Compare ZenML vs
Databricks

Streamline Your ML Workflows

Discover how ZenML offers a flexible, vendor-neutral alternative to Databricks for orchestrating your machine learning workflows. While Databricks provides a robust, Spark-centric ecosystem for big data processing and ML, ZenML delivers a lightweight, adaptable framework that seamlessly integrates with various tools and platforms. Compare ZenML's intuitive pipeline management and multi-cloud flexibility against Databricks' unified analytics platform. Learn how ZenML can accelerate your ML initiatives with reduced complexity and vendor lock-in, while still offering the scalability and collaboration features you need for enterprise-grade machine learning operations.
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ZenML
vs
Databricks

Versatile Tool Integration

  • ZenML seamlessly integrates with a wide range of ML tools and platforms, including Databricks if desired.
  • Easily switch between different compute environments without changing your core pipeline code.
  • Avoid vendor lock-in and maintain flexibility in your ML infrastructure choices.
    • Dashboard mockup
    Dashboard mockup

    Simplified MLOps for All Team Sizes

  • ZenML offers a more lightweight and accessible approach to MLOps compared to Databricks' enterprise-focused platform.
  • Get started quickly with minimal setup, ideal for small teams and projects that don't require Databricks' full ecosystem.
  • Scale your MLOps practices gradually as your needs grow, without committing to a complex, all-in-one platform from the start.

    • Cost-Effective and Open-Source

  • ZenML is open-source and free to use, with optional paid support and enterprise features.
  • Avoid the significant costs associated with Databricks' platform, especially for smaller teams or projects.
  • Leverage your existing infrastructure and tools, potentially reducing overall MLOps costs compared to adopting Databricks' ecosystem.

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    Feature-by-feature comparison

    Explore in Detail What Makes ZenML Unique

    Feature
    ZenML
    ZenML
    Databricks
    Databricks
    Workflow Orchestration Provides a flexible and portable orchestration layer for ML workflows across various environments Offers robust orchestration within the Databricks ecosystem, optimized for Spark-based workflows
    Integration Flexibility Seamlessly integrates with a wide range of MLOps tools and cloud services Primarily focuses on integration within the Databricks ecosystem and select partner tools
    Vendor Lock-In Enables easy migration between different tools and cloud providers Tightly coupled with Databricks' ecosystem, which may lead to vendor lock-in
    Setup Complexity Lightweight setup with minimal infrastructure requirements More complex setup, often requiring dedicated Databricks clusters and workspace configuration
    Learning Curve Gentle learning curve with familiar Python-based pipeline definitions Steeper learning curve, especially for teams new to Spark and the Databricks ecosystem
    Scalability Scalable architecture that can grow with your needs, leveraging various compute backends Highly scalable, particularly for big data processing with built-in Spark capabilities
    Cost Model Open-source core with optional paid features, allowing for cost-effective scaling Subscription-based pricing model, which can be costly for smaller teams or projects
    Data Processing Flexible data processing capabilities, integrating with various data tools and frameworks Optimized for big data processing with native Apache Spark integration
    Collaborative Development Supports collaboration through version control and pipeline sharing Offers collaborative notebooks and workspace management for team development
    ML Framework Support Supports a wide range of ML frameworks and libraries Supports popular ML frameworks, with optimizations for distributed training on Spark
    Code comparison
    ZenML and
    Databricks
    side by side
    ZenML
    ZenML
    from zenml import pipeline, step
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import numpy as np

@step
def ingest_data():
    return pd.read_csv("data/dataset.csv")

@step
def train_model(df):
    X, y = df.drop("target", axis=1), df["target"]
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)
    return model

@step
def evaluate_model(model, df):
    X, y = df.drop("target", axis=1), df["target"]
    predictions = model.predict(X)
    return np.sqrt(mean_squared_error(y, predictions))

@pipeline
def ml_pipeline():
    df = ingest_data()
    model = train_model(df)
    rmse = evaluate_model(model, df)
    print(f"RMSE: {rmse}")

ml_pipeline()
    Databricks
    Databricks
    from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.evaluation import RegressionEvaluator

spark = SparkSession.builder.appName("ML Pipeline").getOrCreate()

df = spark.read.csv("/dbfs/mnt/data/dataset.csv", header=True, inferSchema=True)

assembler = VectorAssembler(inputCols=df.columns[:-1], outputCol="features")
rf = RandomForestRegressor(featuresCol="features", labelCol="target", numTrees=100)
pipeline = Pipeline(stages=[assembler, rf])

model = pipeline.fit(df)
predictions = model.transform(df)

evaluator = RegressionEvaluator(labelCol="target", predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)

print(f"RMSE: {rmse}")

    Flexibility and Vendor Independence

    ZenML offers a vendor-neutral approach, allowing you to integrate with various tools and cloud providers, while Databricks is primarily focused on its own ecosystem.

    Lightweight and Easy Setup

    ZenML provides a more lightweight solution with minimal infrastructure requirements, making it easier to set up and start using compared to Databricks' more complex environment.

    Cost-Effective for Small to Medium Projects

    With its open-source core and optional paid features, ZenML offers a more cost-effective solution for smaller teams and projects, unlike Databricks' subscription-based model which can be costly for limited use cases.

    Gentle Learning Curve

    ZenML's familiar Python-based pipeline definitions and consistent interface across platforms make it easier to learn and use, especially for teams without Spark expertise, compared to Databricks' steeper learning curve.

    Portability and Multi-Cloud Support

    ZenML ensures workflow portability across different environments and supports easy migration between cloud providers, offering more flexibility than Databricks' primarily Spark and AWS-focused environment.

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    Expand Your Knowledge

    Broaden Your MLOps Understanding with ZenML

    Navigating the MLOps Galaxy: ZenML meets Neptune for advanced Experiment Tracking

    The combination of ZenML and Neptune can streamline machine learning workflows and provide unprecedented visibility into experiments. ZenML is an extensible framework for creating production-ready pipelines, while Neptune is a metadata store for MLOps. When combined, these tools offer a robust solution for managing the entire ML lifecycle, from experimentation to production. The combination of these tools can significantly accelerate the development process, especially when working with complex tasks like language model fine-tuning. This integration offers the ability to focus more on innovating and less on managing the intricacies of your ML pipelines.

    A case for declarative configurations for ML training

    Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.

    Streamlining Model Deployment with ZenML and BentoML

    This blog post discusses the integration of ZenML and BentoML in machine learning workflows, highlighting their synergy that simplifies and streamlines model deployment. ZenML is an open-source MLOps framework designed to create portable, production-ready pipelines, while BentoML is an open-source framework for machine learning model serving. When combined, these tools allow data scientists and ML engineers to streamline their workflows, focusing on building better models rather than managing deployment infrastructure. The combination offers several advantages, including simplified model packaging, local and container-based deployment, automatic versioning and tracking, cloud readiness, standardized deployment workflow, and framework-agnostic serving.

    Experience the ZenML Difference: Book Your Customized Demo

    Experience the ZenML Advantage: Start Your Flexible MLOps Journey

    • Explore how ZenML's vendor-neutral approach can simplify your ML workflows
    • Discover the ease of setting up and scaling your MLOps practices with ZenML
    • Learn how to build portable, cost-effective ML pipelines that grow with your needs
    See ZenML's superior model orchestration in action
    Discover how ZenML offers more with your existing ML tools
    Find out why data security with ZenML outshines the rest
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