Stay ConnectedMachine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.
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When developing ML models, defining and selecting the model architecture will be fundamental to ensure the best possible outcomes. Parameters that define the model architecture are referred to as hyperparameters and the process of searching for the ideal model architecture is referred to as hyperparameter tuning. Today on the show, Ben and Michael discuss hyperparameter tuning and how to implement this into your ML modeling.
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Enjoy this engaging AMA conversation with Michael Berk asking Ben Wilson various questions related to industry, strategy, and approaches in data science and ML engineering.
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Ben and Michael interview Maciej Balawejder, a mechanical engineering student passionate about AI, ML, and robotics. As an active contributor on Medium.com, Maciej has already made significant contributions to the AI and ML communities. On the show, they discuss Maciej’s recent article about optimizers in Machine Learning, plus their personal philosophies and approaches to deep learning.
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After ensuring your data has surpassed the hyper parameter tuning phase, what is the next step in your EDA protocol? Today on the show, Ben and Michael continue the discussion on EDA methodology within Machine Learning and discuss linear regression with OLS, decision trees, and common visualization tools for data scientists.
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EDA is primarily used in machine learning to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate. Today on the show, Ben and Michael discuss how to use EDA in machine learning models.
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MLlib is Apache Spark's scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best ML workflow with MLlib.
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Apache Spark is a lightning-fast unified analytics engine for large-scale data processing and machine learning. In this episode, Ben and Michael unpack Spark by ping-ponging questions and answers, supplemented by various examples applicable to machine learning workflows. In this Episode…
Ben and Michael walk through two different cases studies relative to production ML infrastructure and recommendation engines. The first is about a free on-line tutoring service for underserved communities called “Learn to Be”, and the second centers around the online course provider “Coursera”. Ben and Michael set up the case studies with fundamental problem statements, followed by their various approaches to executing the objectives to achieve the desired process outcomes. Sponsors
Sponsored By:Ben interviews Michael Griffiths, Director of Data Science at ASAPP, a company leveraging AI and ML to augment and automate human work, improve operational efficiencies and customer experiences, and ultimately empower people to be their best. Michael shares specific examples of how this can be done for human agent productivity within contact centers. They also discuss fully human controlled vs automated systems, delivering value with AI and ML, and the future of AI driven technology. In this Episode…
AutoML (automated machine learning) has become a hot topic over the past few years. Abid Ali Awan joins the show to share his approach to AutoML, when and how to utilize it compared to classic approaches. Ben and Abid also discuss open-source vs. proprietary platforms. What is AutoML? Automated machine learning provides methods and processes to make machine learning available for non-machine learning experts, to improve efficiency of machine learning and to accelerate research on machine learning. 2 levels of implementation: Blackbox AutoML can do one, or all of the things for feature selection with a statistical outset and self optimizing outcome.
Whitebox AutoML exposes the code to explain how it behaves and allows you to produce predictions as to the influencing variables. Leveraging open-source toolkits vs. proprietary “The output is the model, the input is the data, and you can use that model to predict anything according to your business problem.” - Abid Ali Awan Sponsors
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