DataRobot is an automated machine learning (AutoML) platform that enables organizations to build, deploy, and manage machine learning models without requiring deep expertise in data science. It simplifies the process by automating many aspects of model development, such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. DataRobot’s intuitive interface allows both technical and non-technical users to create predictive models quickly and accurately. It supports a wide range of use cases across various industries, including financial forecasting, customer churn prediction, fraud detection, sales forecasting, and healthcare analytics. By leveraging machine learning algorithms, DataRobot enables businesses to extract insights from their data, make data-driven decisions, and automate processes for improved efficiency and productivity.

What is DataRobot?

DataRobot is an end-to-end machine-learning platform designed to automate the process of building, evaluating, and deploying machine-learning models. With its intuitive interface and automation capabilities, it provides a range of machine learning algorithms, preprocessing methods, and tools to simplify the workflow for data scientists, business analysts, and organizations.

Key Characteristics:

• Automation: DataRobot automates the entire machine learning lifecycle, from data cleaning and preprocessing to model selection and hyperparameter tuning.
• Enterprise Ready: It is suitable for both small teams and large enterprises, and it supports cloud-based and on-premise deployments.
• Model Explainability: Provides tools to understand how machine learning models make predictions, ensuring transparency.

Top 10 Use Cases of DataRobot

1. Predictive Maintenance: DataRobot enables companies to predict equipment failures before they happen, thus minimizing downtime and maintenance costs.
2. Customer Churn Prediction: DataRobot helps businesses predict which customers are at risk of leaving, enabling retention strategies that improve customer loyalty.
3. Fraud Detection: It automates fraud detection processes across industries, helping businesses identify suspicious activities, from financial transactions to insurance claims.
4. Demand Forecasting: Companies in retail and manufacturing leverage DataRobot to predict customer demand and optimize their supply chain and inventory management.
5. Risk Management: DataRobot is widely used in finance to assess risk, such as in credit scoring, loan approvals, and insurance underwriting.
6. Healthcare Predictions: Healthcare providers use DataRobot to predict patient outcomes, optimize treatment plans, and enhance clinical decision-making.
7. Marketing Optimization: DataRobot helps marketers identify trends and optimize marketing campaigns by predicting customer behavior and engagement.
8. Sales Forecasting: DataRobot’s predictive capabilities help sales teams forecast sales trends, identify growth opportunities, and optimize resources.
9. Energy Consumption Optimization: Utility companies leverage DataRobot to forecast energy consumption patterns and optimize the distribution of energy resources.
10. Supply Chain Optimization: DataRobot helps businesses optimize their supply chains by predicting demand, identifying inefficiencies, and improving operational decisions.

What are the Features of DataRobot?

1. Automated Machine Learning (AutoML): Simplifies the process of creating machine learning models, from data preparation to model selection.
2. End-to-End Workflow: Covers the entire AI lifecycle, including data preparation, feature engineering, model building, deployment, and monitoring.
3. Prebuilt Models and Templates: Offers a wide range of pre-configured models for common use cases, reducing time-to-value.
4. Explainable AI: Provides detailed insights into how models make predictions, ensuring transparency and building trust.
5. Scalability: Handles large datasets and complex problems, enabling the deployment of models at scale.
6. Integration Capabilities: Easily integrates with popular data platforms, APIs, and enterprise systems.
7. Collaboration and Governance: Facilitates collaboration between data teams and ensures adherence to compliance and governance standards.
8. Real-Time Predictions: Enables fast, real-time scoring of new data, making it suitable for applications that require immediate results.

How DataRobot Works and Architecture

DataRobot’s architecture is built around automation, scalability, and usability. It typically involves the following components:

1. Data Preparation Layer: Allows users to upload data, clean it, and perform feature engineering directly within the platform.
2. AutoML Engine: Automatically selects and tunes machine learning algorithms, tests multiple model configurations, and identifies the best-performing models.
3. Deployment and Scoring Layer: Offers tools for deploying models as APIs, batch jobs, or embedded solutions.
4. Explainability Layer: Includes features like model interpretability, feature importance, and prediction explanations to help users understand how models make decisions.
5. Monitoring and Management: Provides tools for tracking model performance, detecting data drift, and triggering retraining when needed.

