MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services
مقدمة من
نبذة عن المقرر
This is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).
Data Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1(AWS): Deploying AI & ML Models in Production.
You will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.
Most importantly, by the end of this course, you will know...
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What data engineers need to know to work effectively with data scientists
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How to embed a predictive model in a pipeline that takes in data and outputs predictions automatically
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How to monitor the model’s performance and follow best practices
المدربين
John Elder, IV
Dr. John Elder founded the US's most experienced data science consulting team who has solved hundreds of challenges for commercial and government clients by extracting actionable knowledge from all types of data. John has created data mining tools, was a discoverer of ensemble methods, chairs international conferences, and is a popular workshop and keynote speaker.
Peter Bruce
Peter founded the Institute for Statistics Education at Statistics.com which was acquired by Elder Research, Inc. in 2019, The Institute specializes in introductory and graduate level online education in statistics, optimization, risk modeling, predictive modeling, data mining, and other subjects in quantitative analytics.
Shree Taylor
Dr. Shree Taylor, trained as a computational mathematician, has been delighted to create a rewarding non-traditional career centered around mathematics, business and innovation. She has served clients in the government, private and non-profit sectors for over 15 years, and is known for presenting highly technical analyses and topics to non-technical audiences in a format that is relatable and relevant to customers and stakeholders. Dr. Taylor loves to develop leaders; she is an authentic, empathetic and adaptive leader who engages with colleagues to help them reach their team and individual goals.
Bryce Pilcher
Bryce brings a background in network simulation and software reliability to client tool design. Bryce has leveraged graphs and Java to provide a new interfaces for clients to look at and interact with their data. His love of learning has led him to use Python for gathering useful information from the web and Scala to develop functional and reliable software solutions.
Allison Marrs
Allison helps government agencies and companies solve real-world problems in diverse industry segments. She assisted in developing data and machine learning pipelines using Python and AWS Cloud Platform. Her passion for harnessing data to promote human flourishing and her keen attention to detail give her the ability to view projects through a data-driven and analytical lens.
Ramzi Ziade
Ramzi's passion lies in analytics and data science. He take satisfaction in extracting insights from data and helping businesses understand its impact on performance and decision making. Ramzi's experience is rooted in entrepreneurship and technical consulting.
Greg Carmean
Greg is a data scientist and enjoys helping clients solve business problems and improve their processes. Previously, he was a Data Analyst for the US Navy where he led software development efforts for his group. He leveraged data to solve scientific and operational problems and co-led the development and deployment of an analytics product which more than doubled fleet demand for his group’s services.
LeAnna Kent
LeAnna is a creative, data-driven problem solver dedicated to utilizing her analytical skills to improve the daily lives of others. LeAnna enjoys researching innovative options to achieve goals and objectives and has experience employing her skills to solve real-world problems and influence policy. LeAnna is passionate about the power analytics has to inform decision making.
Henry Mead
Henry joins Elder Research’s Arlington office as a data scientist following three years working on tax and public finance policy on and off Capitol Hill. He is passionate about the ethics of artificial intelligence and invests, taking the time to understand all four corners of a client’s vision and needs.
Kuber Deokar
Kuber is responsible for the coordination of online courses and ensures seamless interactions between the management teams, course creators, course instructors, teaching assistants, and students. Kuber also handles continuous course improvement projects in his capacity as Data Science Lead at UpThink Edutech Services. He has a special interest in Machine Learning, Predictive Analytics, Statistical Modeling, SQL, R, and app development.
Janet Dobbins
Janet works with colleges and universities to create innovative curriculum; and industry teams to help them gain necessary data science and technical skills.
She is on the Board and past President of Data Community DC, a non-profit 501(c)(3) corporation committed to promoting data science by fostering education, opportunity, and professional development through high-quality community-driven events.
