Can ML be absorbed by the DBMS?
When we think of the various people and teams making use of ML and DBMS, we can place them on a spectrum based on the composition of their work.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Snowpark for Python: Bringing Efficiency and Governance to Polyglot ML Pipelines
Machine learning (ML), more than any other workflow, has imposed the most stress on modern data architectures. Its success is often contingent on the collaboration of polyglot data teams stitching together SQL- and Python-based pipelines to execute the many steps that take place from data ingestion to ML model inference.
The ClearML Autoscaler
In this video, you'll get an introduction to how the autoscalers work. They can automatically spin up agents on your cloud provider of choice and then spin them back down again when you don't need them anymore!
[MLOPS] the importance of experiment tracking and data tracability
The recording of our talk at Toronto machine learning summit for finance and insurance tech. About the importance of tracking and tracing of experiments and data.
Hyperparameter Optimization (A Real-Life Use Case)
This is part 3 of our 3-part Hyperparameter Optimization series, if you haven’t read the previous 2 parts where we explain ClearML’s approach towards HPO, you can find them here and here. In this blog post, we will focus on applying everything we learned to a “real world” use case.
[MLOPS] From GTC22: A journey towards building a scalable AI serving solution
The recording of a GTC talk given by our very own CEO, Moses Guttmann. He outlines the limitations of current AI serving options and introduces the new ClearML serving beta than can help alleviate these issues!
Improving a day in the life of: Data Scientist - How ClearML is actually used.
ClearML in the real world, without the marketing fluff. Watch along as we show how ClearML integrates with this audio classification use case. Get lots of tips, tricks and inspiration on the use of the experiment manager and remote agents for use in your own day-to-day life as a data scientist. Chapters.
Using Snowpark As Part Of Your Machine Learning Workflow
Teams working on data science initiatives are tasked with deriving new insights from massive amounts of data. To accomplish this, teams work with compute environments that require heavy operational overhead, which means most of their time is spent extracting and processing features for machine learning model training and inference. Pairing Snowflake’s near-unlimited access to data and elastic processing engine with the most popular programming languages can change that, so more time can be spent on model development.
Using Snowflake and Dask for Large-Scale ML Workloads
Many organizations are turning to Snowflake to store their enterprise data, as the company has expanded its ecosystem of data science and machine learning initiatives. Snowflake offers many connectors and drivers for various frameworks to get data out of their cloud warehouse. For machine learning workloads, the most attractive of these options is the Snowflake Connector for Python.