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DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI action |

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DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI

Exam Specification: DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI

Exam Name: DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI
Exam Code: DP-500
Exam Duration: 150 minutes
Passing Score: 700 out of 1000
Exam Format: Multiple-choice
Exam Delivery: Proctored online or at a testing center

Course Outline:

1. Introduction to Enterprise-Scale Analytics Solutions
- Overview of enterprise-scale analytics solutions
- Understanding the benefits and features of Microsoft Azure and Power BI
- Exploring the architecture and components of Azure and Power BI

2. Planning and Designing Azure Data Platform Solutions
- Gathering and analyzing business requirements
- Designing data storage and processing solutions using Azure services
- Designing data integration and data movement solutions

3. Designing Data Processing Solutions
- Designing batch processing solutions using Azure Data Factory
- Designing real-time data processing solutions using Azure Stream Analytics
- Designing big data processing solutions using Azure Databricks and HDInsight

4. Designing Data Storage Solutions
- Designing relational and non-relational data storage solutions using Azure services
- Designing data warehousing solutions using Azure Synapse Analytics
- Designing data lake and analytics solutions using Azure Data Lake Storage and Azure Analysis Services

5. Implementing Power BI Solutions
- Designing and implementing data models in Power BI
- Creating and optimizing Power BI reports and dashboards
- Implementing security and governance in Power BI

Exam Objectives:

1. Understand the concepts, benefits, and features of enterprise-scale analytics solutions using Azure and Power BI.
2. Plan and design Azure data platform solutions based on business requirements.
3. Design data processing solutions using Azure Data Factory, Azure Stream Analytics, and Azure Databricks/HDInsight.
4. Design data storage solutions using Azure services, including Azure Synapse Analytics and Azure Data Lake Storage.
5. Design and implement data models, reports, and dashboards in Power BI.
6. Implement security and governance measures in Power BI.

Exam Syllabus:

Section 1: Introduction to Enterprise-Scale Analytics Solutions (10%)
- Enterprise-scale analytics solutions overview
- Benefits and features of Azure and Power BI
- Architecture and components of Azure and Power BI

Section 2: Planning and Designing Azure Data Platform Solutions (25%)
- Gathering and analyzing business requirements
- Designing data storage and processing solutions using Azure services
- Designing data integration and data movement solutions

Section 3: Designing Data Processing Solutions (25%)
- Designing batch processing solutions using Azure Data Factory
- Designing real-time data processing solutions using Azure Stream Analytics
- Designing big data processing solutions using Azure Databricks and HDInsight

Section 4: Designing Data Storage Solutions (25%)
- Designing relational and non-relational data storage solutions using Azure services
- Designing data warehousing solutions using Azure Synapse Analytics
- Designing data lake and analytics solutions using Azure Data Lake Storage and Azure Analysis Services

Section 5: Implementing Power BI Solutions (15%)
- Designing and implementing data models in Power BI
- Creating and optimizing Power BI reports and dashboards
- Implementing security and governance in Power BI
Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI
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Question: 25
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Which type of summarization should you use for SalesAmount and StoreKey? To answer, select the appropriate
options in the answer area. NOTE: Each correct selection is worth one point.
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UserPermissions contains the following data.
You plan to create a security role named User Security for the dataset. You need to filter the dataset based on the
current users .
What should you include in the DAX expression?
A. [UserPermissions] USERNAME()
B. [UserPermissions] USERPRINCIPALNAME()
D. [User] = USERNAMEQ()
Answer: D
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Which Python libraries should you use?
A. Seaborn only
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C. Matplotlib only
D. Matplotlib and TensorFlow
E. TensorFlow only
F. Seaborn and TensorFlow
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Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order.
Question: 30
You have the following code in an Azure Synapse notebook.
Use the drop-down menus to select the answer choice that completes each statement based on the information
presented in the code. NOTE: Each correct selection is worth one point.