How to Install DataRobot

To use DataRobot programmatically, you can interact with its API via Python using the datarobot Python package. Here’s how you can install and set it up to work with DataRobot:

1. Create a DataRobot Account

• If you don’t already have an account, sign up for DataRobot on their website: DataRobot.

2. Install the datarobot Python Package

To interact with DataRobot’s services, you’ll need the official datarobot Python client. You can install it via pip:

pip install datarobot

3. Get Your API Key

• Once logged into DataRobot, navigate to the API section in your account settings to retrieve your API key.
• You’ll need this API key to authenticate your Python code when making requests to DataRobot.

4. Set Up Your API Client in Python

After installing the datarobot package, you’ll need to configure it with your API key to interact with the platform. Here’s an example of how to set it up:

import datarobot as dr

# Replace 'YOUR_API_KEY' with your actual DataRobot API key
api_key = 'YOUR_API_KEY'

# Set the API key
dr.Client(token=api_key)

5. Upload Data and Start a Model

Once you have set up the DataRobot client, you can upload your dataset and initiate a model-building process. Here’s an example to get you started:

# Import libraries
import datarobot as dr
import pandas as pd

# Set up the DataRobot client
api_key = 'YOUR_API_KEY'
dr.Client(token=api_key)

# Upload a dataset (CSV example)
dataset = pd.read_csv('your_dataset.csv')
project = dr.Project.create(sourcedata=dataset)

# Start AutoML process (build models)
project.set_target(target='your_target_column')
project.start_all_models()

Replace 'your_dataset.csv' with your dataset file path and 'your_target_column' with the column you want to predict.

6. Monitor Model Progress and Retrieve Results

You can monitor the status of the model-building process and retrieve the top-performing models:

# Get project details
project = dr.Project.get(project.id)
print("Project Status:", project.status)

# Retrieve models
models = project.get_models()
top_model = models[0]  # Assuming the first model is the best
print("Top Model:", top_model)

7. Deploy and Predict with the Model

After training the model, you can deploy it for making predictions:

# Deploy the top model
deployment = top_model.deploy()

# Use the deployment to predict new data
predictions = deployment.predict(new_data=pd.DataFrame({'column1': [value1], 'column2': [value2]}))
print(predictions)

Basic Tutorials of DataRobot: Getting Started

Step 1: Log into DataRobot
Go to the DataRobot platform and log into your account (or sign up for a free trial).

Step 2: Upload Your Dataset

• After logging in, you can upload your dataset through the DataRobot interface.

# Example of uploading a dataset
import datarobot as dr
project = dr.Project.create(project_name='Predictive Analytics', dataset='data.csv')

Step 3: Let DataRobot Automate the Model Building

• DataRobot will automatically analyze the data, preprocess it, and start training various models.

Step 4: Evaluate and Select the Best Model

• Once the models are trained, DataRobot will rank them based on performance, and you can choose the best model for deployment.

Step 5: Deploy the Model

• Once you’ve selected your model, you can deploy it via DataRobot’s user interface.

# Example of model deployment
model = project.get_models()[0]
model.deploy()

The post What is DataRobot and Its Use Cases? appeared first on Artificial Intelligence.

DataRobot brengt enterprise AI-platform naar Nederlandse markt om organisaties te helpen sneller waarde uit data te halen.

DataRobot, leverancier van een geavanceerd enterprise AI-platform, betreedt de Nederlandse markt en opent een kantoor in Amsterdam. Het platform, met end-to-end automatisering voor het bouwen, implementeren en beheren van machine learning-modellen, maakt data science breder toegankelijk. Bedrijven kunnen zo eenvoudiger AI-modellen bouwen en in gebruik nemen. In Nederland tekenden de Gemeente Den Haag en de Nierstichting als eerste voor het platform van DataRobot.  

