Upskilling by means of role-based pathways to speed up your knowledge + AI profession
Databricks has spent years crafting and iterating technical trainings for learners throughout knowledge, analytics, and AI disciplines to make sure that people, groups, and organizations that need to upskill or reskill have accessible and related content material. With the explosion of AI/ML and roles in knowledge, analytics, and AI, the necessity to undertake new know-how has accelerated for a lot of organizations. It is predicted that 97 million jobs involving AI shall be created between 2022 and 2025. This presents a singular problem – upskilling expertise in a scalable approach.
Elevate your profession at this time with Databricks’ Studying Competition
Databricks’ digital Studying Competition is a singular alternative to upskill and reskill throughout knowledge engineering, knowledge science, and knowledge analytics programs constructed for our clients, prospects, and companions. This occasion will present entry to free self-paced, role-based content material. For many who efficiently full the self-paced coaching, they are going to be eligible to obtain a 50%-off Databricks certification voucher (extra particulars under).
Studying aims throughout self-paced programs
1: Knowledge Engineer Course – Knowledge Engineering with Databricks
This course prepares knowledge professionals to leverage the Databricks Knowledge Intelligence Platform to productionalize ETL pipelines. College students will use Delta Stay Tables to outline and schedule pipelines that incrementally course of new knowledge from quite a lot of knowledge sources into the platform. College students will even orchestrate duties with Databricks Workflows and promote code with Databricks Repos.
Studying Targets:
- Use the Databricks Knowledge Science and Engineering Workspace to carry out frequent code growth duties in an information engineering workflow.
- Use Spark SQL or PySpark to extract knowledge from quite a lot of sources, apply frequent cleansing transformations, and manipulate advanced knowledge with superior capabilities.
- Outline and schedule knowledge pipelines that incrementally ingest and course of knowledge by means of a number of tables within the lakehouse utilizing Delta Stay Tables in Spark SQL or Python.
- Orchestrate knowledge pipelines with Databricks Workflow Jobs and schedule dashboard updates to maintain analytics up-to-date.
- Configure permissions in Unity Catalog to make sure that customers have correct entry to databases for analytics and dashboarding.
2: Knowledge Engineer Course – Superior Knowledge Engineering with Databricks
On this course, college students will construct upon their current information of Apache Spark, Structured Streaming, and Delta Lake to unlock the complete potential of the generative knowledge platform by using the suite of instruments supplied by Databricks. This course locations a heavy emphasis on designs favoring incremental knowledge processing, enabling methods optimized to repeatedly ingest and analyze ever-growing knowledge. By designing workloads that leverage built-in platform optimizations, knowledge engineers can scale back the burden of code upkeep and on-call emergencies, and shortly adapt manufacturing code to new calls for with minimal refactoring or downtime. The matters on this course ought to be mastered previous to trying the Databricks Licensed Knowledge Engineering Skilled examination.
Studying Targets:
- Design databases and pipelines optimized for the Databricks Knowledge Intelligence Platform.
- Implement environment friendly incremental knowledge processing to validate and enrich knowledge driving enterprise selections and purposes.
- Leverage Databricks-native options for managing entry to delicate knowledge and fulfilling right-to-be-forgotten requests.
- Handle code promotion, job orchestration, and manufacturing job monitoring utilizing Databricks instruments.
3: Knowledge Analyst Course – Knowledge Evaluation with Databricks SQL
This course supplies a complete introduction to Databricks SQL. It’s designed with the intention of supporting people searching for the Affiliate Knowledge Evaluation of Databricks SQL certification. Members will find out about ingesting knowledge, writing queries, producing visualizations and dashboards, and join Databricks SQL to further instruments by utilizing Companion Join.
Studying Targets:
- Describe how Databricks SQL works within the Lakehouse structure
- Combine Unity Catalog and Delta Lake with Databricks SQL
- Describe how Databricks SQL implements knowledge safety
- Question knowledge in Databricks SQL
- Use SQL instructions particular to Databricks
- Create visualizations and dashboards in Databricks SQL
- Use automation and integration capabilities in Databricks SQL
- Share queries and dashboards with others utilizing Databricks SQL
4: Machine Studying Practitioner Course – Scalable Machine Studying with Apache Spark
This course teaches you scale ML pipelines with Spark, together with distributed coaching, hyperparameter tuning, and inference. You’ll construct and tune ML fashions with SparkML whereas leveraging MLflow to trace, model, and handle these fashions. This course covers the newest ML options in Apache Spark, resembling Pandas UDFs, Pandas Features, and the pandas API on Spark, in addition to the newest ML product choices, resembling Function Retailer and AutoML.
Studying Targets:
- Carry out scalable EDA with Spark
- Construct and tune machine studying fashions with SparkML
- Observe, model, and deploy fashions with MLflow
- Carry out distributed hyperparameter tuning with HyperOpt
- Use the Databricks Machine Studying workspace to create a Function Retailer and AutoML experiments
- Leverage the pandas API on Spark to scale your pandas code
5: Machine Studying Practitioner Course – Machine Studying in Manufacturing
On this course, you’ll be taught MLOps greatest practices for placing machine studying fashions into manufacturing. The primary half of the course makes use of a function retailer to register coaching knowledge and makes use of MLflow to trace the machine studying lifecycle, bundle fashions for deployment, and handle mannequin variations. The second half of the course examines manufacturing points together with deployment paradigms, monitoring, and CI/CD. By the top of this course, you’ll have constructed an end-to-end pipeline to log, deploy, and monitor machine studying fashions.
Studying Targets:
- Observe, model, and handle machine studying experiments.
- Leverage Databricks Function Retailer for reproducible knowledge administration.
- Implement methods for deploying fashions for batch, streaming, and real-time.
- Construct monitoring options, together with drift detection.
There are 4 extra Studying Plans supplied as a part of the Databricks Studying Competition.
* Tips on how to be eligible for Databricks certification voucher
A 50%-off Databricks certification voucher1 shall be given to the primary 5,000 customers who full no less than one of many role-based programs inside the period of the digital Studying Competition.
1The remaining US $100 might be paid for by means of webassesor on the time of the examination registration by means of bank card solely.
Notice:
- Just one voucher shall be given, whether or not the learner completes one or a number of course(s) / studying plan(s).
- The voucher can have a validity interval of 6 months (i.e. expire after 6 months).
- The voucher is relevant for the next exams solely:
- Databricks Licensed Knowledge Engineer Affiliate
- Databricks Licensed Knowledge Engineer Skilled
- Databricks Licensed Knowledge Analyst Affiliate
- Databricks Licensed Machine Studying Affiliate
- Databricks Licensed Machine Studying Skilled
- The voucher shall be distributed 1-2 week(s) after the occasion closes.
- The certification voucher can’t be mixed with different provides or success credit.
Have questions? Ask within the Databricks Group: Databricks Academy Learners Group
Start upskilling and reskilling at this time with Databricks Academy with the digital Databricks Studying Competition
