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5 Tools to Rapidly Launch Production-Grade AI/ML Apps in 2025🔥🏎️

In 2025, AI is no longer just a buzzword, it's an integral part of how businesses innovate and stay competitive. As AI adoption grows, so does the complexity of managing and integrating AI specific features inside products. From data preprocessing and fine-tuning LLMs to managing resources and deploying models, the challenges of scaling and maintaining robust AI/ML workflows are very real.

But the good news? You got some awesome tools to help🤝

In this article, I’ve covered five powerful tools that are revolutionizing the way AI teams build, scale, and manage production-grade AI apps.

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Let's get started!🚀


1. KitOps - The missing link in your AI pipeline

KitOps Dashboard

KitOps is an open-source MlOps tool that is changing how teams package and version their AI/ML projects. It introduces the concept of "ModelKits" - standardized packaging format that bundle your AI model, datasets, code and configurations into a single standardized unit. This solves one of the biggest challenges in AI development, the complex collaboration process between data scientists, developers and production teams.

ModelKits are built on OCI (Open Container Initiative) standards, making them compatible with most modern development and deployment tools. Unlike regular containers, it lets you grab just the parts you need, like only pulling a specific dataset or piece of code when you need it.

KitOps ModelKit

The recent new released KitOps v1.0.0 brings several powerful features to make AI development even smoother:

📌 Direct Hugging Face Import: Import any Hugging Face model with a simple kit import command and convert it into a ModelKit that can be pushed to standard image registries.

📌 Local Dev Mode: Test your models locally without any extra setup - perfect for when you're working with newer, more efficient large language models that can run on your machine

📌 Enhanced Integration Tools: CLI that works seamlessly for both local and in CI/CD systems. For ease of use in AI/ML workflows, they also designed a Python library that lets you package ModelKits directly from your Jupyter notebook, without the need to switch environments.

Here's how you can import huggingface models to ModelKits directly:

Using the kit import command, you can take any model available on huggingface and convert it into a ModelKit that you can push to image registries such as DockerHub.

For example, when you run kit import microsoft/phi-4, the Kit CLI will:

  1. Download the model microsoft/phi-4 from Hugging Face locally

  2. Generate the configuration needed to package the files in that repository into a ModelKit

  3. Package the repository into a locally-stored ModelKit

Once this is done, you can simply kit push microsoft/phi-4:latest docker.io/my-organization/phi-4:latest and share it with your collaborators.

KitOps not only saves teams valuable time but also ensures secure, scalable, and efficient management of AI/ML projects. Whether you're handling a handful of models or scaling up to larger deployments, it keeps your workflows smooth and your artifacts ready for deployment.This makes it way easier to answer tricky questions like where a model came from, when datasets changed, who approved the model and which models are running in production.

To learn more about KitOps, check out the official documentation.

Join KitOps Community


2. Nebius - The ultimate cloud for AI explorers

Nebius Landing

Nebius is a cloud provider that has all the latest NVIDIA GPUs (H100, H200, and L40S) hooked up with super-fast networks, which means you can train and run your AI models without waiting for a long time to get back the results. They're actually an NVIDIA® Preferred Cloud Provider, so they get early access to the newest GPU tech.

Here's what makes Nebius worth checking out:

📌 Serious GPU Power: You can get thousands of GPUs in one cluster, and they let you manage everything through Kubernetes or Slurm - whatever works best for you.

📌 Everything's Taken Care Of: They handle all the boring stuff like setting up MLflow, PostgreSQL and Spark, so you can focus on your actual work.

📌 Dedicated Architecture Support: Get assistance from solution architects for multi-node deployments and access to detailed tutorials and Terraform recipes for complex setups.

They also recently launched Nebius AI Studio, offering a high-performance inference platform. It offers a wide range of state-of-the-art open-source models, including Llama, Mistral and more. The infrastructure ensures ultra-low latency, crucial for applications like real-time chatbots and content generation.

Nebius Dashboard

Getting started with Nebius is super easy. They have a powerful CLI that lets you manage everything from your terminal. Here's how to get up and running:

  1. Install the CLI:
curl -sSL https://storage.eu-north1.nebius.cloud/cli/install.sh | bash
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  1. Restart the CLI or run exec -l $SHELL to complete the installation.

  2. To make sure that the installation was successful, run:

nebius version
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Once you've got everything installed, the CLI makes it super easy to manage everything. You can start up GPU machines, work with Kubernetes, handle your storage, monitor everything, and automate your workflows - all from the command line. It doesn't matter if you're working on your laptop or pushing code to production, you can do it all with simple commands.

Head over to their website or dive into their CLI documentation to explore all the possibilities.

Check Nebius Studio


3. MLflow - Track your ML experiments for free

MLflow Dashboard

MLflow is a free tool that makes managing your machine learning projects way easier. It helps you keep track of your experiments, save your models, and get them ready for real use. Big companies like Microsoft, Meta, and Toyota are already using it, which shows how reliable it is.