De introductie in Nederland is een logisch gevolg van de enorme expansie van DataRobot. Het Amerikaanse bedrijf is opgericht in 2012 en is sindsdien exponentieel gegroeid. DataRobot heeft de afgelopen jaren verschillende bedrijven overgenomen, zoals Nutonian, Nexosis, Cursor, ParallelM en Paxata. Sinds de start is er meer dan $750 miljoen in het bedrijf geïnvesteerd. DataRobot wordt inmiddels gewaardeerd op $2,8 miljard en werkt voor veel Fortune 500-ondernemingen. Inmiddels zijn er meer dan twee miljard machine learning-modellen gebouwd op het cloud-platform. Naast het openen van een kantoor in Nederland is DataRobot onder meer actief in de Verenigde Staten, het Verenigd Koninkrijk, Denemarken, Oekraïne, Singapore en Japan. 

“Voorheen vergde machine learning veel tijd en vereiste het veel handmatig programmeren. Daardoor konden organisaties slechts ten dele van de technologie profiteren”, vertelt Joep Gerrits, Regional Sales Manager bij DataRobot. “Er is een tekort aan data scientists, en zonder hen zijn bedrijven beperkt in het aantal modellen dat ze kunnen ontwikkelen en testen. Bovendien is het naar productie brengen van de modellen vaak een issue, omdat de data scientists en degenen die de modellen moeten toepassen niet dezelfde expertise hebben. Dit betekent in veel gevallen dat modellen soms al zijn verouderd tegen de tijd dat ze ingezet zouden moeten worden.”

Integratie kennis data scientists

Om dit probleem op te lossen, integreert DataRobot de kennis van meer dan 400 van ‘s werelds beste data scientists en engineers die het in dienst heeft, in zijn platform. DataRobot beschikt over een uitgebreid arsenaal krachtige, open-source algoritmen en technieken voor machine learning. Deze worden continu up-to-date gehouden, zodat iedere klant zonder uitgebreide ervaring met data science of codeer kennis de best passende AI-oplossing kan kiezen en inzetten.   

Gerrits ziet door de krachtige technologie van DataRobot veel kansen om het succes verder voort te zetten op de Nederlandse markt. Om deze te benutten wordt de lokale organisatie snel uitgebreid. “Organisaties zijn niet alleen op zoek naar een manier om sneller en makkelijker modellen te bouwen. Ze willen deze ook naar productie kunnen brengen zodat ze daadwerkelijk waarde opleveren. Wij helpen hen bij beide en hebben daarmee een sterke propositie om ook in Europa snel te groeien.” 

The post DataRobot: Sterke groei wordt voortgezet in Europa appeared first on Artificial Intelligence.

DataRobot’s AI platform makes it easy to build predictive models to identify infection hotspots

A new data-driven project is launching with the ambitious aim of resolving the COVID-19 pandemic in 60 days. 

ContagionNET has been designed by DataRobot, a global enterprise AI company that enables easy building and deployment of predictive models. Aimed at solving the data sharing problem that has plagued the COVID-19 pandemic response, the not-for-profit initiative will help determine when a patient is at their most contagious.

ContagionNET combines affordable antigen checks performed at home, anonymous data collection, and an AI platform. With these tools DataRobot are aiming to identify the most contagious areas in a community and encourage people to take preventative action. The goal is to identify people earlier than traditional testing (i.e., when they are most infectious) to disrupt the chain of transmission. 

ContagionNET can also predict how many people need to participate on a county-by-county basis by measuring the impact of different participation levels and testing frequency. This localised approach allows those with the highest viral load to self-isolate earlier, preventing further spread of the virus. 

“The reality is that those most likely to spread COVID-19 aren’t usually aware they are contagious” said Sally Embrey, DataRobot’s VP of Public Health and Health Technologies. 

“With the proliferation of frequent, easy-to-use at home antigen checks, ContagionNET is poised to inform the most contagious of their risk and help them make lifestyle modifications that will significantly reduce spread of the virus.”

