If you’re new to Machine Learning or are in need of revamping your company’s ML infrastructure, you'll most likely end up in a similar situation to that of buying a car. You start the search for the best cars of 2020, safest or slickest. Jumping from one website to another but always watching your top priorities. 

1. What is the need I’m trying to solve

2. Budget

3. Consider other cars in the class

4. Go for a test drive

5. Pick your car

6. Buy

So when you narrow it down to the last two options, it becomes a balancing game. With ML you also want to make sure the tools you choose provide the most benefits for the best value. In our case, we often get asked what’s the main difference between Datagran and other ML providers like Azure ML pipelines, Databricks, Datarobot, and Alteryx. So this article walks you through the differences between this group of tools for the sake of understanding the main differences between them and facilitate decision making.

Datagran:

Datagran is the Zapier.com for ML. It enables companies to centralize data (from databases, warehouses, data lakes, and business applications), visualize, run ML models, and easily send the output of them to business applications, all without the need to write code.

Key highlights:

1. It’s easy to use and has a friendly and clean UI.
2. Not only data scientists can use it. It’s also made for developers, growth hackers, data analysts, mathematicians, and statisticians. 
3. You can easily send the output to a business application like Intercom or Salesforce without having to build APIs.
4. It is a collaboration tool based on workspaces and projects for teams to work together in real-time.
5. There’s no need for ML Ops. Datagran creates clusters automatically.
6. It’s a data warehouse as well. Data can be stored in its platform as well.
7. It has the power and flexibility of the best visualization tools for technical and non-technical folks.

Main Strengths: 

1. Easy to use without the need for ML Ops.
2. Sending the output of ML Models to Business applications like Intercom, Salesforce, among others.
3. Embedded BI tool.

Who is it for:

1. Companies with Data Scientists and ML Ops that need to test and deploy models fast to then make CORE what works.
2. Companies with one or no Data Scientists in the team and no ML Ops.
3. Companies that want to eliminate silos and merge ML and business units.
4. Companies that are not too hung up on Science and on the contrary need to impact the Business.

Azure ML pipelines:

Azure has done a great job at offering a No-Code ML solution as well as a Coding platform to build ML pipelines. Their No-Code offering is fairly similar to Datagran’s platform, with key differences:

1. In Azure ML pipelines one needs to configure containers. For that reason, there needs to be an understanding of ML Ops (specifically in Azure containers). Even if there are  Azure experts in the team, this will take time to implement.
2. Azure ML pipelines don’t connect to different business applications and neither do they have the option to easily send the output to business applications like Intercom, Salesforce, Facebook, among others.
3. Azure ML pipelines is not a visualization tool. Although they do have visualization charts it is not a tool that will provide use cases of a traditional BI tool. For example, visualizations will show up in the form of Pop-Ups.

Main Strengths: 

1. The user interface is easy to use, and it has many options to prepare data, algorithms, etc.

Who is it for:

1. It provides an excellent solution for companies already working with Azure.
2. It is great for teams with MLOps.

Databricks:

Databricks is a Data Science and Data engineering tool. It’s an open platform to store and manage all of your data and support all of your analytics and AI use cases. Here are some key differences:

1. It is not a No-Code offering. It requires proficiency in Python, SQL, among other coding languages.
2. It also requires Apache Spark knowledge in order to set up several machines to run machine learning models.
3. Databricks doesn’t connect to multiple business applications and neither do they have the option to easily send the output of ML models directly to business applications like Intercom, Salesforce, Facebook, among others.
4. Data Visualization is mainly offered through libraries via Python code.

Main Strengths: 

It is catered specifically for data scientists who love the Science part of it, control and flexibility when working with ML.

Who is it for:

1. It serves well for teams with Data Scientists.
2. It is great for teams with MLOps.
3. It is suitable for companies with some maturity working in ML.

Alteryx:

Alteryx is not a cloud-based platform, and it is mainly catered for users who use PC computers. If you have a Mac you will need to set up a PC environment. Other than that, there are several differences:

1. Unfortunately, their user interface is complex. Although it is a No-Code platform, users need a lot of training and playing around with it to completely understand its tools. That is why many clients arguably drop Alteryx claiming they did not take full advantage of their service.
2. It is on the expensive side of the spectrum. If your company needs to deploy models, then it is required to buy add ons that can cost anywhere north of $70k.
3. Alteryx does not connect to business applications and neither do they have the option to easily send the output to business applications like Intercom, Salesforce, Facebook, among others.
4. There has to be an understanding of ML Ops to choose the right machines to run ML models.
5. It is not a visualization tool. Some folks might argue that it is, but visualizations are raw and more focused on providing metrics for data scientists.

Main Strengths: 

It provides lots of different options in terms of data processing and modeling with the power of no-code enabling team members without coding background to operate it.

Who is it for:

1. It is for companies working mainly with Windows Computers and On-prem.
2. It is made for teams with MLOps individuals.

It is made for teams with Data Scientists

Datarobot:

Datarobot is another tool centered around Data Scientists. The main advantage Datarobot has is it allows for the comparison of different models automatically. 

Key differences:

1. It requires to add ons to deploy ML models.
2. It works mostly with Databricks, meaning it needs infrastructure to be set up. Another option would be to send it to Hadoop but setting that up requires an ML Ops engineer.
3. It does not connect to different business applications and neither do they have the option to easily send the output to them.

Main Strengths: 

Its greatest strength is that it allows data scientists to run and test multiple algorithms at the same time.

Who is it for:

1. It is made for teams with Data Scientists.
2. It works for teams with MLOps.
3. Serves teams with some maturity in ML.

Just like choosing a car, picking the right ML tool requires you to look at everything, from the specs, its performance down to the design. Make sure you weigh down positives and negatives and look at what your team needs in order to make the most out of them. Want to learn more about ML tools and how to build the best ML tech stack for your company? Check out this article.