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ml infrastructure

When starting a new project, choosing the right machine learning infrastructure is an important part of the process. Whether for research, development, or production applications, having a solid pipeline of tools to acquire and manage data is everything. The best way to make sure your final model is strong isn't by using the latest tools in research, it's by having a pipeline that works for you and your team.

As you start building your machine learning models for your business, you might have some questions about the infrastructure needed to run these models, especially when it comes to training. Where to deploy the machine learning models? What should the architecture look like? Or if you'll use a centralized approach or a distributed model? 

When you choose the right infrastructure, everything else will fall into place. Here's a detailed guide on how to do it: 

  • Consider The Operational Requirements

When considering the right infrastructure for your machine learning (ML) needs, it's important to understand what operations you're looking to run. If you don't understand how it would fit your operational requirements, you can consult companies like cnvrg.io and others who have specialized in building and deploying ML models on any infrastructure. The most common ML workloads are training, inference, and data processing.

  • Training: Training is a process in which a machine learning model ‘learns’ from a dataset by finding patterns in the data. The model then uses those patterns to make predictions or recommendations about unknown data. Once a model is trained, it can be used for inference.
  • Inference: Inference is applying a trained model to new data to make predictions or recommendations.
  • Data Processing: Data processing is converting raw data into a format that can be used for training and inference.

Once you understand the operational requirements needed in your ML models and what the models need to deliver, you can easily choose an infrastructure that will support it.

  • Consider The Programming Language 

The most popular programming languages in machine learning are Python, R, and Scala. They have their strengths and weaknesses concerning their use cases. For example, if you build an image recognition model, Python is likely to be your best bet because it has several libraries like sci-kit-learn that make it easy to build complex models on large datasets. These libraries can help you work on, store, and manipulate unstructured data.

But if you want to build a model for natural language processing (NLP), R would be a better choice because it supports regular expressions, which can be used for NLP tasks such as part-of-speech tagging or parsing sentences into words. Scala is also a great choice for NLP tasks, but it doesn’t have built-in support for regular expressions out-of-the-box like R does, so you might need to do some additional setup work here before you can start using them.

  • Look At Your Data

Another step to choosing the right infrastructure for your machine learning project is to look at your data. Take an inventory of what you have, where it comes from, how much there is, and, most importantly, how it's structured.

Start by looking at what you have. Look at the sources of all the data that you’ve gathered. You might be getting data from different sources. Do these sources require any special consideration? Are they streaming in real-time, or are they updated less frequently? How often do they update, and how much data are they providing? What formats are they in? Are the contents of some of these sources more important than others? Does this change over time? Think about how these things can affect the quality of your end product.

Next, consider your data types. Is it mostly text, images, audio, video, numerical measurements, or something else entirely? What kinds of outputs does your model need to produce? Are these outputs likely to change over time? The types of data sources you use, the types of questions you’re looking to answer, and the size of your data set will influence which tools you need to use. 

  • Estimation Of Costs

When choosing an ML infrastructure, it's always important to estimate the costs you'll incur. This is important as running out of finance when developing and deploying ML models could stall everything. Here are the things you need to consider: 

  • Computer resources: How much memory and CPU do you need? This is directly related to the scale of your datasets. For example, if you're training an image classifier on millions of images, you'll need more resources than someone training a simple linear regression model on a few thousand records.
  • Time to train: How long does it take to train your models? This is also related to dataset size and depends on your models' complexity. If your models are small, you might be able to use less powerful machines and wait longer for them to train.


Machine learning is an incredibly powerful and widely applicable technology that has a huge number of applications. The correct models will increase efficiency and productivity in your business. So, you need to use the tips discussed in this article to pick the right infrastructure first. This will lay a proper foundation to build and deploy your models.