Microsoft Enterprise-Scale action - BingNews Search results Microsoft Enterprise-Scale action - BingNews Microsoft Tops Estimates on Enterprise Strength: 10 Key Takeaways No result found, try new keyword!After the bell on Wednesday, Microsoft ... Enterprise Mobility + Security software and Power BI business intelligence software. Microsoft and are holdings in Jim Cramer's Action Alerts ... Wed, 23 Oct 2019 13:03:00 -0500 text/html How To Unlock And Scale Enterprise Innovation

Joseph Olassa, CEO, Nuivio Ventures & Ignitho Technologies, with expertise in Data Science, AI & Automation and Product Engineering.

Especially with the rise of AI, established practices and business models are evolving at a fast pace. And since the industry adoption of innovation is moving at the same great speed, incremental advantages are fleeting.

We must focus on improving customer experience in an impactful way. However, innovation in a large enterprise takes time.

In this post, I’ll outline a few ways to speed up innovation, then introduce a way to scale it even faster while transferring the risk.

Speeding Up Enterprise Innovation From Within

There are five methods that allow more practical use cases to be released faster rather into operations than perishing in the innovation lab:

1. Establish clarity.

How many more proofs of concept of blockchain technology, NFTs, AI, augmented reality and the metaverse will need to be finalized before any of it can see the light of the day? While it’s vital to innovate and experiment, it is also crucial for an organization to have a clear understanding of what it is trying to achieve through innovation. To do so, enterprises must create an innovation model that tightly links operations with the lab. This will ensure that resources are focused on initiatives most likely to be implemented because they meet a real or urgent need.

2. Encourage a culture of innovation.

It’s in our nature to get used to the status quo. That leads to inertia and resistance. Creating a culture that supports, encourages and demands constant innovation can foster new ways of thinking and encourage employees to come forward with their own ideas. This can be achieved through activities such as business process workshops, hackathons, design thinking sessions and other forms of collaborative problem-solving.

3. Foster collaboration and cross-functional teamwork.

Enterprises, often by design, operate in silos to maximize efficiency and control. Bringing together people from different functions can help with new ideas and approaches by assembling a bird’s eye view that wasn’t possible while thinking in smaller groups. Different perspectives when developing new concepts can also lead to disruptive innovation. While the tendency to protect the current business model will always persist, the inertia to change can be dramatically lowered with this practice.

4. Invest in training and development.

Innovation, especially automation and AI, can often cause apprehension and fear. History has shown that the opposite is true in most cases so long as we can continue learning. Providing employees with the skills and knowledge they need to innovate and tackle emerging roles can help them feel more confident and capable of pursuing new ideas. This includes training in adjacent areas and emerging technologies.

5. Create an agile development process.

Adopting an agile development process can help with quick prototyping and testing of new ideas. Faster iterations can bring solutions to market more quickly. It also allows for the adoption of design thinking principles.

Changing The Model: Externalizing The Innovation

It also helps to look at other organizational models. One such model is spinning out innovation into a separate company through partnerships. This can be a great way for an enterprise to reduce costs and accelerate its own time to market. It also mitigates the risk and the investment needed.

A good way to think about this is to compare it to a third-party technology product you might license and adopt if it was available.

1. Identify the innovation.

The first step is to identify the right innovation. For example, in media, these could be analytical capabilities, new digital delivery models, automation in content production, new widgets, etc. In retail, these could be new ways to present and target products, new channels, and customer service capabilities. The innovation must have the potential to be a stand-alone business and not be expected to be a core asset of the company. The rationale is that many innovators can be assumed to be bringing such capabilities to market. So, it’s best to focus on adoption and usage rather than its development.

2. Identify a complementary partner.

This step is important because it reduces the risk and investments needed. Look for a partner with complementary skills or resources that can bring innovation to the market quickly and effectively.

A simple analogy of this model is an early product from a startup that you may be willing to bet on in return for some exclusivity guarantees in the short term. The partner you choose can also assess the feasibility of the innovation such as the potential market size, competition, and the costs and resources required to bring the innovation to market.

3. Establish an innovation sponsor from your enterprise.

You may even want this person to lead this new product into the market. Often, there are innovation champions and executives in your enterprise who would welcome the opportunity to do so. The new product would be set up as a new entity with its own management team.

