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How to build a data science and machine learning roadmap in 2022

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Closing the gap between their organization's choice to invest in a data science and machine learning (DSML) strategy and the needs that business units have for results, will dominate data and analytics leaders' priorities in 2022. Despite the growing enthusiasm for DSML's core technologies, getting results from its strategies is elusive for enterprises. Market forecasts reflect enterprises' early optimism for DSML. IDC estimates worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services will grow 15.2% year over year in 2021 to $341.8 billion and accelerate further in 2022 with 18.8% growth, reaching $500 billion by 2024. In addition, 56% of global enterprise executives said their adoption of DSML and AI is growing, up from 50% in 2020, according to McKinsey.


Algorithmia founder on MLOps' promise and pitfalls

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All the sessions from Transform 2021 are available on-demand now. 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. While similar to DevOps, MLOps relies on different roles and skill sets: data scientists who specialize in algorithms, mathematics, simulations, and developer tools, and operations administrators who focus on upgrades, production deployments, resource and data management, and security. While there is significant business value to MLOps, implementation can be difficult in the absence of a robust data strategy.


Amazon, we don't need another AI tool or APl, we need an open AI platform for cloud and edge

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After Amazon's three-week re:Invent conference, companies building AI applications may have the impression that AWS is the only game in town. Amazon announced improvements to SageMaker, its machine learning (ML) workflow service, and to Edge Manager -- improving AWS' ML capabilities on the edge at a time when serving the edge is considered increasingly critical for enterprises. Moreover, the company touted big customers like Lyft and Intuit. But Mohammed Farooq believes there is a better alternative to the Amazon hegemon: an open AI platform that doesn't have any hooks back to the Amazon cloud. Until earlier this year, Farooq led IBM's Hybrid multi-cloud strategy, but he recently left to join the enterprise AI company Hypergiant.


DataRobot CEO calls for 'a new era of democratization of AI'

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Dan Wright just became CEO of DataRobot, a company valued at more than $2.7 billion that is promising to automate the building, deployment, and management of AI models in a way that makes AI accessible to every organization. Following the release of version 7.0 of the DataRobot platform, Wright told VentureBeat that the industry requires a new era of democratization of AI that eliminates dependencies on data science teams. He explained that manual machine learning operations (MLOps) processes are simply not able to keep pace with changing business conditions. This interview has been edited for brevity and clarity. VentureBeat: Now that you're the CEO, what is the primary mission?


What is DataOps, and why it's a top trend

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Enterprises have struggled to collaborate well around their data, which hinders their ability to adopt transformative applications like AI. The evolution of DataOps could fix that problem. The term DataOps emerged seven years ago to refer to best practices for getting proper analytics, and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just as the DevOps trend led to a better process for collaboration between developers and operations teams, DataOps refers to closer collaboration between various teams handling data and operations teams deploying data into applications. Getting DataOps right is a significant challenge because of the multiple stakeholders and processes involved in the data lifecycle.