70-774 - Perform Cloud Data Science with Azure Machine Learning? - braindump
| Vendor | Microsoft | |
| Exam Number | 70-774 | |
| Exam Name | Perform Cloud Data Science with Azure Machine Learning? | |
| Questions | 37 Q & A | |
| Recent Update | March 20, 2019 | |
| Free PDF Download | 70-774 Brain Dump | |
| Download Complete PDF | Killexams 70-774 Complete Document |
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Alteryx dropped from the Leader’s Quadrant by maintaining its “ability to execute” (the Y axis) but losing some of its “completeness of vision” (the X axis). Gartner heralded the Irvin, California company’s citizen data science capabilities within an end-to-end pipeline. Despite its capabilities, the market perceives Alteryx as just a data preparation tool, which obscures its value, the analyst group says.
Dataiku‘s Data Science Studio (DSS) offering received high marks for the way it fosters collaboration among different stakeholders, from data engineers to scientists. Gartner also liked the automation it brings to the machine learning workflow, as well as the management and monitoring of models once they’re in production. Some concerns include scalability, pricing, and support for streaming analytics and IoT use cases, it says.
funding this week, impressed Gartner with its support for the full analytics life cycle, its support for hybrid cloud strategies, and its capability to support a variety of users. Users spoke highly of the Spark-based cloud offering, and documentation was a plus, per Gartner. Pricing and contract negotiations were potential weak spots for Databricks, along with monitoring, management, and troubleshooting and debugging potential problems.
week, dropped from the Leader’s Quadrant in 2019 into the Visionaries Quadrant as a result of strong competition, and some concerns from customers about capabilities. The performance of its core open source machine learning components remain a strength for H2O.ai, and Gartner was impressed with its GPU-based deep learning and the automated ML capabilities of Driverless AI. But a steep learning curve for non-developers, a lack of management capabilities, and a lack of data access and data prep features were concerns.
Challengers to the Visionaries Quadrant, thanks to “a remarkable strength” in serving the demands of its customers in asset-centric industries, according to Gartner (the company has a long heritage among manufacturers and engineering organizations). Its MATLAB offering was hailed for its “citizen engineer” capabilities, and integrated data prep and support for real-time streaming, deep learning, and simulation impressed the G man. Dings were difficulty of use by non-engineers, no support for Google Cloud Platform, and a lack of automated machine learning capabilities were downsides.
which include Azure Machine Learning, Azure Data Factory, Azure HDInsight, Azure Databricks, and Power BI. Gartner liked how Microsoft works with third-parties, in particular Databricks’ Spark offering. Support for diverse data personas, including entry-level ML enthusiasts, was also a plus. Automation in the ML process was a concern, as was the coherence of all the different tools. A lack of on-prem capabilities also limits its applicability.
2019, but it has lost ground. Gartner praised the comprehensive nature of IBM’s Watson Studio offering, which serves expert and citizen data scientists. Integration of the SPSS modeler into Watson Studio was also praised. But the frequency that IBM rebrands products and shifts strategy is a concern to Gartner, as is the need to license multiple products to get complete end-to-end capabilities.
and ML platform ranking, thanks largely to the wide breadth of tools available on its cloud. Its core data science platform consists of Cloud ML Engine, Cloud AutoML, TensorFlow, and BigQuery ML. But Google also offers unique hardware, with the Tensor Processing Unit (TPU), crowdsourcing with Kaggle, and a range of other offerings. Scalability and speed are strengths. But a lack of end-to-end cohesion among the tools was a concern, as well as a lack of reusability. The lack of an on-prem offering was also a concern.
SAP’s Predictive Analytics (PA) offering is tightly integrated with HANA, which makes it suitable for SAP HANA customers. The capability to process large HANA datasets and deploy models to SAP applications are strengths. So is SAP’s vision of a unified ML fabric, which is tied to its Leonardo Machine Learning Foundation. However, product coherence, a changing AI strategy, and the customer experience were marks against the German giant.
Domino Data Lab was downgraded from the Visionaries Quadrant, which reflected mostly a drop in its perceived ability to execute. Gartner likes Domino’s product strategy, in particular its focus on collaboration and building an end-to-end solution. Its ability to integrate with open source and proprietary products was a bonus, as was its scalability. But Domino’s focus on expert data scientists leaves citizen data scientists wanting, according to Gartner, and it also lacks some data prep, automation, and augmentation capabilities.
Anaconda remained in the Niche Players category. Key strength of the Anaconda product is its reach into the open source Python community, which continues to churn out data science innovation. Its capability to scale open source Python is also a plus. But the expertise needed to successfully wield the Anaconda platform is a caution, per Gartner, and the complexity of the Python “jungle” is also a concern. Reliance on the open source community also puts customers at a disadvantage when they need something specific (Gartner uses the example of model operationalization), and the overall level of coherence is a downside.
Datawatch is a newcomer to this Magic Quadrant by way of its January 2018 acquisition of Angoss, which has more than 20 years of experience in the field. Gartner praised the coherence and ease of use of the Datawatch products, and marked the text analytics and optimization engine components as above average. Customer support was also a plus. A lack of data preparation capabilities dragged Datawatch’s score down, while the overall vision of the product and uncertainties raised by the acquisition were also mentioned.