Using this model, the new company can focus on developing and commercializing the innovation, while the parent company can continue to focus on integrating and adopting the innovation into its core business.

Because it is a co-investment model, the enterprise can reduce costs and eliminate the overhead of developing and commercializing the innovation within the larger organization. The new company can be more agile and focused, allowing it to bring innovation to market more quickly.

Final Thoughts

It is imperative for enterprises to rapidly bring innovation to the market. The costs of not doing so are too great.

The best practices I outlined in this post are sure ways to speed up innovation and help it break out of the lab. At the same time, thinking outside the box by using the model of spinning out innovation can often pay dividends.

Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?

Sun, 12 Feb 2023 23:30:00 -0600 Joseph Olassa en text/html
New York Times sues OpenAI, Microsoft for copyright infringement

The New York Times is suing OpenAI and Microsoft, accusing them of using millions of the newspaper's articles without permission to help train artificial intelligence (AI) technologies.

The Times said it is the first major U.S. media organization to sue OpenAI and Microsoft, which created ChatGPT and other AI platforms, over copyright issues.

"Defendants seek to free-ride on the Times's massive investment in its journalism by using it to build substitutive products without permission or payment," according to the complaint filed Wednesday in Manhattan Federal Court.

"There is nothing 'transformative' about using the Times's content without payment to create products that substitute for the Times and steal audiences away from it." 

The Times is not seeking a specific amount of damages, but said it believes OpenAI and Microsoft have caused "billions of dollars" in damages by illegally copying and using its works.

In a statement to CBC News, OpenAI said: "We respect the rights of content creators and owners and are committed to working with them to ensure they benefit from AI technology and new revenue models.

"Our ongoing conversations with The New York Times have been productive and moving forward constructively, so we are surprised and disappointed with this development. We're hopeful that we will find a mutually beneficial way to work together, as we are doing with many other publishers."

Investors have valued OpenAI at more than $80 billion US. None of the allegations have been proven in court.

Last September, a group of 17 prominent U.S. authors — including George R.R. Martin, John Grisham and Jodi Picoult — joined a class-action lawsuit against OpenAI, alleging the company had pirated hundreds of books online without permission or compensation.

AI systems like ChatGPT, launched in late 2022, learn to generate new content by first ingesting huge amounts of existing content — often taken from the internet. The collected material is then used to build a large-language model that can produce human-like responses to a user's query.

In papers filed in Federal Court in New York, the authors allege "flagrant and harmful infringements of plaintiffs' registered copyrights" and called the ChatGPT program a "massive commercial enterprise" that is reliant upon "systematic theft on a mass scale."

The suit was organized by the Authors Guild and includes David Baldacci, Sylvia Day, Jonathan Franzen and Elin Hilderbrand, among others.

WATCH | AI warnings: Is anybody listening? | About That: 

AI warnings: Is anybody listening? | About That

7 months ago

Duration 9:27

More than 350 tech leaders, academics and engineers — the people who best understand artificial intelligence — have signed a dramatic statement, warning AI could be a global threat to humanity on the scale of pandemics and nuclear war. But who's listening?

OpenAI argued in court last summer for two similar lawsuits on copyright infringement that the training of AI systems using copyrighted material is fair use of content.

Several companies that use generative AI — including Meta Platforms and Stability AI — face similar lawsuits by writers, visual artists and source-code writers.

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What a month!  De ja vu all over again. Over the past year or so I’ve written about broadband access, loving/hating Amazon, the Capital One data breachcivilian surveillanceMicrosoft, AI and the technology oligarchy, and over the past month or so all seven hit the headlines somewhere. So let’s review with some prescriptions: 

Amazon is a blessing and a curse. Even CNN got into the act with its “The United States of Amazon.” Amazon’s a blessing because it makes life easier. It’s a curse because it displaces traditional retailers, often charges more than brick-and-mortar stores and makes us dependent upon a digital platform beyond our control. But Amazon isn’t the only tech company upon which we’ve become dependent. The invasion is growing and unstoppable as we all learn about digital addiction. Rehab is impractical and likely ineffective. It is a macro trend that shows no signs of slowing.  Exploit the convenience after conducting pricing due diligence.     

