Dive into Learning Explore the rich courses offerings of MammothClub.

Test your knowledge with 50+ questions from the Model Deployment section of the Databricks Certified Machine Learning Professional certification. Find out if you’re ready to ace the exam — can you explain why batch deployment is the appropriate approach for the vast majority of deployment use cases, describe how batch deployment computes predictions and stores them for later use, identify the live serving benefits of querying precomputed batch predictions, recognize less performant data storage as a solution for certain other use cases, load registered models using load_model, and deploy a single-node model in parallel using spark_udf? And much more!

Modern fullstack development is about speed, structure, and seamless integration—and this stack delivers all three.

Featuring 15 code-driven challenges with unlimited attempts, this hands-on test empowers you to master the core wireless penetration testing tools in the Aircrack-ng suite. Learn to enable monitor mode with airmon-ng, capture live wireless traffic using airodump-ng, and inject packets or deauthenticate clients with aireplay-ng. Crack WEP and WPA-PSK keys using aircrack-ng, decrypt packets with airdecap-ng, and visualize attack paths through airgraph-ng. Each scenario replicates real-world wireless security assessments, giving you practical experience in capturing handshakes, analyzing networks, and executing ethical attacks to strengthen your understanding of wireless vulnerabilities.

Not all data lives in massive warehouses—sometimes a lightweight solution is enough.

Designed by humans, elevated with AI and perfected to deliver the best learning experience. Data science with R blends traditional analysis with powerful machine learning tools.

These tutorials bring together human insight and AI support to deliver dependable learning. Development goes beyond writing code—it’s about building reliable systems.

Test your knowledge (with 45+ questions and unlimited attempts!) on Evaluation and Monitoring: Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics, select key metrics to monitor for a specific LLM deployment scenario, evaluate model performance in a RAG application using MLflow, use inference logging to assess deployed RAG application performance, use Databricks features to control LLM costs for RAG applications, use inference tables and Agent Monitoring to track a live LLM endpoint, identify evaluation judges that require ground truth, compare the evaluation and monitoring phases of the Gen AI application life cycle.