DataBloom

# inputs: A 3D tensor with shape [batch, timesteps, feature]. # https://keras.io/api/layers/recurrent_layers/lstm/ dropout = 0.1 recurrent_dropout = 0 # 0 for cudnn model.add(LSTM(25, input_shape=(x_shape[1:]), activation='tanh', return_sequences = True, dropout=dropout, recurrent_dropout=recurrent_dropout)) #model.add(BatchNormalization()) # (A) model.add(LSTM(15, activation='tanh', return_sequences = False, dropout=dropout, recurrent_dropout=recurrent_dropout)) #model.add(BatchNormalization()) # (B) model.add(Dense(10, activation='gelu')) model.add(BatchNormalization()) # (C) #model.add(Dropout(0.1)) model.add(Dense(1, activation='sigmoid')) #model.add(BatchNormalization()) # (D) loss = tf.keras.losses.MeanAbsoluteError() model.compile(loss = loss, metrics=["accuracy",tf.keras.metrics.MeanAbsoluteError()], optimizer=tf.keras.optimizers.Adam())#lr=LEARN_RATE_LSTM, decay=LEARN_RATE_LSTM_DECAY return model 

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Your next trip to the dentist might offer a taste of AI. Pearl, a West Hollywood startup, provides AI for dental images to assist in diagnosis. It landed FDA clearance last month, the first to get such a go-ahead for dentistry AI. The approval paves the way for its use in clinics across the United Read article >

The post Tooth Tech: AI Takes Bite Out of Dental Slide Misses by Assisting Doctors appeared first on NVIDIA Blog.

The gods must be smiling this GFN Thursday — God of War today joins the GeForce NOW library. Sony Interactive Entertainment and Santa Monica Studios’ masterpiece is available to stream from GeForce NOW servers, across nearly all devices and at up to 1440p and 120 frames per second for RTX 3080 members. Get ready to Read article >

The post GFN Thursday Is Fit for the Gods: ‘God of War’ Arrives on GeForce NOW appeared first on NVIDIA Blog.

model = Sequential() model.add(Dense(10000, input_dim=212207, kernel_initializer='normal', activation='relu')) model.add(Dense(100, activation='relu')) model.add(Dense(1, kernel_initializer='normal')) model.compile(loss='mean_squared_error', optimizer='adam') for i in range(10000000): #X is input with 212207 values #Y is a output value if i<6000000: model.fit(X.transpose(), Y, epochs=30, batch_size=1, verbose=0) else: prediction=model.predict(X.transpose()) 

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Is there an ideal network design that always works? Let’s find out. This blog covers the pros and cons of pure layer 3, layer 2 only, and VXLAN and EVPN.

Network administrators have a hard job. They are responsible for ensuring connectivity for all users, servers, and applications on their networks. They are often tasked with building a network design before getting application requirements, making a challenging project even more difficult. In these scenarios, it’s logical for networking admins to try to find an ideal network design they can use with any set of applications. 

There is no one-size-fits-all network solution that will work every time, and every design has benefits and drawbacks. In this post, we analyze three different network types that could be perceived as ideal. Then, we describe where each falls short, based on real-world factors.

The candidates are:

• Pure layer 3
• Layer 2 only
• Overlay with VXLAN and EVPN

Ready? Let’s get started.

Pure layer 3 design

Many forward-thinking architects think pure layer 3 (L3) is the ideal design due to its simplicity and reliance on only one protocol stack. All traffic is routed and balanced at the L3 level using equal-cost multipath and endpoint redundancy is achieved through a natively functional anycast address solution. It’s simple and elegant. 

Many large web-scale IT companies choose it for its excellent operational efficiency. It also gives them robust control over their application environment to design applications that work within this design.

Applications that rely on network overlays or pure routing are optimized for the L3 architecture. Whether using a container-based solution that leverages routing as its mechanism to provide access to the environment, or a Container Network Interface to encapsulate the container-to-container communication, these solutions work great on this architecture. 

