MLOps Platform for Databricks
Streamline your MLOps and AIOps processes to unlock the full potential of Databricks. Our comprehensive end-to-end solution seamlessly bridges the gap between machine learning, Generative AI development, and operations, empowering you to innovate faster and with greater efficiency.
What exactly does MLOps platform stand for?
Scalable, maintainable, and secure MLOps platform
Our platform offers a comprehensive solution spanning machine learning, Generative AI development, and operational workflows. From automated notebook creation to seamless GitHub integration and robust production environments, Addepto’s framework ensures efficiency, scalability, and reliability throughout the AI lifecycle.
It streamlines 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.
By leveraging Addepto's expertise in AlOps and Databricks' unified analytics platform, organizations can accelerate time-to-value for AI projects while maintaining rigorous standards of security and scalability.
The framework facilitates end-to-end automation of AI workflows, from data preprocessing to model deployment and beyond, ensuring organizations can achieve their AI goals efficiently and effectively.
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.
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?
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.
We are a fast-growing company with
the trust of international corporations
Building and implementing MLOps platform is like charting a voyage. We're not just constructing a ship, but also crafting the compass, designing the maps, and guiding the crew. We navigate the sea of data, driven by the winds of innovation, to reach the shore of actionable insights."
Edwin Lisowski co-founder and CSO – Addepto
Addepto has been ranked #143 on the Deloitte Technology Fast 500 EMEA list, achieving a remarkable revenue growth rate of 1,193%!
We have been recognized as a Top AI consulting company 2024 by TrueFirms
Addepto is one of Europe's FT1000 Fastest Growing Companies 2024 according to FT
We have been listed in the annual MAD Landscape 2023 in the Data & AI Consulting category
Addepto has been listed in the annual Deloitte Technology Fast 50 Central Europe 2023 ranking
Our clients