DataRobot has been involved in pandemic response since early 2020. Working with the US government, DataRobot’s forecasting helped vaccine manufacturers select the right participants and prioritise enrollment in the highest risk locations over each vaccine trial period. 

The organisation has also used modeling to improve testing distribution, equity, and diagnostic reporting, all of which have informed its approach to ContagionNET. 

The post DataRobot launches solution to defeat Covid-19 appeared first on Artificial Intelligence.

How to bridge the production gap between data and IT

Data scientist has been one of the superstar IT roles of recent years, with the promise of spinning data into gold with the application of AI and machine learning.

Using cutting-edge technology to extract deep insights from reams of business data, data scientists aim to help guide their organisations into a more innovative, efficient and profitable future. But so far, return on investment hasn’t always been what companies might hope.

“One of the biggest mysteries in data science today actually has very little to do with data science: What is that last mile to AI ROI?” says Sivan Metzger, managing director MLOps and governance at DataRobot. “You build your machine learning, you find the data, you get it cleaned up, you build the models, you try 90 different iterations, you make a good and clean one and it’s ready to go. What happens then? Why are we not seeing value at scale from AI?”

Metzger credits these issues to a disconnect between the data team, IT operations and stakeholders on the business side (i.e. the potential consumers of data science insights). Data science and IT operations teams have very different considerations and goals – and machine learning is very different from running software. This disconnect is known as the ‘production gap’, and can prevent AI solutions from being properly executed.

Machine Learning Operations (MLOps) is a combination of processes, best practices and underpinning technologies which seeks to bridge this gap by increasing collaboration and communication between data scientists and operations staff – and ultimately ensuring that AI is properly deployed and can begin to deliver the ROI promised.

To learn more about how MLOps can improve your returns on AI, watch IT Pro and DataRobot’s webinar ‘The Last Mile to AI ROI’, in which Metzger and data scientist Rajiv Shah discuss topics including:

• How to eliminate AI-related risks by adopting MLOps best practices
• The inherent challenges of production model deployment and how to overcome them
• Model-monitoring best practices
• Production lifecycle management and why it matters

The post Why isn’t your AI delivering ROI? appeared first on Artificial Intelligence.

BOSTON–(BUSINESS WIRE)–AtScale, the leading provider of intelligent data virtualization, today announced a partnership with DataRobot, the leading enterprise AI platform, to deliver a turnkey approach to predictive and descriptive data analysis. The partnership builds a bridge between data science and business intelligence to incorporate and share the same KPIs used for predictive and prescriptive business decisions.

“We are thrilled to partner with DataRobot to accelerate our go-to-market within the data science community. DataRobot’s vision for maximizing business value via AI is unparalleled, and the combination of our capabilities is another step toward democratization of predictive and prescriptive analytics,” said Christopher Lynch, executive chairman and CEO, AtScale.

Organizations globally are moving AI predictions into day-to-day business operations. The combination of DataRobot and AtScale provides a consumable enterprise metrics hub with consistent, governed KPIs and a simplified interface for automated feature creation utilizing DataRobot’s Feature Importance and Impact functionality. AtScale uniquely makes DataRobot predictions consumable to business intelligence and reporting workstreams, providing both technical and non-technical stakeholders access to predictions using the tool of their choice.

“Our global market presence and diverse customer base continue to highlight the need for greater unity between the data science and business intelligence communities,” said Jeremy Achin, co-founder and CEO, DataRobot. “AtScale’s intelligent data virtualization adds a new business constituency in the enterprise who can incorporate AI predictions for operational decision making.”

About AtScale
AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data Virtualization platform provides organizations the opportunity to enhance or create a self service data culture by leveraging its business-friendly semantic layer, intelligent data engineering, easy-to-use web-based design interface, multi-cloud support, and strong data source and client integration.

Learn more about why AtScale is a leader for enterprises to deliver fast, accurate data-driven business intelligence and machine learning analysis at scale in GigaOm’s Data Virtualization Radar Report. For more information, visit www.atscale.com, and connect with us on Twitter, and LinkedIn.

