In 2020, data science and machine learning are finally starting to mature as regular business departments and roles.

Sectors like finance, health care, telecom, retail, public and private sectors, etc collect a large amount of data and now more than ever need to reduce costs. Most companies are struggling to keep up with all the data science questions their executives have as well as functional requirements.

Often times data is trapped behind data silos and data governance. This makes what should be a simple question take months to answer.

With all these demands and problems it was unavoidable that a whole host of third-party vendors would develop solutions. 

However, there are many more vendors than one can keep track of so we wanted to help narrow down some great data science tools for 2020. Today we’ll discuss the top 7 data science platforms used in 2020.

 

DataBricks

 

DataBricks, is a cloud-based data engineering platform that makes predictive analysis and real-time apps easy. Based on Apache Spark, DataBricks developed a fully managed service that helps get your data processed faster and more efficiently. The end goal of DataBricks is to be the platform that performs both your data engineering and ML pipelines in one easy workflow. 

This really goes above and beyond the current paradigm of data engineering and machine learning processes that are often quite separate.

In addition, DataBricks also has several other benefits listed below:

1. It is optimized for performance and cost-effectiveness. It adds capabilities at run time to increase performance by 10X,  provides high-speed connectors to Azure storage like Blob Store or Data Lake, and has auto-scaling and auto-termination capabilities. 
2. It increases collaboration by allowing your notebook to be shared for data. Other team members can use the same data for the Machine AI process. Interactive dashboards and notebook environment, monitoring tools, security controls, and power BI for interactive visuals ease the process of Data Scientist.
3. Provides debugging environment and preinstalled analyzing tools using python, R data science stacks. 
4. Total Azure integration to provide diversified VM types like F-series, M-series, and D-series. It works in a secure environment with compliance with your company. 
5. Deploying customer’s subscriptions with VNET, Power BI, JDBC, Azure Active Directory, SQL warehouse, SQL DB, easily loads ecosystem for further analysis, and real-time serving. 
6. Azure Container Service, Accelerating Networking, the latest generation of Hardware system increases DataBrick’s performance.

This is a unified solution for data scientist, data engineers, ML engineers, and data analyst. By using DataBricks large companies have created their success stories.

SageMaker

 

Powered by AWS, it is a fully managed Machine Learning service. Used for authoring, training, and deploying models into production. SageMaker is a one-stop platform for all to data scientists, developers, and machine learning experts. It works on data exploration, cleaning, preprocessing, and uses Jupyter notebook integrated development environments (IDEs).

It provides you the location in S3 for model building training and validations and provides a popular algorithm and platform to build models using your favorite tools. Along with that there are several other benefits SageMaker offers data scientists:

1. AWS allows accessibility anytime anywhere.
2. Provides fully integrated (IDE) for machine learning called SagaMaker Studio. Allows creating and training models by setting up a notebook, moving in between the steps of models. The activities like debugging, model drift detection makes it easy for Data Scientist to work on model training.
3. You can build and collaborate your work by sharing the URL of your creation in SageMaker. This brings the entire team on the same page.
4. It has autoML capacity that is controlled by Data Scientist. It chooses the best model for your data according to the parameters and gives you the control of selecting the model. The autoML feature is great for analysts or data Scientists as steps involved in pipelines are automatically taken care of using autoML. It also provides you with detailed results of algorithms selected for your data and displays its analysis with just a click. It is easy for more naive to user auto saga for their analysis and does not require experience in ML.
5. It supports deep learning frameworks like TensorFlow, PyTorch, Apache MXNet, Chainer, Keras, Gluon, Horovod, Scikit-Learn, and Deep Graph Library.

