Simplifying Analytics with Natural Language

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.

It’s not always about the numbers; it’s more about how you present them in an easily understandable way. Not everything can go into a pie chart, and not everything looks right in a line graph. That’s why it’s crucial to know the best principles of data visualization.

View this webinar where our experts discussed the new era of analytics with the Microsoft Azure Synapse Analytics platform. It is a limitless analytics service with unmatched time to insight that bring together data integration, enterprise data warehousing and big data analytics – all into a single service.

PrecisionProfile – a bioinformatics technology company – focuses on enabling oncologists and research scientists to rapidly analyze genomic profiles and create personalized treatment plans for cancer patients. Like most organizations, PrecisionProfile struggled with the most time-consuming part of every analytics exercise - combing, cleaning, and shaping data into actionable information. With self-service data preparation, they were able to design and develop a platform to accelerate data pipelines, enabling scientists to spend more time analyzing data and formulating how they can leverage it to save lives. View this webcast to learn: Empower researchers and oncologists to spend a fraction of their time restructuring data. Reduced cycle time of a genomic clinical study from 1-3 months to 2-8 hours.