About DataRobot
DataRobot is the leader in enterprise AI, delivering trusted AI technology and enablement services to global enterprises competing in today’s Intelligence Revolution. DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting-edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.

DataRobot has offices across the globe and funding from some of the world’s best investing firms including Alliance Bernstein, Altimeter, B Capital Group, Cisco, Citi Ventures, ClearBridge, DFJ Growth, Geodesic Capital, Glynn Capital, Intel Capital, Meritech, NEA, Salesforce Ventures, Sands Capital, Sapphire Ventures, Silver Lake Waterman, Snowflake Ventures, Tiger Global, T. Rowe Price, and World Innovation Lab. DataRobot was named to the Forbes 2020 Cloud 100 list and the Forbes 2019 and 2020 Most Promising AI Companies lists, and was named a Leader in the IDC MarketScape: Worldwide Advanced Machine Learning Software Platforms Vendor Assessment. For more information visit www.datarobot.com, and join the conversation on the DataRobot Community, More Intelligent Tomorrow podcast, Twitter, and LinkedIn.

The post AtScale Partners with DataRobot to Drive Collaboration between Data Science and Business Intelligence Teams appeared first on Artificial Intelligence.

Humility in AI protects the quality of Decision systems

In DataRobot, Humble AI is a new feature that protects the quality of your predictions in circumstances where the model is not confident enough. With Humble AI, users make rules for models in deployment for forecasts made in real-time. These rules recognize conditions which indicate that a prediction may not be sure and trigger actions such as defaulting to a ‘safe’ prediction, overriding outlier values, or not predicting at all.

Even if Humble AI is a new concept, it is clear that it holds tremendous value for decision systems. It is particularly real-time decision systems where each decision counts and is urgent. Before we talk about the cost to business decision-making, let’s look at the three most common frameworks around AI and human intelligence working as a single integrated decision system.

Human-in-the-loop

In this process, the individual operating the system considers the recommendation of the AI system but takes the final call. For instance, though a doctor signs off on the definitive diagnosis and prognosis of kidney failure, they leverage an advanced visual artificial intelligence system to score the patient’s X-rays.

Human-out-of-the-loop

In this process, the AI system is entirely under control with zero human involvement. For example, an AI is running a real-time bidding (RTB) system creates instantaneous predictions around potential ad buys in milliseconds, without any human involvement.

Human-over-the-loop

An individual, in this process, supervises the system and can intervene whenever the AI system runs into an unexpected scenario and deviates from its performance standard. For instance, a regression model monitors manufacturing processes in real-time. As soon as an outlying prediction is triggered, a human operator is alarmed. And it turns off the system and investigates.

When it’s about choosing a level of automation for any given system, the resulting trade-off should be considered. On the edge of the spectrum, you have unbridled industrialisation that approaches maximum efficiency. However, it comes at the cost of losing the guardrails delivered by humans looped into the process. If your appetite is compact or you’re working in a highly regulated environment, the human-out-of-the-loop system may cause a serious business risk.

However, in some cases, such as real-time ad bidding, it’s impossible to get a human in the loop with decision-making capacity in a fraction of second. This is all accomplished based on a head-spinning amount of anonymized user information and quick behavioural patterns of each user. Such digital advertising systems can multi-task and effectively when a human is not in the loop. How do you mitigate risks when the decision window is unexpectedly small, and the decisions are extraordinarily complicated?

This is when the human-over-the-loop and humility in AI concept come into play. Human-over-the-loop system can balance between automation and human participation by enabling the system to perform in the fully automated mode with human intervention when required.

DataRobot MLOps currently supports a long-term oversight framework by uninterrupted collecting scoring data, predictions, and original outcomes to compare trained and installed models’ statistical properties. Wherein it can help detect the critical moment when retraining the model is necessary, humility in AI indicates that human intervention should also be an alternative on the level of an instantaneous individual prediction. However, there are a few primary questions in it.