The Capital One breach is just the latest cyberattack upon large institutions, and now we learn that the accused Capital One hacker may have hacked thirty other companies. As I’ve argued many times here before, breaches are growing in number and severity. CIOs, CTOs and CISOs should re-double their efforts and investments in cybersecurity (with no ensure that they won’t be hacked). The worst outcome is a breach without a solid defense. If you haven’t done a penetration test recently, do one ASAP. Check your team as well to make sure they have the right skills and competencies. Your vendors can be your friends here as well – and if there’s a breach they can be enlisted to support their cybersecurity products and services (and share some of the blame).   

Microsoft will probably end up bigger than Apple, Amazon and Google. It’s accurate announcement about the OpenAI partnership formalizes its most accurate focus on AI/ML and one of its key growth strategies:

  • “Microsoft and OpenAI will jointly build new Azure AI supercomputing technologies
  • “OpenAI will port its services to run on Microsoft Azure, which it will use to create new AI technologies and deliver on the promise of artificial general intelligence
  • “Microsoft will become OpenAI’s preferred partner for commercializing new AI technologies”

Artificial intelligence and machine learning (AI/ML) will run much of the digital world. Microsoft’s decision to invest $1B in AI/ML speaks directly to the role that AI will play in software engineering, augmented analytics and automation, among other functional and non-functional domains. Microsoft’s aggressive bet on OpenAI: 

“ … will focus on building a computational platform in Azure of unprecedented scale, which will train and run increasingly advanced AI models, include hardware technologies that build on Microsoft’s supercomputing technology, and adhere to the two companies’ shared principles on ethics and trust. This will create the foundation for advancements in AI to be implemented in a safe, secure and trustworthy way and is a critical reason the companies chose to partner together.”

Track progress here – along with the progress that all of the major hardware and software vendors – and your vendors – make. Request monthly updates from your vendors on their AI/ML strategies and how AI/ML will impact the products and services your buy from them. The focus here should be on process improvement and expense reduction.  New TCO and ROI models should be developed to measure the impact AI/ML will have on your operations and strategy. 

Surveillance is still with us. If you’re into marketing analytics, you love the data regardless of how it’s collected. If you’re concerned about privacy, you have a problem if you’re in any way, shape or form “digital.” Amazon, Facebook, Waze, Instagram, Google, Twitter – you name the platform – is collecting – and monetizing – your data. Privacy remains a conundrum. Convenience creates vulnerability. The very business models of the major digital media platforms require data monetization without which prices would increase where “free” platforms would be forced to charge for their services. How many of us would be willing to pay for Google searches? Location tracking is one form of surveillance that can be semi-controlled. You can turn off location tracking features when you’re not using location apps. But if you shop online, you’re helping vendors learn more and more about who you are, what you like and what you’re likely to buy.


Elizabeth Warren wants broadband for everyone: obviously a good thing, especially since about a quarter of Americans in rural areas have no home Internet. Her idea to create an Office of Broadband Access will help expand broadband a lot, and of course she supports Net Neutrality. These are solid proposals if the government wants to directly or indirectly subsidize access. The counter-argument – that some Americans should not have broadband – is a difficult one to defend.

There’s a wide and deep technology oligarchy in place today. Even the “FTC Chief Says He’s Willing to Break Up Big tech Companies.” Joe Simons says that he’s even prepared to undo past mergers. As I’ve argued in the past, break-ups make sense if leveling the competitive playing field is the objective. If it’s not, then the oligarchy will grow.

Are we prepared for all this?

Thu, 15 Aug 2019 02:41:00 -0500 Steve Andriole en text/html
Q&A: People will only use technology they trust, Microsoft expert says

The rise of artificial intelligence and its ever-increasing presence in our daily lives has sparked a plethora of debates.

Among those, the question of its governance has emerged as one of the most pressing issues of our time, with Brussels at the forefront of the race to regulate AI with its flagship AI Act.

However, regulation does not come without its own hurdles, and solutions are bound to determine the future of tech and Europe’s citizens alike.

