Machine Learning: five platforms on which to train your next model

Improve the way machines understand and interact with us, discover new medical treatments or develop algorithms that allow companies to make better business decisions. These are just some of the possibilities that data scientists are currently exploiting, employing techniques of machine learning

To achieve this, training models that “learn faster,” are easier to get up and running, and can scale, data scientist they need platforms that are not only powerful when it comes to processing huge amounts of “raw” data, but also offer them all the features they need so that, for example, they don’t have to worry too much about the programming language they are using or offer an integrated experience. If we examine the panorama of the offer available at the moment, the ones we propose below are among the most interesting.

AWS SageMaker

No one should be surprised that what is the largest infrastructure as a service (IaaS) company is also one of the organizations that has advanced the most in the field of machine learning. AWS leads the field of public cloud by far and stores the data of more companies than anyone else on its servers, so it has an almost infinite capacity to train all kinds of ML models.

In that space, its most prominent product is AWS SageMaker, a suite made up of different machine learning tools that allows users to manage a wide variety of applications. Among its most interesting tools are the following:

  • Ground Trutha training dataset management function
  • autopilotwhich automatically builds and tunes ML models
  • Data Wranglerwhich speeds up data cleaning and visualization
  • Studiowhere users can browse and manage their ML models
  • Feature Storewhich allows users to upload and share ML features

All this makes AWS SageMaker one of the most complete Machine Learning platforms today, being oriented both to business analysts, data scientists and MLOps engineers.

IBM Watson Studio

That beast of artificial intelligence that goes by the name of IBM Watson is without a doubt one of the most interesting technological projects launched by the American multinational in recent decades.

From being little more than a curiosity capable of beating any human being in a television contest, it has shown enormous potential to revolutionize fields of research such as biochemistry or medicine, helping companies make better decisions based on data. and it is getting us to start “talking” with machines in a natural way. In the field of machine learningIBM Watson Studio helps scientists around the world develop their machine learning models.

One of the highlights of this suite of applications is its accessibility. In this sense, it stands out for functions such as AutoAI, which allows companies to create and deploy ML models without the need for code, as well as the possibility of deploying the model with a single click. This allows even companies that have less experience in this field to start taking advantage of one of the most cutting-edge technologies of the moment.

Google Cloud Vertex AI

If there is a company that has been able to take full advantage of the algorithms of machine learning it’s Google. Products like your Google Assistant they are a small wonder that are integrated into all kinds of products, from smartphones to smart speakers, through wearables and third party products.

It is therefore not surprising that data scientists interested in training their models consider Google products as one of their first options. In this field, the company promotes Vertex AI, a platform in which the multinational unifies its AI services, machine learning, deep learning and TensorFlow services as part of the same offering.

Having such an integrated solution allows users to work with ease in each of the development stages of their machine learning. In addition, and like IBM, Google offers less experienced companies in this field the possibility of working with “point and click” tools in which the need to enter code disappears.

Dataiku DSS

Although, of course, multinationals such as AWS, Google or IBM are the most “accessible” for those who are introduced to the world of machine learning for the first time, this does not mean that they do not have a few competitors: startups that sometimes use original approaches and different, they have a lot to offer data scientists. One of the most interesting is Dataiku.

Founded in 2019 and with clients such as Sephora, General Electric or Unilever, its flagship product is Dtaiku Data Science Studio (DSS), a platform that aims to centralize the operations of machine learningso that the deployment of the algorithms is much faster and more efficient.

To do this, the company offers tools for data collection, preparation and visualization before the initial training phase. Dashboards make it easy to develop new models even for inexperienced work teams, and it has internal collaboration tools that make it easy to share work.

One of the most notable features of Dataiku DSS is its scalability. The platform can easily scale from small teams to large enterprises, helping companies scale their ML operations as they grow.

data bricks

Another startup that has managed to make a name for itself in the world of Machine Learning is Databricks, a company that offers its ML software on the infrastructure of the main public cloud players, such as AWS or Azure.

Although Databricks doesn’t offer code-free programming, it does support multiple programming languages, making it accessible to all types of developers. It also offers APIs that allow different Data Lakes and data silos to be combined in a unified platform, which makes it a very flexible alternative.

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *