Once upon a time, Data Science was something that was restricted only to the tech giants, but in this fast-growing world, it is slowly becoming an integral part of businesses as big companies start to integrate these techniques into their business models. In this blog, we go through what a Data Science Platform is, the different types of platforms, and how they can be used to bring value to the business so that the big corporates can stay in the race to conquer the market of the future.
What is a Data Science Platform?
A data science platform is software that includes a variety of technologies for machine learning, data science, and other advanced analytics projects.
Typically, data science projects involve using an abundance of ls (eg. incorrect, incomplete, inaccurate, irrelevant parts) to be identified in each step of the data analysis, cleaning, and modeling process. That is why it is important to have a centralized and unified platform so data science teams can collaborate on those projects. A single, integrated platform where a whole team of data scientists works together can lead to better results and, therefore, greater business value.
These platforms offer collaborative environments, helping organizations to incorporate data-driven decisions into operational and customer-friendly systems to enhance business outcomes.
Types of Data Science Platforms
The data science platform landscape can be overwhelming. There are dozens of products describing themselves using similar language despite addressing different problems for different types of users.
We can divide the types of Data Science Platforms into 3 parts. They are:
1. Automation Tools
These tools help engineers to automate repetitive tasks in data science, including training models, selecting algorithms, and more. These solutions are targeted primarily at non-expert coders or data scientists interested in shortcutting tedious steps and repetitive steps. They help spread data science work by getting non-expert data scientists into the model-building process, offering drag-and-drop interfaces.
2. Proprietary (Often GUI-driven) Data Science Platforms
Proprietary tools support a lot of use cases, including data science and model building. They provide both drag-and-drop and code interfaces and have a stronghold in big companies and may even offer unique capabilities or algorithms.
While these solutions offer a great breadth of functionality, users must leverage proprietary user interfaces or programming languages to express their logic.
3. Code-first Data Science Platforms
Code-first Data Science Platforms target data scientists and coders who use statistical programming languages and spend their days in IDEs like Jupyter and Colab, leveraging a mix of open-source and Machine Learning packages and tools to develop sophisticated models. These data scientists require the flexibility to use a constantly evolving software and hardware stack to optimize each step of their model lifecycle. These code-first data science platforms orchestrate the necessary infrastructure to accelerate power users’ workflows and create a system of record for organizations with hundreds or thousands of models.
Top Data Science Platforms
1. Anaconda Data Science Platform
Anaconda offers the easiest way to perform Python/R data science and machine learning on a single machine. You can work with thousands of open-source packages and libraries on it. Navigators can search packages on an anaconda cloud or local repository, install them and update them as required.
Features of Anaconda
- It is free and open source with more than 1500 Python/R data science packages
- It simplifies package management and working with tools and libraries
- It has tools to easily collect data from sources using machine learning and AI
- It creates a simplified environment that is easily manageable for deploying any project
- Build and train ML and deep learning models with scikit-learn, TensorFlow, Pytorch, etc.
- Easily manageable for deploying any project
- Good community support
- Manage libraries, dependencies, and environments with Conda
- Build and train ML and deep learning models with inbuilt libraries
- It can be a bit bulky sometimes, slowing down and lagging while you are working on your code, especially when you are on a low-end system.
- Lots of packages and environments can complicate simple stuff sometimes.
- Gets slow when working on heavy Deep Learning Algorithms
2. H2o.ai Platform
H2O.ai is an Open-source and freely distributed platform. It is working to make AI and ML easier. H2O is popular among novice and expert data scientists. H2O.ai Machine learning suite.
Features of H2o.ai
- It works across a variety of data sources, including HDFS, Amazon S3, and more. It can be deployed everywhere in different clouds
- Driverless AI is optimized to take advantage of GPU acceleration to achieve up to 40X speedups for automatic machine learning.
