Content provided by Toronto Machine Learning Society.
Toronto, Canada, June 16 – 18 – Toronto Machine Learning Society (TMLS) hosts MLOps, Production & Engineering 2020 through an interactive conference to enable attendees the opportunity to virtually engage with speakers and establish a stronger network within the AI community.
MLOps: Production and Engineering World is the world’s first virtual conference focusing specifically on machine Learning in Production. It is intended to unite and support the wider AI Ecosystem, and companies operating within it. With an explorative approach, our initiatives address the unique needs of our community of over 7,500+ ML researchers, professionals, entrepreneurs, and engineers. Intended to empower its members and propel AI research, & business applications on a global stage, our events attempt to re-imagining what it means to have a connected community; offering support, growth, inclusion for all participants.
60 + Speaker Including:
● Senior Algorithm Expert, Alibaba Group● Chief Data Scientist, AI Task Force , United States Army● AI Innovation Team Lead, General Motors● Senior Cloud AI/ML Leader, Google Inc.● Head of Engineering, Petuum● Senior Cloud Solution Architect, Microsoft● Head of Research, MIT, IBM Watson AI Lab● Technical Advocate, Executive Briefing Center, GitHub● CEO, Core contributor, Great Expectations● CTO, John Snow Labs● Co Founder, Chief Data Scientist, Anodot● Founder, CTO, Iguazio Founder, Algorithmia● Machine Learning Lead Engineer, DKB Bank● Deep Learning Engineer, Weights & Biases● Vice President, Machine Learning Expert, Morgan Stanley● Principal Data Scientist, Oracle● Senior Data Scientist, Pitney Bowes● SEE FULL LIST HERE
Topics & Workshops Include:
● Model Validation, Model Monitoring,Model Drift, Model De-bugging,● Building and Operating an Open Source Data Science Platform● Turbo-Charging Data Science with AutoML● Deploying Spark Model with MLflow and Sagemaker● ML Model Deployment with Azure Machine Learning Services● Simplify ML Pipeline Automation and Tracking using Kubeflow and ServerlessFunctions● Managing Machine Learning Experiments with MLflow● Quickly Deploy ML Workloads on Multi-Cloud using Kubeflow Pipelines● The Do’s and Don’ts of Delivering AI Projects: A Practitioners Guide● Real-World Strategies for Model Debugging● SEE FULL LIST HERE
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