Π£ Π½Π°Ρ Π²Ρ ΠΌΠΎΠΆΠ΅ΡΠ΅ ΠΏΠΎΡΠΌΠΎΡΡΠ΅ΡΡ Π±Π΅ΡΠΏΠ»Π°ΡΠ½ΠΎ MLOps on Databricks: A How-To Guide ΠΈΠ»ΠΈ ΡΠΊΠ°ΡΠ°ΡΡ Π² ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ½ΠΎΠΌ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅, Π²ΠΈΠ΄Π΅ΠΎ ΠΊΠΎΡΠΎΡΠΎΠ΅ Π±ΡΠ»ΠΎ Π·Π°Π³ΡΡΠΆΠ΅Π½ΠΎ Π½Π° ΡΡΡΠ±. ΠΠ»Ρ Π·Π°Π³ΡΡΠ·ΠΊΠΈ Π²ΡΠ±Π΅ΡΠΈΡΠ΅ Π²Π°ΡΠΈΠ°Π½Ρ ΠΈΠ· ΡΠΎΡΠΌΡ Π½ΠΈΠΆΠ΅:
ΠΡΠ»ΠΈ ΠΊΠ½ΠΎΠΏΠΊΠΈ ΡΠΊΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π½Π΅
Π·Π°Π³ΡΡΠ·ΠΈΠ»ΠΈΡΡ
ΠΠΠΠΠΠ’Π ΠΠΠΠ‘Π¬ ΠΈΠ»ΠΈ ΠΎΠ±Π½ΠΎΠ²ΠΈΡΠ΅ ΡΡΡΠ°Π½ΠΈΡΡ
ΠΡΠ»ΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠΎ ΡΠΊΠ°ΡΠΈΠ²Π°Π½ΠΈΠ΅ΠΌ Π²ΠΈΠ΄Π΅ΠΎ, ΠΏΠΎΠΆΠ°Π»ΡΠΉΡΡΠ° Π½Π°ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ ΠΏΠΎ Π°Π΄ΡΠ΅ΡΡ Π²Π½ΠΈΠ·Ρ
ΡΡΡΠ°Π½ΠΈΡΡ.
Π‘ΠΏΠ°ΡΠΈΠ±ΠΎ Π·Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅ΡΠ²ΠΈΡΠ° ClipSaver.ru
As companies roll out ML pervasively, operational concerns become the primary source of complexity. Machine Learning Operations (MLOps) has emerged as a practice to manage this complexity. At Databricks, we see firsthand how customers develop their MLOps approaches across a huge variety of teams and businesses. In this session, we will show how your organization can build robust MLOps practices incrementally. We will unpack general principles which can guide your organizationβs decisions for MLOps, presenting the most common target architectures we observe across customers. Combining our experiences designing and implementing MLOps solutions for Databricks customers, we will walk through our recommended approaches to deploying ML models and pipelines on Databricks. You will come away with a deeper understanding of how to scale deployment of ML models across your organization, as well as a practical, coded example illustrating how to implement an MLOps workflow on Databricks. Connect with us: Website: https://databricks.com Facebook: Β Β /Β databricksincΒ Β Twitter: Β Β /Β databricksΒ Β LinkedIn: Β Β /Β data.Β Β . Instagram: Β Β /Β databricksincΒ Β