Empowering Data Scientists with MLOps

Empowering Data Scientists with MLOps
Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of cnvrg.io will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.
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Data Strategy Best Practices

infogix

Your Data Strategy should be concise, actionable, and understandable by business and IT! Data is not just another resource. It is your most powerful, yet poorly managed and therefore underutilized organizational asset. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Overcoming lack of talent, barriers in organizational thinking, and seven specific data sins are organizational prerequisites to be satisfied before (a measurable) nine out of 10 organizations can achieve the three primary goals of an organizational Data Strategy, which are to:
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Why IBM for Sales Performance Management?

FinTech

Improve sales performance and operational efficiencies with better management of incentive compensation plans and smarter administration of sales territories and quotas. Get faster insights with advanced analytics.
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The evolution of analytics: From BI to AI

AVORA

Analytics have come a long way – from data warehousing and manual report building to centralised data in the cloud and machine learning driven analytics. Business Intelligence (BI) tools were revolutionary when they arrived: they made analytics more accessible to the wider business users. Their speedy and powerful analytics provided insights that businesses only dreamt off in the past. But as technology advances, so has the analytics world evolved.
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Cloud Data Strategies for Mission-Critical, Distributed Applications

When organizations need to move quickly to launch new, digitally transformed applications that rely on terabytes (if not petabytes) of data, they cannot afford to wait for legacy database management systems to catch up. Cloud computing platforms give organizations the ability to stand up systems quickly, but if the data layer cannot offer the linear scalability, low latency, high availability, performance, fault tolerance, security, and agility needed for today’s 24/7 applications, organizations will never realize their objectives.
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