The advent of SmartNICs and DPUs makes L3 more user-friendly by providing host-based solutions to offload resource-intensive tasks such as storing routing tables, performing packet encapsulation, and doing NAT.

The biggest drawback with L3 is that it doesn’t allow any distribution of layer 2 (L2) adjacency. Over time, most enterprises must introduce an application that requires L2 adjacency, either within or between racks. Historically, developers have been unreliable in writing their applications to handle clustering using L3 capabilities. Instead of using DNS or other L3 discovery processes, many legacy applications use L2 broadcast domains to discover and detect nodes to join the cluster. A pure L3 solution struggles to service software that requires such an environment because each L2 domain is limited to one node or one server.

Layer 2 only design

The L2 only solution is the opposite of pure L3. L2 only primarily leverages VLANs for segregating its connectivity and relies on legacy features like MLAG and spanning tree protocol (STP) to provide a distributed solution. The L2-only solution still has a place in network environments, typically in simple, static environments that don’t require scale. 

People are comfortable with L2, as it uses tried-and-true technologies familiar to most people. It’s simple in the protocol stack, making all forwarding decisions based on only the first two layers of the OSI model. Also, most low-cost network devices on the market are capable of these feature sets.

However, L2 has gaps in scale and performance. Relying on STP across three tiers to prevent loops, leads to inefficient redundant paths. To circumvent this limitation in spanning-tree convergence, you can try deploying back-to-back MLAG. MLAG is not as efficient as a pure layer 3 solution at handling device failures and synchronizing control planes, however. L2 networks tend to limit broadcast and multicast traffic. These are just a few limitations that create a hidden cost of ownership around deploying an L2 only design.

Overlay design: VXLAN and EVPN

The most common design in the enterprise data center is VXLAN as the transport layer encapsulation technology, with EVPN as the control plane technology. This architecture provides the greatest flexibility, with all the benefits of a pure layer 3 solution, and provides the network administrator the adaptability to support applications requiring L2 to function. 

It provides the benefits of L2 adjacency without introducing inefficient protocols such as STP and MLAG. Leveraging EVPN as the L2 control plane and multihoming as the optimal alternative to MLAG, overlay solutions solve many inefficiencies with L2.

A one-size-fits-all solution like VXLAN and EVPN could be thought of as ideal, but even this has drawbacks. Its detractors point to the multiple layered protocols required to make it operate. The solution builds on a BGP-enabled underlay with EVPN configured between the tunnel endpoints. VXLAN tunnels are configured on top of the overlay with varying levels of complexity depending on tenancy requirements. This may include integrating with VRFs, introducing L3 VNIs for intersubnet communication, and the reliance on border leafs for intertenant communication through VRF route leaking. Combining all these technologies can create a level of complexity that makes troubleshooting and operations difficult.

Conclusion

Everything has tradeoffs, whether you’re sacrificing operational complexity for network simplicity or trading application control for flexibility. The upside of accepting that there is no perfect network design is that you are now free to pick and choose the architectures and workflow that best fit your network. Work with your application and infrastructure teams to identify the server requirements, optimize your workflows, and select the best solutions for your applications’ needs.

Learn about NVIDIA Networking Solutions.

To scale AI for financial services, companies must face several resource hurdles. The full-stack solution from NVIDIA and VMware helps you leverage the competitive advantages of AI.

AI adoption has grown rapidly over the past few years due to its ability to automate repetitive tasks and increase revenue opportunities. Yet many companies still struggle with how to meaningfully scale AI in financial services. Increased data needs and a lack of internal talent are a few issues.

In this post, we provide a landscape overview of AI use cases and highlight some key scaling challenges, along with how leading banks and financial institutions are using end-to-end solutions to mobilize AI adoption.

What drives the use of AI in financial services?

The power of AI to solve complex business problems is widely recognized in the financial services industry, an early adopter of data science. Banking is among the top three industries in annual investment in big data and analytics. An NVIDIA survey found that 83% of C-suite leaders, IT architects, and developers in financial institutions believe that AI is vital to their future success.

