Upskilling by way of role-based pathways to speed up your information + AI profession
Databricks has spent years crafting and iterating technical trainings for learners throughout information, 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 information, 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 might be created between 2022 and 2025. This presents a singular problem – upskilling expertise in a scalable manner.
Elevate your profession in the present day with Databricks’ Studying Competition
Databricks’ digital Studying Competition is a singular alternative to upskill and reskill throughout information engineering, information science, and information analytics programs constructed for our prospects, 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 beneath).
Studying aims throughout self-paced programs
1: Information Engineer Course – Information Engineering with Databricks
This course prepares information professionals to leverage the Databricks Information Intelligence Platform to productionalize ETL pipelines. College students will use Delta Dwell Tables to outline and schedule pipelines that incrementally course of new information from quite a lot of information sources into the platform. College students can even orchestrate duties with Databricks Workflows and promote code with Databricks Repos.
- Use the Databricks Information Science and Engineering Workspace to carry out widespread code improvement duties in an information engineering workflow.
- Use Spark SQL or PySpark to extract information from quite a lot of sources, apply widespread cleansing transformations, and manipulate complicated information with superior capabilities.
- Outline and schedule information pipelines that incrementally ingest and course of information by way of a number of tables within the lakehouse utilizing Delta Dwell Tables in Spark SQL or Python.
- Orchestrate information 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: Information Engineer Course – Superior Information Engineering with Databricks
On this course, college students will construct upon their current data of Apache Spark, Structured Streaming, and Delta Lake to unlock the complete potential of the generative information platform by using the suite of instruments offered by Databricks. This course locations a heavy emphasis on designs favoring incremental information processing, enabling programs optimized to repeatedly ingest and analyze ever-growing information. By designing workloads that leverage built-in platform optimizations, information engineers can cut back the burden of code upkeep and on-call emergencies, and rapidly adapt manufacturing code to new calls for with minimal refactoring or downtime. The subjects on this course needs to be mastered previous to making an attempt the Databricks Licensed Information Engineering Skilled examination.
- Design databases and pipelines optimized for the Databricks Information Intelligence Platform.
- Implement environment friendly incremental information processing to validate and enrich information driving enterprise choices and functions.
- Leverage Databricks-native options for managing entry to delicate information and fulfilling right-to-be-forgotten requests.
- Handle code promotion, job orchestration, and manufacturing job monitoring utilizing Databricks instruments.
3: Information Analyst Course – Information 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 Information Evaluation of Databricks SQL certification. Members will study ingesting information, writing queries, producing visualizations and dashboards, and find out how to join Databricks SQL to extra instruments by utilizing Associate Join.
- Describe how Databricks SQL works within the Lakehouse structure
- Combine Unity Catalog and Delta Lake with Databricks SQL
- Describe how Databricks SQL implements information safety
- Question information 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 find out how to 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, equivalent to Pandas UDFs, Pandas Features, and the pandas API on Spark, in addition to the newest ML product choices, equivalent to Characteristic Retailer and AutoML.
- 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 Characteristic 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 information 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 should have constructed an end-to-end pipeline to log, deploy, and monitor machine studying fashions.
- Observe, model, and handle machine studying experiments.
- Leverage Databricks Characteristic Retailer for reproducible information 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 provided as a part of the Databricks Studying Competition.
* Easy methods to be eligible for Databricks certification voucher
A 50%-off Databricks certification voucher1 might be given to the primary 5,000 customers who full no less than one of many role-based programs inside the length of the digital Studying Competition.
1The remaining US $100 might be paid for by way of webassesor on the time of the examination registration by way of bank card solely.
- Just one voucher might be given, whether or not the learner completes one or a number of course(s) / studying plan(s).
- The voucher could have a validity interval of 6 months (i.e. expire after 6 months).
- The voucher is relevant for the next exams solely:
- Databricks Licensed Information Engineer Affiliate
- Databricks Licensed Information Engineer Skilled
- Databricks Licensed Information Analyst Affiliate
- Databricks Licensed Machine Studying Affiliate
- Databricks Licensed Machine Studying Skilled
- The voucher might be distributed 1-2 week(s) after the occasion closes.
- The certification voucher can’t be mixed with different affords or success credit.
Have questions? Ask within the Databricks Neighborhood: Databricks Academy Learners Group
Start upskilling and reskilling in the present day with Databricks Academy with the digital Databricks Studying Competition