Bringing your AI solution in production & ready to scale
From building AI solutions in a production-ready manner to advising on MLOps, ML6 empowers your business by ensuring ML models can generate business value in a sustainable way.
From POC to production
Many companies have data science teams but struggle to move models from research to production. At ML6, we know that machine learning is more than just model creation. MLOps simplifies deploying, monitoring, and managing ML models, bridging the gap between data science and operations. It ensures efficient transitions of models to real-world applications. With the right infrastructure, MLOps makes models deployable, scalable, and maintainable, helping businesses maximize their ML investments.
Advice and guidance towards
value creation with MLOps
MLOps advice & assessments
Along with implementing MLOps in our projects, we offer tailored, strategic, and collaborative advice on implementing MLOps in your organization. Our deep expertise ensures we address immediate issues and build a sustainable framework that evolves with your needs, driving impactful and lasting results.
Our process
A general introduction between the client and ML6 engineers to discuss key issues.
Conducting multiple deep dive sessions with different people to understand the full picture.
Identifying the most important challenges and objectives throughout these sessions.
Gaining a shared understanding of the current MLOps maturity in technology, data, and organization.
Education & workshops
Our educational approach equips your team with the latest MLOps best practices, fostering collaboration and bridging technical and functional gaps. This creates a cohesive strategy and efficient project execution. ML6's commitment to continuous learning ensures successful MLOps implementation.
Our process
Educating the client team on MLOps best practices through parallel sessions.
Conducting workshops to lay the foundation for the remainder of the project.
Introducing a common language to improve understanding between teams.
Aligning on the most pressing challenges through these workshops.
Strategic prioritisation & action plan
Our approach ensures that every step is tailored to your unique needs, leveraging our deep expertise to create customized solutions that drive meaningful results.
Our process
Conducting sessions to develop a prioritized action plan for the project.
Identifying key challenges, like developing a new ML project for data scientists.
Creating a tailored MLOps playbook for streamlined solutions.
Planning MLOps Track
After the MLOps Accelerator track, your team will be well-versed in MLOps best practices and methodologies. You'll also have a solution running on AWS Sagemaker, serving as a
Week 1-8
Architecture review and interviews to determine current MLOps maturity
Workshop to educate the team about MLOps principles and to build a custom roadmap
Co-development of a first solution on Sagemaker applying best practices
Outcome
Assessment of current MLOps maturity, an MLOps roadmap for the future & a first solution on your cloued of choice ready to scale
Success stories
We design AI solutions with MLOps best practices, ensuring real business value and ROI from your AI project
Increasing sales effectiveness at Randstad by leveraging multiple data sources
We developed a scalable AI solution that allows their sales consultants to focus their time on contacting companies who have real potential for Randstad, and enabling them to build conversations based on accurate and relevant information
Enabling MLOps to shorten GfK’s development cycle
ML6 advised and supported GfK on setting up an MLOps way-of-working, enabling GFK to bring insights to production in weeks instead of months, at much lower costs. With the base system set up and the knowledge in their hands, GfK will now expand the system to include more models and expand existing functionality further. ML6 will continue to provide expert support where needed.
Driving the future of microchips with Google Cloud AI and machine learning capabilities
ASML boosts engineering speed, strengthens security, accelerates time to market, and enhances competitive advantage by adding Google Cloud to its on-premises machine learning solutions and collaborating with ML6 to lift the data ingestion pipeline off the ground, and optimise model training pipeline.
10+ years of experience in bringing solutions to production
With over 300 AI & ML use cases across various cloud providers, industries, and client maturity levels, we bring extensive experience in delivering solutions. We adhere to MLOps best practices throughout the project lifecycle, ensuring smooth transitions for internal teams and offering long-term support options.
In case you prefer to fully prepare your own teams to learn and implement ML solutions from the start, we have advisory offerings to fast-track you in this journey.
Our machine learning expertise extends far beyond model development and operations. We recognize that successful AI initiatives can require more than a robust MLOps foundation.
Fondant contributions
we have contributed to the open source community by building a Python framework to build reproducible datasets
Connexion
by maintaining an API framework to build REST APIs in a spec-first way to build your microservices in an API-first fashion.
Nimbus
by maintaining an API framework to build REST APIs in a spec-first way to build your microservices in an API-first fashion.
Blog Posts and Insights
Read our case studies and blog posts to learn more about NLP.