How to understand when precisely human beings should be involved? It depends on the confidence level of the model’s prediction. The confidence level could be determined by monitoring:

Uncertainty in predictions: Predicted values are beyond the range of expected values

Outlying outputs: There are several features in the scoring data dissimilar to what the training model absorbed

Low monitoring regions: A categorical feature value is the one which a user has mentioned unexpected or inappropriate.

With DataRobotMLOps, capabilities such as no operation, overriding prediction, and discarding the prediction are baked into the Humble AI feature.

The post HOW HUMBLE AI INFLUENCES DECISION MAKING SYSTEM appeared first on Artificial Intelligence.

Dublin, June 15, 2020 (GLOBE NEWSWIRE) — The Global Data Science Platform Market study with 100+ market data Tables, Pie Chat, Graphs & Figures is now released by Data Bridge Market Research. This Data Science Platform market report serves to be an ideal solution for better understanding of the market and high business growth. It has become the requisite of this rapidly changing market place to take up such marker report that makes aware about the market conditions around. This Data Science Platform market report comprises of an array of factors that have an influence on the market and industry which are industry insight and critical success factors (CSFs), market segmentation and value chain analysis, industry dynamics, drivers, restraints, key opportunities, technology and application outlook, country-level and regional analysis, competitive landscape, company market share analysis and key company profiles

Due to the pandemic, we have included a special section on the Impact of COVID 19 on the Global Data Science Platform Market which would mention How the Covid-19 is affecting the Industry, Market Trends and Potential Opportunities in the COVID-19 Landscape, Covid-19 Impact on Key Regions and Proposal for the Global Data Science Platform Market Players to Combat Covid-19 Impact.

Key Market Features in Global (United States, European Union and China) Data Science Platform Market:

The report highlights Data Science Platform market features, including revenue, weighted average regional price, capacity utilization rate, production rate, gross margins, consumption, import & export, supply & demand, cost bench-marking, market share, CAGR, and gross margin.

Analytical Market Highlights & Approach

The Global (United States, European Union and China) Data Science Platform Market report provides the rigorously studied and evaluated data of the top industry players and their scope in the market by means of several analytical tools. The analytical tools such as Porters five forces analysis, feasibility study, SWOT analysis, and ROI analysis have been practiced reviewing the growth of the key players operating in the market.

List of Best Players profiled in Data Science Platform Market Report;

Google, Inc., Domino Data Lab, IBM Corporation, Datarobot, Inc., Microsoft Corporation, Wolfram, Continuum Analytics, Inc., Dataiku, Bridgei2i Analytics, Feature Labs, Datarpm, Rexer Analytics, Civis Analytics, Sense, Inc., Alteryx, Inc., Rapidminer, Inc., IBM, Snowflake, MeritDirect, Cazena, CBIG Consulting, Loggly, Clairvoyant, Arcadia, Experfy, Datatorrent, Jethro, Tableau, VMware, New Relic, Alation, Tera Data, SAP, Alpine Data Labs, SiSense, Thoughtworks, MuSigma, Cogito, Datameer among others

Key Benefits:

The report provides a qualitative and quantitative analysis of the current Data Science Platform market trends, forecasts, and market size to determine the prevailing opportunities.
Porter’s Five Forces analysis highlights the potency of buyers and suppliers to enable stakeholders to make strategic business decisions and determine the level of competition in the industry.
Top impacting factors & major investment pockets are highlighted in the research.
The major countries in each region are analyzed and their revenue contribution is mentioned.
The market report also provides an understanding of the current position of the market players active in the Data Science Platform industry.

Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.)

Key poles of the TOC:

Chapter 1 Global Data Science Platform Market Business Overview
Chapter 2 Major Breakdown by Type
Chapter 3 Major Application Wise Breakdown (Revenue & Volume)
Chapter 4 Manufacture Market Breakdown
Chapter 5 Sales & Estimates Market Study
Chapter 6 Key Manufacturers Production and Sales Market Comparison Breakdown
…………………..
Chapter 8 Manufacturers, Deals and Closings Market Evaluation & Aggressiveness
Chapter 9 Key Companies Breakdown by Overall Market Size & Revenue by Type
…………………….
Chapter 11 Business / Industry Chain (Value & Supply Chain Analysis)
Chapter 12 Conclusions & Appendix

What Businesses Can Hope to Get in Business Intelligence on Data Science Platform Market?