- Feature engineering is the secret weapon that advanced data scientists use to extract the most accurate results from algorithms, and it employs a library of algorithms and feature transformations to automatically engineer new, high-value features for a given dataset.
- It provides an AutoDoc for each experiment, relieving the user from the time-consuming task of documenting and summarizing their workflow used when building machine learning models.
- Driverless AI provides robust interpretability of machine learning models to explain modeling results in a human-readable format.
- With the system’s powerful GPU acceleration support, H2O Driverless AI is a quick performing automation platform that provides
- Employing H2O Driverless AI service would allow the automation of a big chunk of workflows, which would mean reduced expenses for the company and speed up the process of the work.
- This platform has features referred to as interpretability tools that give users the ability to acquire, model in English, and debug reason codes.
- It is a user-friendly automation platform compared to many of the other solutions in the market
- Lots of bugs in the codebase.
- It is not very scalable compared to other platforms
- Lack of proper documentation
- H2O.AI can take up lots of memory
3. Data Science on Google cloud platform
Google Cloud is one of the best data science learning platforms. It offers all of the tools data scientists need to unlock value from data. From data engineering to ML engineering, TensorFlow to PyTorch, GPUs to TPUs, data science on Google Cloud helps your business run faster, smarter, and at planet scale.
Features of Google cloud platform
The following are some key features of Google Cloud Platform:
- An automated environment with web-based tools. Therefore, no human intervention is required to access the resources.
- The resources and the information can be accessed from anywhere.
- Google has its own network that enables users to have more control over GCP functions for smooth performance and increased efficiency over the network.
- Users are getting a more scalable platform over the private network and have more scalability
- There is a high number of security professionals working at Google to give high security to its customers
- The availability of more resources whenever required.
- The easy-to-pay feature enables users to pay only for consumed services.
- Google enables users to get Google Cloud hosting at the cheapest rates. The hosting plans are not only cheaper than other hosting platforms but also offer better features than others. GCP provides a pay-as-you-go option to the users where users can pay separately only for the services and resources they want to use.
- Once the account is configured on GCP, it can be accessed from anywhere. That means that the user can use GCP across different devices from different places. It is possible because Google provides web-based applications that allow users to have complete access to GCP.
- GCP has relatively few global data centers across the World compared to other cloud services
- There are very few customization options available in GCP products such as BigQuery, Spanner, and Datastore.
- GCP Application Engine is restricted only to languages like Java, Python, PHP, and Google Go only.
- GCP’s support is not the strongest when it involves handling customer issues plus the support fees are quite expensive.
4. Data Science on AWS
Amazon Web Services (AWS) provides a dizzying array of cloud services, from the well-known Elastic Compute Cloud (EC2) and Simple Storage Service (S3) to platform as a service (PaaS) offering covering almost every aspect of modern computing.
It specifically provides a mature big data architecture with services covering the entire data processing pipeline — from ingestion through treatment and pre-processing, ETL, querying, and analysis to visualization and dashboarding. It lets you manage big data seamlessly and effortlessly without having to set up complex infrastructure or deploy software solutions like Spark, which makes it one of the best and most used platforms globally.
Features of AWS
- Flexibility is one of the most popular key features of AWS. The flexibility of AWS is a great asset for organizations to deliver the product with updated technology in time and overall enhance productivity. Scalability in AWS has the ability to scale the computing resources up or down when demand increases or decreases respectively.
- AWS provides a scalable cloud-computing platform that provides customers with end-to-end security and end-to-end privacy.
- AWS incorporates security into its services and also maintains confidentiality, integrity, and availability of your data which is of the utmost importance.
- A very user-friendly interface that provides access to a wide number of applications and services.
- Expanded into over 70 more services. This includes database, software, mobile, analytics, and networking.
- Huge, unlimited bandwidth for highly trafficked websites
- Another major benefit of AWS is its flexibility, with basically no limit to how much you can use.