MLflow works with pretty much any ML library you want to use. You can log everything about your experiments - like what settings you used, how well your model performed and where you saved your files. This is beneficial when you're trying different things and need to remember what worked best. Plus, if you're working with a team, everyone can see what everyone else is doing and build on each other's work.

When you're ready to use your model in production, MLflow makes that easier too. It has something called the Model Registry where you can keep different versions of your models, track which ones are being used where and make sure everything's organized.

You can then mark the best model as a production model and use it in production for all your devices / services. This makes managing your models in production a lot easier.

MLflow Model Registry

Let me show you how easy it is to use MLflow with a quick example. Let's say we're building a simple model to classify different types of flowers:

import mlflow
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Load some flower data
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Set up our model settings
params = {
    "solver": "lbfgs",
    "max_iter": 1000,
    "random_state": 8888
}

# Tell MLflow we're starting a new experiment
mlflow.set_experiment("Flower Classification")

# Start tracking our work
with mlflow.start_run():
    # Train the model
    model = LogisticRegression(**params)
    model.fit(X_train, y_train)

    # Save everything to MLflow
    mlflow.log_params(params)  # Save our settings
    mlflow.log_metric("accuracy", model.score(X_test, y_test))  # Save how well it did
    mlflow.sklearn.log_model(model, "flower_model")  # Save the actual model
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That's it! MLflow just saved everything about our model - how we built it, how well it works and the model itself. Later, we can load it back super easily:

# Load the model back whenever we need it
loaded_model = mlflow.pyfunc.load_model("runs:/[run-id]/flower_model")
predictions = loaded_model.predict(X_test)
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Want to get started? Head over to MLflow's website where they've got lots of guides and examples to help you out.

Contribute to MLflow


4. Kubeflow - The Machine Learning Toolkit for Kubernetes

Kubeflow Landing

Kubeflow is a free, open-source platform that makes running machine learning on Kubernetes super simple. It's a complete toolkit that helps you handle every part of your ML work - from building and training models to getting them running in production. It works anywhere you have Kubernetes running, so you can use it on your laptop or in any cloud you want.

Getting started with Kubeflow is pretty straightforward. You have two main options:

  1. Install Individual Parts: If you just need specific features, you can install standalone components like KServe for model serving or Katib for hyperparameter tuning

  2. Install Everything: For the full experience, you can either:

* Use a packaged distribution from providers like AWS, Google Cloud or Azure

* Install directly using Kubeflow Manifests if you're comfortable with Kubernetes
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What's great about Kubeflow:

📌 Everything in One Place: It comes with tools for every part of ML work - like Jupyter notebooks for coding, pipelines for automating workflows and KServe for running your models

📌 Works With Your Favorite Tools: You can use popular ML frameworks like TensorFlow, PyTorch and XGBoost without any extra setup

📌 Easy to Scale: Since it runs on Kubernetes, you can start small on your laptop and easily scale up when you need more power.

Check out the Kubeflow website for detailed guides or jump straight to the installation docs to get started. The introduction guide is also super helpful if you want to understand more about how everything works together.


5. Amazon SageMaker - The AWS companion for AI

SageMaker Overview

Amazon SageMaker is AWS's all-in-one platform that brings together all your data, analytics and AI needs in one place. It's designed to help you build, train and get your machine learning models up and running without having to deal with complicated setups. It works with all your data, whether it's sitting in data lakes, warehouses or coming from other sources.

One of the best things about SageMaker is how it helps you work faster with its unified studio. You can use familiar AWS tools to build models, work with AI, process data and run SQL queries all from the studio itself.

It also comes with Amazon Q Developer, which basically an AI assistant that can help you with your development work. It's super useful when you're trying to find data, build models or set up data pipelines.

SageMaker Studio

Check out the Amazon SageMaker homepage to get started. They've got everything from basic guides to advanced features and you can even try it out with their free tier to see if it's right for you.

Conclusion

Managing AI pipelines doesn't have to be overwhelming. Each tool we've covered serves a unique purpose in streamlining your AI workflows:

  • KitOps solves the packaging and versioning challenges✅

  • Nebius provides the raw computing power you need✅

  • MLflow keeps track of your experiments and models✅

  • Kubeflow handles the Kubernetes orchestration✅

  • SageMaker offers an integrated AWS experience✅

Yo can choose the right combination of tools that fits your specific needs. You might start with MLflow for experiment tracking, KitOps for better versioning and scaling, Nebius for more computing power. Or you might decide that an all-in-one solution like SageMaker is the best fit for your AWS-centric team.


If you found this article useful, share it with your peers and community.

Got other awesome tools in mind? Drop them in the comments!👋

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