Data and Analytics Trends for 2019 and Beyond: A Panel Discussion

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.

The challenges and complexity of maintaining a “ready-to-recover” posture grow exponentially as the volume of data, the number of locations and numbers of workloads continue to skyrocket. Join two seasoned backup-and-recovery experts as they discuss solutions for managing backups at a massive scale and how you can deliver predic

The number of sources generating continuous, streaming data has exploded in recent years. From website clickstream data to telemetry data from Internet of Things (IoT) devices, the variety, volume, and velocity of data continues to increase. In response, businesses are evolving their analytics approach from batch to real time, and turning to new tools to deliver actionable insights in seconds instead of hours or days. With AWS, you can easily and cost-effectively collect, process, and analyze real-time streaming data at any scale, so you can learn what your applications and customers are doing right now—and respond immediately.

According to the Office for National Statistics, in April 2020, the number of remote workers increased to 46% of the population. Now with the easing of lockdown measures, many businesses are considering or are already implementing what the Financial Times calls a "split model' a.k.a "hybrid working" that divides the time between the home and the office. If home-office working is to become the norm, then organisations need to consider the data protection challenges they will be faced with.