- AWS has quite complicated billing, which can be confusing for beginners
- Another downgrade is Amazon’s EC2 has limits like limiting resources by region. So, where you are located, or your region can determine just how many resources you will have access to
- Limit spending on resources for new users
- Common Cloud Computing Problems like backup protection, risk of data leakage, privacy issues, security, downtime, and limited control
Data Science Platforms Features
Open-source Data Science Platforms will have many of these features.
Integrate multiple data science tools
The most important feature of these platforms is integrating all the tools in one place so that all the work like data cleaning, analysis, modeling, and deployment can be done with ease, and also this will fasten the process.
Centralize data resources
Data Science Platforms have a unified location for all work.
Handle very large amounts of structured and unstructured data
They help in the smooth handling of large GBs of data
Data mining, Data access, gathering, and preparation
The platforms provide tools to fasten cleaning and data analysis.
No code options
Even people with no coding knowledge can work on these platforms with the help of no-code tools
They have integrated dashboards to help visualize the graphs and results for the clients.
Multiple programming language support
Data Science Notebooks come with multiple language support like Python, R, etc
Model development and iteration
These platforms come with inbuilt tools for model building and training, which does the work in a few lines of code.
Machine Learning Deep learning
It has inbuilt advanced ML and DL libraries like Keras, Pytorch, etc., which makes coding very simple and faster
Automated documentation and explainers
It comes with automated documentation and code helpers to guide the engineers in the further steps of modeling.
Since a lot of people collaborate together, good security services are a must on these platforms.
Cloud-based, on-premises, hybrid installations
Data Science platforms have cloud-based services infused like google colab for efficient collaboration on cloud without wasting local resources.
Why Does Your Company Need a Data Science Platform?
Data Science has become the need of the hour. Over the last decade, it has been rapidly progressing both as a technology and has taken over all sectors of the world. However, there is a need for a next step for the companies to take their products to an advanced level in data science platforms that can be integrated directly into their models.
Owning a Data Science platform and integrating it into their business model is becoming increasingly important for the big business sectors to stay ahead. The biggest challenges companies face in leveraging data science are the relatively small number of trained data scientists and the historical ad hoc, manual approach involved in the work. For example, data scientists have traditionally conducted data exploration and model training and optimization using their own tools, on their own computers, with relatively little tracking, consistency, or collaboration and reuse of code.
The steps involved in building optimal ML models are quite time-consuming, especially when done manually. Pressure to produce models quickly can thus short-circuit the optimization work, resulting in less-accurate models. This is where data science platforms come in. They supply the fit-and-finished end-to-end solutions needed to provide the required efficiency gains.
When evaluating vendor offerings, decision-makers should consider their company needs, goals, budget, and employee skill sets.
A company needs to evaluate if it really needs a Data Science Platform on factors like:
- Collaboration – If the team if the number of engineers is large, they need a centralized platform
- Configuration & workload – To have the configuration and environment readily set up and available for the team to start the work.
- Automation / workflow loads – It is really needed to automate the workflow so that the same steps don’t need to be manually repeated again and again.
Need for a Data Science Platform
1. To Enable Better Teamwork with Data Scientists
If the data scientists are solving the same problem in several ways and working separately, productivity will decrease as it will not deliver effective value to the organization.
If the whole team of data scientists works on a unified and single platform, where they are provided with the required tools, it ensures that all the contributions of the data scientists, i.e., data models, data visualizations, and code libraries, exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects,
2. Help Minimalize Engineering Effort
With data science platforms, data scientists get help in moving analytical models into production. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts.
It will decrease the additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine, then the data scientist will require the efforts of a software engineer for testing, refining, and integrating the data model before the users start seeing the product recommendations on the basis of their behavior
3. Help to Offload a Number of Low Value Tasks
Data scientists can cut off the burden of menial tasks such as reproducing past results and configuring new environments for non-technical users for every project, as these tasks can be efficiently handled with data science platforms.
