MLFlow Services

About MLFlow

MLflow is an open-source platform designed to manage the entire machine learning lifecycle. It addresses key challenges in machine learning workflows, including experiment tracking, model management, and code reproducibility. MLflow is based on two main concepts: runs, which are individual executions of ML code, and experiments, which group multiple runs together.

MLflow supports multiple programming languages, including Python, Java and R. Additionally, its open-source nature allows integration with various ML libraries and platforms, making it a versatile choice for both traditional machine learning and generative AI applications.

MLflow expertise when you need it most.
dida has a proven track record solving critical ML production bottlenecks — contact us to accelerate your workflow.

MLflow implementation challenges

Organizations adopting MLflow often encounter technical hurdles that can slow down their machine learning workflows. Despite MLflow's powerful capabilities, teams frequently struggle with properly configuring and leveraging its features for maximum benefit.

Setting up MLflow's experiment tracking system requires careful configuration to effectively organize model training processes. Many teams find it challenging to establish the right structure for experiment logging and model versioning, leading to inconsistent documentation and difficulty comparing results across iterations.

Collaboration through MLflow presents another common obstacle. While the platform is designed to enable transparency, teams without specialized knowledge often implement it in ways that create new information silos rather than breaking them down. This results in fragmented workflows where the full potential of collaborative model development remains unrealized.

dida services

We provide specialized MLflow expertise to help organizations overcome implementation challenges and maximize the value of their machine learning operations. Our targeted services ensure you can fully leverage MLflow's capabilities without the typical learning curve or technical hurdles.

Set up and maintain mlflow tracking server on a cloud / on-premise infrastructure

Support mlflow integration into the existing model training code

How it works

Our streamlined process ensures you receive expert machine learning support tailored to your specific needs, with a clear path from initial consultation to implementation.

We work with you through these key steps:

1

Book a 30min introductory meeting with one of our Machine Learning Engineers and tell us about your current situation / problem.
2

Based on your situation and requirements, dida will propose a lean plan on how to support your team quickly and efficiently (i.e. 1-3 days of MLFlow support for approx. 1-3K EUR).
3

Within the next three working days, an experienced Machine Learning Engineer with competences in MLFlow will start working on your problem.
4

After successful completion: Decide to expand the service, book a certain capacity per month or continue on your own.

Who is this for

This service is best suited for data science and IT teams that…

are planning on working with MLFlow and want dida to do the initial setup and integration into their projects, or
are already working with MLFlow and desire consultation, development support or evaluations of already developed code.

About dida

dida is a software company from Berlin, Germany, specialized in the development of individual machine learning services. The highly technical team not only trains and optimizes neural networks, it also deploys them in complex production systems and takes care of operations, maintenance and scaling

With the rise of LLMs, dida has extensively worked on MLflow for managing experiments, model tracking, and deployment. Having solved common challenges related to MLflow integration, dida now offers its expertise through a dedicated MLflow support service, helping organizations optimize their machine learning workflows.

Frequently asked questions

Who will be working on our project?

Depending on the desired support volume dida will provide you with 1-2 experienced Machine Learning engineers that have the most experience with this specific tool / technology / framework.
Who will be our main point of contact?

The dida engineer who will lead the project will be the main point of contact so that respective engineers can directly communicate with each other.
How quickly can dida’s team help us address our current challenges with MLFlow?

After signing the contract, dida guarantees to start within the next three working days.
How will we communicate during the project?

We’re open to your preferred choice of communication and organization (Email, Slack, MS Teams, Gitlab / GIthub issues, …)
Does dida work remotely or on-site?

Most of dida’s work is typically performed from our office in Berlin, Germany. Nevertheless we regularly visit our clients for workshops, interim and final presentations or whenever the situation demands it. If on-site work is required, please let us know so that we can arrange it.
How often will we receive updates on the project?

For short term support services you will be updated either daily or after every milestone.
What qualifications and experience does your team have?

dida’s team comprises largely of scientists and engineers with backgrounds in mathematics and physics - many of them holding PhDs from prestigious institutions. The highly specialized team has 8 years of industry experience in implementing machine learning solutions. dida solves algorithmically complex problems and often tackles use cases where less specialized in-house departments previously failed. Amongst its clients are large European organizations such as Deutsche Bahn, Klöckner, Zeiss or APCOA.
Are there any subcontractors involved in the service delivery?

No, all purchased services will be provided by dida employees.

Contact us

Connect with our ML engineering specialists to discuss your MLflow implementation needs.
Our team is ready to provide expert consultation on optimizing your machine learning infrastructure.