, best-practices released by Azure to accelerate time to real-world ML solutions.”

Google Cloud Next 2019 | Networking On Cloud: Unity’s Networking Journey On GCP

Google Cloud Next 2019 | Game Sessions Google Cloud Next 2019 | Networking On Cloud: Unity’s Networking Journey On GCP Unity has a global team across five continents with over six business units that have more than 50 aggregated services managed by different teams. Now what makes this migration journey interesting is that the entire infrastructure and security was managed and enforced by a team of four people. The network design was complex, as every workload had different SLAs and security requirements. Hence, careful network planning was required, from IP addressing to HA, DR, shared services, and security policing. But…

Google Cloud Next 2019 | BigQuery Tips: Nested and Repeated Fields And How GOJEK Builds Data Warehouses

Google Cloud Next 2019 | Retail Sessions Google Cloud Next 2019 | BigQuery Tips: Nested and Repeated Fields And How GOJEK Builds Data Warehouses Gain expert advice on BigQuery to take advantage of data warehousing. In this session, GOJEK, a global company with multiple products and services and lots of data, will share how they have designed their data model to have BigQuery and Google Cloud Storage for their data lake and data warehouse, how they’ve set up their infrastructure using GCP, and how they’ve built internal tools as part of this process. GOJEK will also share how they govern…

Google Cloud Next 2019 | Things I Wish I Knew Before Building Serverless ML Solutions

Google Cloud Next 2019 | Retail Sessions Google Cloud Next 2019 | Things I Wish I Knew Before Building Serverless ML Solutions Building ML models for a proof-of-concept app is easy. To deploy them in a production system is hard. It becomes even more challenging when you have to keep your models fresh by retraining them every day. These challenges are faced by every team working on deploying their ML solutions. The session will demonstrate how Omni Labs is doing this process in a serverless manner. After the talk you should be able to reason about your ML models and…

Google Cloud Next 2019 | Maintain Control Over Hybrid Workloads With DevOps Best Practices From Google Cloud

Google Cloud Next 2019 | Financial Services Sessions Google Cloud Next 2019 | Maintain Control Over Hybrid Workloads With DevOps Best Practices From Google Cloud Maintaining visibility, control, and compliance of hybrid workloads is increasingly complex. Administrators need to navigate and upkeep multiple tools, configs, policy systems and languages to ensure that their security and compliance requirements are met, while enabling developers agility. In this session, we demonstrate how to leverage out-of-the-box devops best practices and automation to manage infrastructure and configs as code from a centralized git repository. Features such as approval workflows, pre-deployment checks, ongoing monitoring, and…

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