Machine Learning Development Services

As one of the world’s leading software development companies, DevTeam.Space offers a comprehensive range of machine learning development services, including custom machine learning model development, automated machine learning (AutoML), machine learning as a service (MLaaS), natural language processing, computer vision, and more.

We are a community of over 1200 developers and 62 development teams. All of our developers have extensive experience working in their particular industry segment and we only match your project to the ones with relevant expertise. Hundreds of companies, including some of the biggest names in the United States, have used our development services to build their software products.

Our machine learning developers are skilled in all machine learning technologies used to build innovative ML software solutions. DevTeam.Space offers companies the option to either outsource their entire machine learning application development project to our expert ML development teams or hire ML developers and manage themselves.

Naturally, the specific machine learning frameworks and platforms that you choose for your machine learning application project will depend on your unique development requirements. If you need help with defining the exact skills that you will need, or with anything else, get in touch by submitting your project request here.

To give you an idea of some of the top ML technologies we use every day and why our developers love to use them, here are a few examples.

Top Machine Learning Development Technologies

1. Python
2. Amazon SageMaker
3. IBM Watson
4. TensorFlow
5. Keras
6. Scikit-learn
7. Theano
8. Apache Spark
9. Caffe
10. Deeplearning4j

Python

If you have not heard of Python then it is likely that you have been living on Mars. For the benefit of those that have, Python is an extremely popular programming language used for all kinds of software projects including machine learning development.

Created in 1991 as a hobby project, many people don’t realize that it is actually older than Java (released in 1995). This amazing language is able to define infinite numbers and comes in multiple variants, i.e. CPython, Jython, IronPython, etc.

Our developers love using Python for machine learning development for these main reasons:

1. Python is easy to understand with a simple syntax, helping developers perform complex machine learning model development and training while reducing the risk of coding errors.
2. Python offers several out-of-the-box libraries that allow developers help building machine learning solutions. Some popular Python machine learning libraries include PyTorch, Scikit-learn, Pylearn2, etc., which help to simplify data mining, natural language processing, image processing, etc.
3. Python provides extensive support for application feature engineering. Developers can use Python libraries like Scikit-learn, Category Encoders, etc., to perform essential feature creation tasks like variable transformation, missing value imputation, discretization, etc.
4. Big community support is another huge bonus or Python. If your developer is trying to solve a common task then chances are that there is some help available.

Amazon SageMaker Machine Learning Development

Amazon SageMaker is a comprehensive machine learning development platform from, yes you guessed it, Amazon Web Services.

Our developers really like using Amazon SageMaker for ML development because:

1. AWS SageMaker comes with SageMaker Studio which includes all the necessary tools to prepare training data and then to build, train, and deploy machine learning models.
   It uses only a single web-based interface where developers can undertake all ML development, training, and testing.
2. Developers can create repeatable training workflows and integrate these workflows into CI/CD pipelines for faster development and deployment.
3. AWS SageMaker provides tools like SageMaker Model Monitor for ongoing monitoring of ML models. Developers can track and view metrics, like loss and accuracy, in SageMaker Studio.
4. AWS SageMaker supports distributed model training. Developers can use libraries, like SageMaker data parallel and model parallel, to speed up training processes.

TensorFlow

TensorFlow is an open-source framework from Google that is designed to expedite machine learning model development and visualization.

Here are some of the key TensorFlow features that our developers love:

1. TensorFlow offers developers a range of pre-built models using traditional machine learning algorithms and advanced deep learning methodologies. It also enables developers to build custom models. The end-to-end platform allows developers to build and deploy ML models via tools like core framework, TensorFlow Lite for mobile devices, TensorFlow.js for web browsers, etc.
2. TensorFlow offers several powerful tools for data processing. These tools include standard datasets for initial training and validation, preprocessing layers for data transformations, data pipelines for loading data, etc.
3. TensorFlow supports MLOps and helps implement the best techniques for model tracking, performance monitoring, etc. Developers can use the TFX platform to deploy production pipelines for machine learning models.
4. The precise technology stack, including frameworks, libraries, and the programming languages that you choose to create your machine learning software will depend on your unique project specifications. These will include the specific features you intend to implement, your non-functional requirements, industry regulations, etc.

If you need help with your machine learning project or to find ML experts, you can arrange a call with one of our machine-learning-experienced account managers by submitting your project details via the button below.