The study insights on the Data Science Platform market growth dynamics and opportunities highlights various key aspects, in which crucial ones are:

Which are the technology and strategic areas that emerging, new entrants, and established players should focus on keep growing in the industry-wide disruptions that COVID-19 has caused?
Which new avenues bear incredible potential during the ongoing COVID-19 lockdown restrictions?
Which policies by governments can give the top stakeholders support their efforts of consolidation?
What new business models are gathering pace among companies to remain agile in post-COVID-era?
Which segments will see a surge in popularity in near future, and what calibrations players need to make to utilize the trend for an elongated period?

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

The post DATA SCIENCE PLATFORM MARKET COMPETITIVE ANALYSIS, MARKET ENTRY STRATEGY, PRICING TRENDS, SUSTAINABILITY TRENDS AND INNOVATION TRENDS appeared first on Artificial Intelligence.

Analysis of COVID-19 Impact on the Data Science and Machine Learning Platforms Market with Key Players Analysis

The report highlights the current impact of COVID-19 on the Data Science and Machine Learning Platforms market along with the latest economic scenario and changing dynamics of the market. The report on the Data Science and Machine Learning Platforms market is an all-inclusive document comprising crucial information about top players, market trends, pricing analysis, and overview of the market for the forecast period. It consists of valuable information and an in-depth analysis of primary and secondary drivers, market share, leading segments, and regional analysis. The report also encompasses details on the key competitors and their strategies, such as mergers, acquisitions, recent technological developments, and the business landscape.

Competitive Analysis:

The competitive analysis covers key players and the innovations and business strategies undertaken by them. The report captures the best long term growth opportunities for the sector and includes the latest process and product developments. The report includes basic information of the companies along with their market position, historical background, and market capitalization and revenue. The report covers revenue figures, market growth rate, and gross profit margin of each player based on regional classification and overall market position. The report provides a separate analysis of the recent business strategies such as mergers, acquisitions, product launches, joint ventures, partnerships, and collaborations.

Key features of the Report:

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:
AWS, Databricks, Dataiku, DataRobot, Domino, Google, H2O.ai, IBM Watson Studio/Watson ML, KNIME, Microsoft Azure, RapidMiner, SageMaker, SAP

Market Breakdown:

The market breakdown provides market segmentation data based on the availability of the data and information. The market is segmented on the basis of types and applications.

Data Science and Machine Learning Platforms

To understand the global Data Science and Machine Learning Platforms market dynamics, the market is analyzed across major global regions and countries. Market Expertz provides customized specific regional and country-wise analysis of the key geographical regions as follows:

North America: USA, Canada, Mexico
Latin America: Argentina, Chile, Brazil, Peru, and Rest of Latin America
Europe:K., Germany, Spain, Italy, and Rest of EU
Asia-Pacific: India, China, Japan, South Korea, Australia, and Rest of APAC
Middle East & Africa: Saudi Arabia, South Africa, U.A.E., and Rest of MEA
The report considers:

Historical Years: 2017-2018
Base Year: 2019
Estimated Year: 2020
Forecast Years: 2020-2027
Benefits of Data Science and Machine Learning Platforms Market Report:

In-depth understanding of the market size of Data Science and Machine Learning Platforms market
Easy identification of growth opportunities and key product development strategies
Comprehensive historical and accurate forecast data for the Data Science and Machine Learning Platforms market to ease the decision-making process
Production and consumption ratio, import/export data, and companies’ market positions explained in detail with graphs and charts to aid in formulating lucrative strategies.
Strategic recommendations about partners and suppliers
Report Overview with TOC:

Research report overview along with COVID-19 impact analysis
Regional analysis of growth trends
Competitive landscape along with estimated revenue share, market share, and market concentration ratio
Segmentation data based on product types
Segmentation data based on applications

Thank you for reading our report. Customization of the report is also available on the basis of region and countries. Kindly get in touch with us for more information regarding the report and its customization. Our team will ensure the report is best suited according to your needs.

