MLOps Platform for Databricks


Accelerate the implementation of MLOps processes and increase the capabilities of Databricks.


Business benefits

What exactly does MLOps platform stand for?


MLOps Platform
Why Addepto?
Technology

Scalable, maintainable, and secure MLOps platform


It streamlines machine learning workflows on Databricks, covering includes critical areas such as:

  • data management,
  • model training and validation,
  • drift detection,
  • CI/CD,
  • model serving,
  • infrastructure management,
  • open source readiness.

MLOps platform is a set of tools, technologies, and practices designed to streamline and automate the development, deployment, and management of machine learning models.

It combines elements of machine learning, software engineering, and operations to enable efficient and reliable machine learning operations.


We have a track record of successfully working on MLOps projects for companies across various industries.


You can see some of our successful collaborations on our Clutch profile.

Experienced Professionals

Our team at Addepto is composed of experts with proven market experience. We have a strong portfolio of delivered MLOps projects, showcasing our expertise and capabilities.

Dependable Solutions

Your business needs are our top priority. At Addepto, we strive to provide your company with a reliable and customized solution that meets your requirements. We are committed to delivering on time and ensuring your satisfaction.


We leverage the most recent tech solutions


Tools
Databricks, Python, SQL

Versioning
Databricks notebook versioning for code versioning

Data pre-processing
Notebooks sometimes use Spark feature engineering

Orchestration
UI-built workflows or None

Data Outputs
Materialized into Databricks storage



MLOps Platform and Databricks











Robust Data Management using Databricks pipelines and Delta Lake


Databricks pipelines provide a powerful orchestration framework for managing end-to-end data workflows, while Delta Lake ensures reliable and scalable data storage with features like ACID transactions and data versioning.

Together, they enable organizations to process and manage large volumes of data efficiently, ensuring data integrity and simplifying data governance.

Read more: Delta Lake on Databricks – Reliable Data Lakes at Scale

Data Drift Detection with Databricks Structured Streaming


Databricks Structured Streaming allows for real-time data processing and analysis, making it a valuable tool for detecting data drift.

By continuously monitoring incoming data streams, organizations can identify and respond to changes in data distribution or quality, enabling proactive measures to maintain the accuracy and reliability of machine learning models.

Model Training & Validation using MLflow


MLflow provides a comprehensive platform for managing the end-to-end lifecycle of machine learning models.

It offers functionalities for tracking experiments, managing model versions, and performing model validation.

Data Scientists can leverage MLflow’s tracking and logging capabilities to iterate, compare, and select the best-performing models efficiently.

Model Drift Monitoring with MLlib or third-party libraries


MLlib, a machine learning library in Databricks, or third-party libraries, can be utilized for monitoring model performance and detecting drift.

By comparing model predictions against actual outcomes, organizations can identify potential deviations in model behavior and take necessary actions, such as retraining or fine-tuning models, to maintain model accuracy over time.

CI/CD implementation using Databricks Jobs API and tools like Jenkins or GitHub Actions


Databricks Jobs API, along with popular CI/CD tools like Jenkins or GitHub Actions, allows for the automation of model deployment pipelines.

By integrating these tools, organizations can establish a seamless and standardized process for building, testing, and deploying models, enabling efficient collaboration between Data Scientists and DevOps teams.

Model Serving & Monitoring via MLflow's Model Registry and Databricks' built-in monitoring tools


MLflow’s Model Registry facilitates model serving by providing a centralized repository for deploying and managing models in production.

Combined with Databricks’ built-in monitoring tools, organizations can continuously monitor model performance, track model usage, and detect anomalies or degradation in model quality, ensuring optimal performance and reliability in production environments.

Infrastructure Management through Databricks Terraform provider and Clusters API


Databricks Terraform provider and Clusters API enable streamlined infrastructure management by allowing organizations to provision, configure, and scale Databricks clusters programmatically.

This ensures the availability of the required computational resources for model training, serving, and monitoring while maintaining consistency and reproducibility in the infrastructure setup.

Collaboration & Documentation using Databricks notebooks


Databricks notebooks provide a collaborative environment for Data Scientists to develop, document, and share their work.

With features like version control, markdown support, and inline visualizations, Data Scientists can collaborate effectively, document their workflows, and share best practices, fostering knowledge sharing and improving productivity within the team.