4. Facilitate Faster Research and Experimentation
Whenever there is a new person in the data science team, the employee can start working exactly from the point where the old employee left, as it is easier to restore the work through the unified platform. Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on.
What Makes a Data Science Platform Valuable?
When evaluating how good a platform is, the key factor is the outputs and business value it provides to the organization because that is the main objective of it in the first place.
The stage is made to fulfill the needs of the business, and anything less would be a disappointment. The method for assessing it hence is to map the stage against the objectives of the organization to check whether it fits to such an extent that it can assist the organization with accomplishing those objectives.
1. How to ensure that data science project delivers profits to business
Ensuring value to the business is the most important aspect because the platform is of no great use if it completes the tasks but, in the process, utilizes lots of resources and results in losses to the finances.
2. How to deliver a project efficiently
Delivering a project efficiently with smooth operations and services to the clients by keeping regular meetings to have a mutual understanding between organization and clients regarding each stage of the process
3. Deliver Actual Business Value by Being Outcome-Focused
Making sure the main priority is it brings value to the business and clients with less resources utilized so that it becomes more economically feasible. In this way, the project will attract even more customers.
Building a great machine learning model is of no use. If the output from that model is never used by anyone, then the model is not delivering value and bringing profits to the company.
To ensure a project is aligned with stakeholders’ needs, we should try to understand the problems/opportunities a business/organization is facing and the metrics they are trying to improve. These metrics should form the backbone of the project. To identify your client’s needs, have frequent meetings with them.
Data Science platforms also have inbuilt MLOps functionalities. MLOps is a system of processes for the end-to-end data science lifecycle at scale. It provides a venue for data scientists, engineers, and other IT professionals, to efficiently work together with enabling technology on the development, deployment, monitoring, and ongoing management of machine learning (ML) models.
The benefits of MLOps are rapid deployment of multiple models, accelerated time-to-value by building and deploying models faster, increased productivity due to improved cooperation and the reuse of models. With enterprise MLOps, everything from data analysis and data processing to scalability and tracking can be made more efficient.
Before beginning any machine learning work, measure the core business metrics so that they can then be tracked following the project to see whether they improve. This will then allow you to measure whether the project has helped improve these metrics.
These tips should help bridge the gap between just completing a data science project vs delivering profits and value from a data science project.
Data Science Platforms: Should You Build or Buy
A big dilemma for many organizations is whether they should buy or build their own data science platform. Buying the platform is the logical choice for most. And the reason for that is for the vast majority of organizations, the competitive differentiator is not the platform, but the entire organizational capability encompassing many different technologies, and business processes. In a few select situations, the platform makes the difference. These organizations have highly specialized resources and people (e.g., Amazon, Uber), good software, and skilled Data Scientists at their disposal, so they can afford to build an advanced data science platform for their organization.
Most of the companies that buy platforms usually fail because they underestimate the heavy resources needed to build them. Those who have purchased a platform are operationalizing data science at scale.
Variables like the t cost of ownership, managing and operating a data science platform need to be carefully studied. Many organizations underestimate the total cost of ownership in the building approach and when they waste opportunities building a data science platform, they have no choice but to divest from other projects which can seriously hurt the organization’s revenue.
Best Enterprise Data Science Platforms
Some of the most popular platforms used by large enterprises:
1. Databricks Lakehouse Platform
Databricks Lakehouse Platform, a data science platform and Apache Spark cluster manager were founded by Databricks, which is based in San Francisco. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines and data modeling.
MATLAB is another data science platform used by large enterprises designed specifically for data scientists to analyze and design Machine Learning products that transform our world. MATLAB is operated by the MATLAB language, a matrix-based language allowing faster computational mathematics.
3. Oracle Machine Learning
Oracle Machine Learning combines the classic Oracle database with Oracle Data Miner and SQL as well as adds the R programming language functionality for data science tasks, thereby providing a complete predictive analytics suite.