The post DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET INSIGHTS BUSINESS OPPORTUNITIES 2027 appeared first on Artificial Intelligence.

Ergomed plc, a global company renowned for providing specialized services to the pharmaceutical industry, has reportedly announced the strategic collaboration of PrimeVigilance with Automation Anywhere, a global leader in Robotic Process Automation (RPA) and DataRobot, a leader in Artificial Intelligence (AI). The collaboration has apparently taken place with a view to accelerate the Intelligent Automation strategy of the company.

Sources claim that the partnership is said to enhance PrimeVigilance’s existing skills and expertise, with the combination of human insight and knowledge, alongside the deployment of intelligent technologies, in order to improve the speed and accuracy of analysis and decision-making.

PrimeVigilance, a division of Ergomed, is the world’s leading provider of a complete suite of pharmacovigilance (PV) services, and presently employs more than 750 people. By empowering its customers and colleagues with RPA and Machine Learning (ML) applications, PrimeVigilance will enable customers to improve quality and consistency in safety databases, as well as productivity.

As pre reliable reports, through this deal, Automation Anywhere’s cloud-based Robotic Process Automation, on its digital workforce platform, in tandem with DataRobot ‘s enterprise AI platform, is likely to bring new levels of speed and intelligence to a critical business component. In essence, this would free up valuable hours, enabling highly trained pharmacovigilance professionals to focus on value creation and problem-solving that can be addressed only by humans.

PrimeVigilance wanted a cure for the manual repetitive processes which hardly add any value to the company, yet are required for the business. This need for an Automation partnership comes as hardly a surprise given how many firms are considering cutting costs by saving valuable human hours on such processes. Such automation techniques help humans save time and focus on innovation led projects which not only is the need of the hour, but is also the shortest route for market consolidation.

With the world economy suffering amidst this pandemic, firms are considering more and more strategic partnerships to accelerate growth and come up with radical breakthroughs. This partnership was made on the same foundation of mutual acceleration with synergetic growth.

This synergy will ensure that PrimeVigilance is able to apply the latest technologies, such as automation and robotic technology throughout their pharmacovigilance business, ensuring their clients, consistent cutting-edge services.

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Machine Learning has been serving several industries for the past many years. It has enabled businesses to work easily with data. Moreover, the acceleration in the adoption of ML tools has evolved with time making it even easier to use today. Using AutoML tools, the act of gathering data and turning it into actionable insights has become much convenient. People with even less knowledge of data science and machine learning can work with these automated tools.

DataRobot

In 2013, DataRobot invented automated machine learning — and an entirely new category of software as a result. Unlike other tools that provide limited automation for the complex journey from raw data to return on investment, the company’s Automated Machine Learning product supports all of the steps needed to prepare, build, deploy, monitor, and maintain powerful AI applications at enterprise scale.

DataRobot’s AutoML product accelerates the productivity of your data science team while increasing your capacity for AI by empowering existing analysts to become citizen data scientists. This enables your organization to open the floodgates to innovation and start your intelligence revolution today.

Google Cloud AutoML

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.

Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions.

dotData

dotData was born out of the radical idea, unique among machine learning companies, that the data science process could be made simple enough for just about anyone to benefit from it. Led by Dr. Ryohei Fujimaki, a world-renowned data scientist, and the youngest research fellow ever appointed in the 119-year history of NEC, dotData was created to accomplish this mission. The company values its clients and works hard to provide the highest value possible in Automated Machine Learning (AutoML).

dotData was first among machine learning companies to deliver full-cycle data science automation for the enterprise. Its data science automation platform speeds time to value by accelerating, democratizing, and operationalizing the entire data science process through automation.

Splunk

Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies, and generating predictions for future values.

H2O

H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

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