Discussing the practices for moving your AI/machine learning solutions from design and testing towards production and live data. As more companies are moving their AI/machine learning from testing to deployment, companies require a new set of MLOps / AIOps skills for combining AI/machine learning, DevOps deployment, and live data engineering.
TensorFlow is one of the most popular open-source machine learning technologies, and you can get up to speed on the latest developments and best practices of this technology. Pioneered by the Google Brain team, TensorFlow makes large-scale machine learning projects faster and easier.
Data science enables a company to utilize algorithms and predictive models to draw insights and forecasts from their data. What are the cutting-edge technologies and best practices for implementing data science initiatives in your company, and what types of predictive models prove the most useful?
Chatbots and natural language processing are rapidly redefining industries such as customer service and support, information search, knowledge management, and fraud detection. Learn how to utilize existing technologies to build your own chatbot or NLP solution.
This track targets both beginners and professionals looking to utilize open source or commercial machine learning technologies to implement business solutions. We will cover libraries and frameworks, training the AI with data, and analyzing / implementing results.
For AI professionals and advanced engineers, this track highlights the cutting edge of neural networks, automating human tasks through deep learning, advanced image / voice recognition, and thought leadership on the next phases of AI.