At this year's International Artificial Intelligence Summit, organised by Euronews in Brussels on 8 November, Euroviews spoke to Jeremy Rollison, Head of EU Policy and European Government Affairs at Microsoft Europe, about many of the questions around European and global regulatory cooperation, and what it will mean in practice.

Euroviews: At the Brussels Tech Forum in 2018, you named AI as one of two emerging technologies that will have the biggest impact on people and their relationship with tech. How much more is yet to come?

Jeremy Rollison: At Microsoft, we’ve been working on AI for decades. It’s already built into many of our products, and our customers use it every day.

But this year we saw AI accelerate more than ever and become mainstream with the advent of large language models and generative AI. The technology augments human abilities, and is changing everything about how we live, work and learn.

We’re seeing AI aid the research of new medicines, find solutions for accelerating the decarbonising power grids, and enhance cybersecurity.

We also see the technology driving economic growth by supporting the development of new products and services.

So we really see AI as playing a key role in helping address some of society’s most pressing challenges.

Euroviews: You often have an opportunity to talk to decision-makers about the various challenges they encounter in regulating AI. Could you share with us some of the key takeaways on what can be done and what needs to be done to ensure future-proof regulation, especially in the EU?

Jeremy Rollison: There is a balance to strike between supporting Europe’s ability to innovate while ensuring the rights and values of Europeans are protected.

Legislation is needed to get the balance right. As AI technology continues to evolve at pace, an ongoing dialogue between companies, governments, businesses, civil society and academia is important.

AI governance frameworks need to move in lockstep with technology innovation and we need diverse voices around the table to make this happen.

MEPs at the European Parliament plenary in Strasbourg, October 2023 - AP Photo/Jean-Francois Badias

AI governance frameworks need to move in lockstep with technology innovation and we need diverse voices around the table to make this happen.

This is how we minimise risk and maximise opportunity, allowing for people to use the technology safely and unlocking benefits for the whole of society.

Ultimately, it’s about promoting responsible innovation of a technology that we believe will lead to huge economic growth in Europe and beyond.

Euroviews: There has been a lot of talk about future-proofing. Can we really look that far ahead?

Jeremy Rollison: We certainly should be looking ahead. The EU has ambitious plans — take the Digital Decade objectives and European Green Deal for example.

AI will play a key part in engendering this transformation including accelerating the deployment of sustainability solutions and the development of new ones — faster, cheaper, and better.

A great example is the Belgian startup BeeOdiversity which developed an AI-based system used by farmers to measure environmental impact and better protect biodiversity.

What’s important is making sure there are frameworks in place to address potential emerging risks. The pace of innovation is moving so fast and we need safety breaks built into AI systems by design.

We also need frameworks that allow us to respond to emerging challenges quickly, while ensuring the AI ecosystem can thrive and companies in Europe can adopt the technology at scale.

Euroviews: In the past, you’ve advocated for Data for All, making data available for all and not just a few, be it governments or companies, particularly in the context of AI development. How important is this access to data for AI and why?

Jeremy Rollison: Data has a key role in advancing AI technology responsibly.

The value an organisation will get from AI is largely determined by the quality of their data as well as how well governed their data are. Data is powering algorithms and enabling them to learn and make predictions.

Responsible data policies and practices for both inputs and outputs of AI models and applications allow for harnessing AI while protecting user privacy protection, safety, and security.

AI’s ability to process large datasets and provide insights can be crucial in advancing key societal challenges, for example, accelerating progress on climate action.

An instructor gives direction to a student during Hack the Hood boot camp in East Palo Alto, June 2015 - Lea Suzuki/San Francisco Chronicle via AP

The growth of AI makes access to data and large language models more critical than ever. AI models can accomplish a wide variety of tasks using natural language — from drafting the first draft of a presentation to writing computer code.

In addition, AI’s ability to process large datasets and provide insights can be crucial in advancing key societal challenges, for example, accelerating progress on climate action.

AI technologies can be used to analyse energy consumption patterns and optimise the use of renewable energy sources or assist in the efficient management of natural resources and suggest conservation strategies.

Euroviews: What would be the ways to alleviate the concerns of legislators but also citizens when they hear the words “free access to data”? What can be done to assure them that their personal data is safe and will remain private?