Dataiku

Dataiku was developed to be the DataOps platform of all platforms. Where data scientist, data engineers, business analyst, and analytical leaders can come together and collaborate. It works as a one stop shop for those looking to develop their DataOps culture as it has features that helps manage, design and deploy machine learning code. Here is a list of Dataiku benefits:

1. Elastic compute: It uses Elastic Compute to dynamically spawned Azure Kubernetes clusters. It provides Python/ R recipe notebooks, in-memory visual ML, visual and code spark recipes, and notebook.
2. Power BI and Interactive Visual Statistics: Visualization and interactive statistical charts with drag and drop features make it easy to choose different chart sets. Exports DSS data to Power BI 
3. Access any-time, any-where: Data access anytime anywhere according to the role now using SharePoint and OndeDrive.
4. Quality and preparation tools: Displays data for inspections, identifying duplicates, ensuring precision, along with its statistics easily done. Preparation tools and preinstalled visual processors for filtering, searching and code-free wrangling allow users to make their custom choices.

Saturn Cloud

Very few data science platforms are designed while keeping data scientist in the mind. Saturn Cloud is one of them. Founded by Hugo Shi and Sebastian Metti, Saturn Cloud is working towards making the Data Scientist team self-sufficient by providing them the easy to use Jupyter Notebooks and DataOps features.

Saturn Clouds Benefits:

1. Saturn Cloud focuses on end-to-end data science workloads that are needed for the data science to perform. It is based on Python and allows for easy management of Jupyter Notebooks, and Dask jobs.
2. Saturn Cloud is a SaaS platform with high-leverage automation tools. With one click you can create notebooks to write your Python programs, right from enriching data, to structuring modeling and productions SC takes care of all the tasks for Data Scientist. 
3. It works on AWS to provide secure and scalable infrastructure. Scientists can run their data science and machine learning workloads. It provides EC2 service with resizable computing capacity.  

DataRobot

DataRobot, founded in 2012 in Boston by Jeremy Achin and Tom de GodoyIt, is built to focus development and end-to-end deployment of AI and ML.

1. It makes use of open source libraries found in R, Python, H2O, Spark, and TensorFlow, Builds models to find the best-suited one for your data.
2. Sets best working practices by automating steps like partitioning data, performing training and validation of data, testing, and ranking models. 
3. It provides auto-modeling tools to with fine-tuned models. It also works for all the roles and levels defined in an organization.
4. It leads to transparency in understanding your models that infers your predictions and to start with DataRobot, they have CFDS ( customer-facing data scientists ) team of DataRobot to train you with DataRobot for immediate delivery.
5. Along with analyzing and creating models it also automates and democratizes the ML models and allows working with time-series data.

Domino Data Lab

If you work with DevOps in AI and ML then having Domino as a Data Science tool is beneficial. It automates the DevOps process for you. It offers unified solutions to develop, validate monitor, and deliver models. It provides flexibility to choose the tools and language for data modeling.

1. It works on Spark and Hadoop and other cloud-based and distributed platforms. 
2. It automated the workloads by configuring docker containers that enable to the reusable environment.
3. Utilizes Kubernetes to scale or unscale horizontally and vertically according to workload. Boosts computing requirements for the development of models.
4. Allows interactive workspace using web-based solutions like Zeppelin, H2O, SAS, RStudio, or Jupyter
5. Simultaneously, train multiple training sets by modeling and comparing them.

Algorithmia

Algorithmia is another great DataOps tool that makes it easy to deploy, develop and scale your future ML models. Here are a few points were noting:

1. It centralizes the model and production in a cohesive workflow, that allows Data Scientist to envision the algorithm from different technology stack as a production-ready code.
2. Each version of the custom algorithm is saved and accessible with unique, versioned, low-latency, and reliable REST API endpoint that scales and interconnects with the ETL pipeline or interconnected-device.
3. With all security measures, the software runs on a Virtual Private cloud that enables the data scientist to build their algorithms quickly.
4. Every version of the model creates are cataloged and can be searched by everyone.
5. A stack of cloud-agnostic serverless AI layer, Java, Scala, Python, Ruby, NodeJS, Rust, or R and can combine microservices in multiple languages
6. Scales automatically as per demands and provide modifiable security to comply with your regulatory requirements.
7. It can horizontally develop and scale deep learning models and allows cherry-picking of algorithms from the public market place.

TakeAway:

Data Science platforms are amazing to work with! They quickly set up the required infrastructure for AI and ML. To select the best platform for your app, check out the features and pricing options.

If you would like to read more about data science or cloud computing, then please click below.

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