Wolfram’s flagship product Mathematica is a modern technical computing application that features a flexible symbolic coding language and a wide range of graphing, data visualization and diagram capabilities.
Best Platforms to Learn Data Science
These platforms and the data science and machine learning courses they offer are suitable for all, from freshers to experienced professionals. We hope that you will find the right course, and patiently work on finishing it. Here is the list:
The bible of data scientists. It is a subsidiary of Google LLC and serves as an online community of data scientists and machine learning practitioners. Kaggle provides data science enthusiasts with a platform to interact and compete in solving real-life problems while upskilling themselves. Kaggle also works towards finding and publishing data sets and building models in a web-based data-science environment. The platform is more towards offering online micro-courses that can be helpful for those who look forward to quickly upskilling themselves.
2. Microsoft Learn
Microsoft has a lot of great data science certifications and recently announced the release of three job-role-based Azure data and AI certifications, focused on validating your skills in advanced field of ML and AI technologies that are changing how organizations think about and leverage data in their journey to automate workflows. With the ever-increasing need of data scientists, these courses will elevate your status:
Microsoft Certified: Azure AI Engineer Associate
Learn to design scalable systems with help of Azure and AI to modernize business operations, from revolutionizing AI integrated solutions through cognitive services, machine learning and data analysis
Microsoft Certified: Azure Data Engineer Associate
This certification will prove you have the skills to ensure that you know how to design and implement cloud-based systems and design for reliability, performance, and scale.
Microsoft Certified: Azure Data Scientist Associate
Demonstrate that you have the skills to unlock insights, assess advanced statistics and machine learning to keep your company a step ahead of the competition.
Pursuing a data and AI certification with Microsoft helps showcase your skills to both current and potential employers, proving that you have the skills to help them to implement their intelligent cloud and intelligent edge strategies.
3. MIT Open CourseWare
MIT OpenCourseWare (MIT OCW) is an initiative by the Massachusetts Institute of Technology (MIT). The aim of this initiative is to provide all of the educational materials from its undergraduate and graduate-level courses completely free. They are available to anyone, anywhere, especially on YouTube. As of May 2018, over 2,400 courses were available online. A majority of courses also have provided homework problems and exams, and notes. All video and audio files are also available from YouTube, iTunes U, and the Internet Archive profile.
You can sharpen Your Skills with Data Science Training by KnowledgeHut. You can learn to wrangle massive data sets, data visualization, etc. and get ready for lucrative job offers with their online Bootcamps. Acquire skills across programming languages and technologies including Python, R, MongoDB, TensorFlow, Keras and more. You will also gain real life experience with labs, assignments and build real-world-like projects to impress recruiters at top tech companies with your portfolio.
We have successfully gone through the impact of Data Science Platforms and how they can aid address multiple real-world issues. With the power of automation in Data Science, Data Scientists and researchers can focus more on analytics and research rather than maintaining code and working on broiler code. Looking at and understanding all of these platforms will help you form a solid foundation for some of the technical progress that we are making in the Data Science Community.
Frequently Asked Questions (FAQs)
1. Which are the best platforms to learn data science?
Apart from open platforms like Kaggle, Colab by Google, learning platforms on AWS by Amazon, Azure by Microsoft and IBM, learners can always opt for instructor-led programs on platforms like KnowledgeHut and more.
2. How does a data science platform handle the dynamic nature of data science work?
Typically, data science projects involve a number of varied tools designed for each step of the data analysis and modeling process. Hence, a single, integrated platform where a whole team of data scientists can work together and complement each other’s work rather than starting from scratch every time adds the dynamic nature.
3. Why do existing tools like Git, JIRA, and Jenkins fail to meet the needs of a data science platform?
Tools like Jenkins, Git, JIRA, are open-source Continuous Integration tools for orchestrating a chain of actions to achieve the Continuous Integration process in an automated fashion. They fail to meet the dynamism required to be a data-science platform, where a team can work parallelly together.