Jeremy Rollison: People will only use technology they trust. We believe customers’ data is theirs and theirs alone.

Data processed by AI products is also subject to the GDPR as well as our customer commitments on data privacy and security that often exceed the strong data privacy laws in the EU.

Organisations large and small are deploying AI solutions because they can achieve more at scale, more easily, with the proper enterprise-level and responsible AI protections.

Customers can trust that the AI applications they deploy on our platforms meet the legal and regulatory requirements for responsible AI and that we keep their data secure.

Our mission is to empower customers to achieve more and to enable them to drive their own innovation. Their success is our success.

Wed, 20 Dec 2023 18:59:00 -0600 en-US text/html
Dow Jones Bull Market: 2 Highly Recommended Growth Stocks to Buy Now, According to Wall Street No result found, try new keyword!N EW YORK — The S&P 500 closed out 2023 with a gain of more than 24 percent and the Dow finished near a record high, as easing inflation, a resilient economy, and the prospect of lower interest rates ... Fri, 29 Dec 2023 08:10:00 -0600 en-us text/html Pentagon Will ‘Reconsider’ JEDI Award To Microsoft

A federal judge allowed the government to remand the sourcing decision back to the military to consider issues raised by AWS in its lawsuit alleging President Donald Trump corrupted the process for selecting a commercial cloud provider to modernize IT systems.


A federal judge allowed the United States military on Friday to “reconsider certain aspects” of how it evaluated vendor bids to deliver the controversial JEDI cloud computing contract to Microsoft over Amazon Web Services.

It’s not clear the extent of the intended evaluation, or whether Pentagon officials will look at and address all the issues raised by Amazon Web Services when it sued the United States government after losing the contract to its hyper-scale rival. AWS argues interference from President Donald Trump corrupted award of the potentially $10 billion contract to modernize military IT systems with a commercial cloud provider.

Judge Patricia E Campbell-Smith, who sits on the United States Court of Federal Claims, granted the government’s motion to remand the case back to the Department of Defense for 120 days to allow military officials to look further into Amazon’s complaints regarding evaluation of six technical criteria in the source-selection process.

[Related: AWS Vs. Microsoft: 7 Things To Watch As The JEDI Cloud Saga Unfolds]

Last month, Judge Campbell-Smith granted Amazon’s request for a temporary restraining order that prevented Microsoft from moving forward with task orders and substantial implementation work on the Joint Enterprise Defense Infrastructure contract that has caused a firestorm of controversy in Silicon Valley and in Washington D.C.

A week ago, the judge explained that ruling as based on one likely deficiency involving Microsoft’s ability to provide online storage to the military’s stated specifications, prompting the military to ask for a reassessment phase. By deciding AWS was likely to win on the merits of that single evaluation criteria, Judge Campbell-Smith paused the process without immediately addressing several others AWS argues were improperly evaluated.

Microsoft, which voluntarily joined the federal government as a co-defendant in Amazon’s lawsuit, said that the company supports the Pentagon’s decision to “reconsider a small number of factors as it is likely the fastest way to resolve all issues and quickly provide the needed modern technology to people across our armed forces,” said Frank Shaw, Microsoft’s vice president of communications.

“Throughout this process, we’ve focused on listening to the needs of the DoD, delivering the best product, and making sure nothing we did delayed the procurement process. We are not going to change this approach now,” Shaw said.

An AWS spokesperson said: “we are pleased that the DoD has acknowledged ‘substantial and legitimate' issues that affected the JEDI award decision, and that corrective action is necessary. We look forward to complete, fair, and effective corrective action that fully insulates the re-evaluation from political influence and corrects the many issues affecting the initial flawed award.”

Neither the military in asking for the remand, or Judge Campbell-Smith in allowing it, mentioned the issue of presidential interference. Amazon argues that Trump’s animosity to Amazon CEO Jeff Bezos stemming from his ownership of the Washington Post, a newspaper the president perceives as antagonistic to his administration, corrupted JEDI source selection.