Addepto MLOps Framework


Supercharge your company's performance with the MLOps Framework. Take action today and revolutionize your operations for greater efficiency, innovation, and success.


MLOps Platform Architecture

MLOps Framework

Software

Programming languages


MLOps Platform Architecture


MLOps Platform Architecture – study the diagram below. MLOps Platform Architecture Component Details  
Addepto MLOps Framework


Addepto MLOps Framework – study the diagram below to see the full picture of our framework. Addepto MLOps Framework
Databricks


Databricks – Databricks is a cloud-based platform for big data processing and analysis based on Apache Spark. It provides a collaborative work environment for data scientists, engineers, and business analysts. It offers features such as an interactive workspace, distributed computing, machine learning, and integration with popular big data tools. Databricks is available on the cloud, but there is also a free community edition that provides an environment for individuals and small teams to learn and prototype with Apache Spark. The Community Edition includes a workspace with limited compute resources, a subset of the features available in the full Databricks platform, and access to a subset of community content and resources.
Python


Python – Python is considered the most popular programming language in the Data Science area mostly because of its quite straightforward and easy-to-read syntax. Still, the benefits of using it in building Machine Learning solutions are numerous. This language has a large and active community that develops and maintains a wide range of libraries and frameworks specifically for Machine Learning and Artificial Intelligence, which provide pre-built algorithms and tools for building and training models. Python is a versatile and flexible language; it can be used in scientific computing and web development, which makes it a great choice for building ML models, often requiring a mix of programming, data analysis, and visualization.

Key benefits

Experience a game-changing transformation with the MLOps Platform



Full ML process automation


MLOps platform provides end-to-end automation of machine learning workflows, including data preprocessing, model training, deployment, monitoring, and retraining, ensuring scalability, reproducibility, and efficiency throughout the entire process.


Security & Compliance


By using Databricks’ built-in security features and compliance toolkits, you can be sure that your data is protected from unauthorized access, your data governance policies are enforced, and your organization remains compliant with industry regulations and standards.


Consistency with Databricks


By utilizing environments, Data Scientists can create standardized development environments with specific libraries, dependencies, and configurations and reproduce experiments, ensuring consistent results and reducing potential errors caused by incompatible or inconsistent environments.

Read more: Data Engineering on Databricks


A comprehensive overview of MLOps platforms and their key aspects


What is MLOps?
Why is MLOps important?
What are the key components of an MLOps platform?
How does MLOps address model drift?
Can MLOps be applied to any data platform?
How does MLOps relate to DevOps?


What is MLOps?


MLOps (Machine Learning Operations) refers to the methodologies used to streamline the machine learning lifecycle.

It involves the integration of data engineering, machine learning development, and operations, aiming to ensure the scalability, reliability, and reproducibility of machine learning workflows in production environments.

Why is MLOps important?


MLOps addresses the challenges faced by organizations when deploying and managing machine learning models at scale.

It ensures efficient collaboration between data scientists, data engineers, and IT teams, enabling seamless model deployment, monitoring, and maintenance.

From a business perspective, MLOps helps organizations achieve faster time-to-market, improved model performance, and better governance and compliance.

What are the key components of an MLOps platform?


MLOps platform typically includes components such as:

  • data management tools for data ingestion, transformation, and storage;
  • model training and validation frameworks for developing and testing machine learning models;
  • model deployment and serving capabilities for making models available in production;
  • monitoring and drift detection tools for tracking model performance and detecting deviations;
  • infrastructure management features for provisioning and scaling computational resources.

How does MLOps address model drift?


MLOps addresses model drift (degradation of model performance with time) by implementing monitoring and drift detection mechanisms.

By continuously collecting and analyzing real-time data, organizations can compare model predictions against actual outcomes, detect deviations, and trigger retraining or fine-tuning processes.

Can MLOps be applied to any data platform?


While there might be specific tools and integrations available for certain platforms, the core concepts of MLOps, such as version control, automation, monitoring, and collaboration, can be adapted to fit various machine learning frameworks and platforms.

However, reaching out for specific tools, it is important to choose the ones that align well with the specific infrastructural requirements.

How does MLOps relate to DevOps?


While DevOps focuses on software development and operations, MLOps addresses the unique challenges of deploying and managing machine learning models.

MLOps builds upon the foundations of DevOps, incorporating specialized tools and methodologies to address the specific requirements of machine learning workflows, including data management, model versioning, monitoring, and drift detection.


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