🥅 Each set of sessions (typically, 3-4 per set) cover a mix of need-to-know minimums across required skills to build something. This will be followed up with additional sessions as and when there’s a demand.
All sessions, current and archived.
Learn about production grade API development. This is the second of two sessions. ...
Learn about production grade API development. This is the first of two sessions. ...
A good Data Analyst is likely to be assigned various tasks (because of how awesome they have been on other tasks), some of which (explicitly or implicitly) require good Python programming skills to make them more efficient. This session is intended to help Data Analysts become more productive by using Python to automate parts of their growing variety of tasks. ...
A good Data Engineer must be familiar with GUI-based tools and be able to develop efficient code for data engineering activities, such as developing and maintaining ETL/ELT pipelines, data transformations, feature engineering to support model training, and insights generation. ...
Virtual machines, Docker containers, and Kubernetes are essential technologies for modern software development and deployment; including serving of ML models. Learn the basics of these technologies and how they can be used to build, run, and manage containerized applications. ...
Learn what is serverless computing, how to use them for various use cases. ...
Learn how to use embeddings, vector databases, and similarity measures to solve real-world problems. These powerful tools power many of the products and services we use today, from search engines to social media platforms. In this session, you’ll learn the basics of these technologies and how to use them to build your own innovative applications. Embeddings are a powerful way to represent data in a high-dimensional space. Vector databases provide efficient storage and retrieval of embedding data. Similarity measures can be used to compare embeddings and identify similar data points. You will learn how to use these powerful tools to solve real-world problems in a variety of domains, including natural language processing, product recommendation, and image search. ...
Master MLOps fundamentals in this 2-part session. Learn model lifecycle best practices like versioning, monitoring and explainability. Then, explore deployment, optimization and governance techniques to keep models performing over time in complex environments. Walk away with practical skills to successfully manage machine learning applications from development through production. ...
Learn how Large Language Models (LLMs) work, and how to use them for various use cases, how they can be customized for specific use cases. ...
Autoencoders are used in various applications, including fraud detection, predictive maintenance, image processing, etc. This session will give you a comprehensive overview of autoencoders. It will go over key concepts, model architectures, and training methods. Beginner to experienced individuals can all benefit from this focus on applicable skills development. ...