Judge Campbell-Smith noted the Pentagon has indicated the “primary harm it will suffer will be inconvenience and expense, rather than the inability to carry out national security functions” if implementation is delayed.

The judge’s decision to halt the process only addressed Factor 5, Price Scenario 6—a technical matter involving the capability to deliver online storage. She found it likely AWS could prove on at least that criteria that Microsoft proposal didn’t meet the requirements set forth in the military’s solicitation.

But the DoD, in asking for the remand, said it also wanted to “conduct clarifications with the offerors,” regarding the availability of their online marketplace offerings.

The military will also reconsider its decision in regard to “other “technical challenges” that probably won’t need any clarifications from the vendors. And vendors won’t be allowed to modify their proposals during the process, the government’s motion for remand said.

Amazon has repeatedly said it only wants a fair and objective valuation. The world’s largest public cloud provider has argued at least six of eight evaluation factors show flaws and signs of bias.

The ruling to remand the case back to the Pentagon leaves in limbo an AWS motion asking the court to order President Trump, Secretary of Defense Mark Esper and former Secretary of Defense James Mattis to sit for questions in a deposition.

Amazon wants to ask Trump directly about his statement to Mattis to “screw Amazon” out of JEDI, as reported by a Mattis aide.

JEDI began to take shape back in 2017, with the solicitation and source-selection process playing out throughout 2018 and the award announced in 2019.

Military leaders have been eager to begin implementation of the massive cloud transformation project after the multiple delays resulting from the previous legal and administrative challenges.

“Over two years the DoD reviewed dozens of factors and sub factors and found Microsoft equal or superior to AWS on every factor,” Microsoft’s Shaw said. “We remain confident that Microsoft’s proposal was technologically superior, continues to offer the best value, and is the right choice for the DoD.”

Fri, 13 Mar 2020 08:14:00 -0500 text/html
The 10 Coolest Enterprise AI Platforms Of 2021 (So Far)

As demand surges for enterprise AI software, we’ve rounded some of the top platforms from vendors including AWS, DataRobot, Google Cloud, Hewlett Packard Enterprise, IBM and Microsoft.

Enterprise AI Platforms

As Ritu Jyoti, program vice president for AI research at research firm IDC, puts it, the pandemic of 2020 and 2021 has pushed artificial intelligence “to the top of the corporate agenda.” And that’s leading to a surge in demand for enterprise AI platforms, IDC reports. For 2021, revenue in the global AI market is expected to jump by 16.4 percent, to reach $327.5 billion, according to the research firm. And AI software represents the vast majority—88 percent—of total AI market revenues, IDC says.

“AI is becoming ubiquitous across all the functional areas of a business,” Jyoti said in a news release announcing IDC’s AI market projections. “Advancements in Machine Learning, Conversational AI, and Computer Vision AI are at the forefront of AI software innovations, architecting converged business and IT process optimizations, predictions and recommendations, and enabling transformative customer and employee experiences.”

At CRN, we’ve been tracking some of the top enterprise AI and machine learning platforms, from vendors including AWS, Databricks, DataRobot, Google Cloud, Hewlett Packard Enterprise, IBM, Microsoft and SAS.

What follows is our roundup of the 10 coolest enterprise AI platforms of 2021 so far.

For more of the biggest startups, products and news stories of 2021 so far, click here.

Amazon SageMaker

The flagship machine-learning service from AWS, Amazon SageMaker enables developers and data scientists to rapidly build and train machine-learning models and deploy them into production environments. SageMaker offers tools for each step of the machine-learning development lifecycle, including labeling, data preparation, feature engineering, statistical bias detection, auto ML, training, running, hosting, explainability, monitoring and workflows. It provides an integrated Jupyter authoring notebook instance for easy access to data sources for exploration and analysis; common machine learning algorithms optimized to run efficiently against massive data sets in distributed environments; and native support for bring-your-own algorithms and frameworks for flexible distributed training options.

Databricks Unified Data Platform

With an emphasis on scalability, the Databricks Unified Data Platform covers data science, machine learning, analytics and data engineering, and is available on multiple clouds. Databricks allows customers to rapidly experiment and train their models, and then enables quick scaling of the models, as well. The platform offers automanaged and scalable CPU and GPU clusters on multiple cloud platforms, preconfigured with popular machine learning frameworks with built-in optimizations.

Dataiku Data Science Studio

Dataiku’s Data Science Studio provides a single platform for all data science and machine learning tasks, with a focus on multidisciplinary data science teams, collaboration and ease of use. Dataiku caters to customers that have a need for performance metrics that go beyond model accuracy, providing the ability to create custom business metrics optimized to deliver a particular business benefit and to monitor concept drift.

DataRobot Enterprise AI Platform

DataRobot says that its Enterprise AI Platform “democratizes” data science with end-to-end automation for deploying AI applications within an organization. The platform is used to prepare data for machine learning and AI applications; automate the creation of machine learning and time series models; and centrally deploy, monitor, manage and govern production machine learning models. The DataRobot Enterprise AI Platform ​is available on multiple cloud platforms, as well as in on-premises environments or as fully managed service.

Google Cloud Vertex AI

Google Cloud‘s Vertex AI platform is designed to help developers more easily build, deploy and scale machine learning models with pre-trained and custom tooling within a unified artificial intelligence platform. The managed platform brings together AutoML and AI Platform into a unified API, client library and user interface. It requires almost 80 percent fewer lines of code to train a model versus competitive cloud providers’ platforms, according to Google Cloud. The product allows data scientists and ML engineers across ability levels to implement MLOps to build and manage ML projects throughout a development lifecycle.

H2O Driverless AI

Leveraging automation to rapidly accomplish machine-learning tasks,‘s Driverless AI offering is an Automatic Machine Learning (AutoML) platform that aims to provide a full suite of data science capabilities for the enterprise. Key capabilities include automatic feature engineering, which taps into a library of algorithms and to automatically create useful new features for a dataset. Other core capabilities for the H2O Driverless AI platform include model validation, tuning, selection and deployment--as well as time-series, “bring your own recipe” and machine learning interpretability.

HPE Ezmeral

Included in Hewlett Packard Enterprise’s Ezmeral software portfolio are several solutions for enabling enhanced AI and machine learning for customers. The HPE Ezmeral ML Ops solution provides “DevOps-like” agility and speed to machine learning workflows, according to HPE. The solution provides support for all stages of the machine learning lifecycle--across sandbox experimentation, model training, deployment and tracking. Meanwhile, the HPE Ezmeral Data Fabric platform (formerly the MapR Data Platform) brings together technologies for data management and data processing to enable data science, analytics and other advanced enterprise data needs.

IBM Watson Studio

IBM Watson Studio, available in the IBM Cloud Pak for Data offering, is a modular platform offering a wide array of enterprise AI capabilities powered by IBM’s Watson technology. The platform is ideal for enterprises that are seeking to run and manage AI models with greater efficiency, while also simplifying their lifecycle management for AI deployments, according to IBM. Ultimately, IBM Watson Studio is a modern solution that “leverages [IBM’s] roots in SPSS, ILOG CPLEX and other earlier products, complemented by a stream of innovations from IBM research.”

Microsoft Azure Machine Learning

With the aim of enabling enterprises to build and deploy models more quickly, Microsoft’s Azure Machine Learning offering is a solution for “all skill levels” using the Jupyter Notebook open document format, drag-and-drop functionality and automated machine learning capabilities. Azure Machine Learning offers “end-to-end” MLOps for creation and deployment of models, leveraging automated workflows, as well as support for open-source technologies including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow and Python. Recent enhancements have included the introduction of Azure Machine Learning managed endpoints, which automates the creation and management of the necessary compute infrastructure.

SAS Visual Data Mining and Machine Learning

As SAS’ integrated solution for solving complicated analytical problems, the SAS Visual Data Mining and Machine Learning offering is a “comprehensive” visual and programming interface that leverages the company’s cloud-enabled, in-memory Viya analytics engine. SAS Visual Data Mining and Machine Learning enables machine learning processes and data mining for users across skill levels, with capabilities including automatically generated insights for identifying common variables across models.

Thu, 08 Jul 2021 06:00:00 -0500 text/html

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