It is easy to understand why: Business Insider estimates that the potential for AI-driven cost savings for banks will reach $447 billion by 2023. Across consumer finance and capital market sectors, firms are looking for ways to mine that potential.

Improved fraud detection and prevention rank highest among proven AI use cases, especially with online-payment fraud losses expected to hit $48 billion annually by 2023.

Other business drivers include maximizing client returns through portfolio optimization or creating personalized customer service through call center transcription. The list continues to grow.

A recent paper by McKinsey and Company underscores the coming sea change: to thrive, banks need to operate “AI first.” Therefore, realizing AI’s value does not come from single projects or teams but instead requires collaboration among data engineers, data scientists, and app developers across the financial institution, all supported by an AI-enabled IT infrastructure.

Finance industry use cases

The quickest returns often stem from improved fraud detection and prevention, the most cited use of AI in financial services. AI is also beneficial for creating efficiencies and reducing compliance costs related to anti-money laundering efforts and know-your-customer regulations.

When combining risk management with profit maximization, investment firms increasingly look to AI for building algorithmic trading systems, which are powered by advances in GPUs and cloud computing. Analysts involved in asset management and portfolio optimization can use natural language processing (NLP) to extract more relevant information from unstructured data faster than ever before, reducing the need for labor-intensive research.

Within consumer finance, NLP helps retail banks gain a better understanding of customer needs and identify new marketing opportunities through call center transcription. Along with chatbots and virtual assistants, transcription falls within the larger category of conversational AI, which helps personalize service by creating 360^o views of customers.

Another use of AI within customer engagement is recommendation engines. This technology improves cross-selling by leveraging credit usage, scoring, and balances to suggest fitting financial products.

Key challenges of scaling AI

Banking, trading, and online-payment firms that do not adopt AI and machine learning (ML) risk being left behind, but even companies that embrace the technology face hurdles. In fact, almost half of AI projects never make it to production.

Elevated costs

One fundamental concern: AI demands exceed the existing IT infrastructure for most companies. The tooling that data scientists require for AI workloads is often difficult to deploy onto legacy systems. 28% of IT pros rate the lack of infrastructure as the primary obstacle to adoption. The proliferation of AI apps is also making IT management increasingly difficult.

Shadow IT

Financial institutions that invest in AI only within a research lab, innovation team, or line of businesses frequently have data scientists operating on customized bare-metal infrastructure. This setup produces silos in data centers and leads to shadow IT existing outside departmental IT control.

Given that structure, it is easy to see why 71% of banking executives struggle with how to scale AI, and 32% of IT pros rank data silos and data complexity as the top barrier to adoption.

These silos further hinder productivity. Rather than focusing on training ML models, data scientists spend more time as de facto IT managers for their AI fiefdoms.

Furthermore, if that infrastructure does not include accelerated computing and the parallel processing power of GPUs, deep learning models can take days or weeks to train, thereby wasting precious time for data scientists. It is like hiring world-class race car drivers and equipping them with Yugos.

Unused infrastructure

As expected, costs begin to spiral. Disparate IT increases operational overhead. Inefficient utilization of infrastructure from silos results in higher costs per workload when compared with the cloudlike efficiency achieved by allocating resources on demand through virtualization. Expensive data scientists are forced to manage IT, and ROI slows. AI projects languish.

AI-driven platforms offered by NVIDIA and VMware

So how can financial institutions fully adopt AI, scale what they have implemented, and maximize its value?

Built on NVIDIA GPUs and VMware’s vSphere with Tanzu, NVIDIA AI Enterprise offers an end-to-end platform with a suite of data science tools and frameworks to support diverse AI applications. Crucially, it also helps reduce time to ROI while overcoming problems posed by ad hoc implementations.

NVIDIA and VMware’s full-stack solution is interoperable from the hardware to the application layer, providing a single platform for both financial services apps and AI workloads. It eliminates separate AI infrastructure, as it is developed on common NVIDIA-Certified Systems with NVIDIA GPUs and VMware vSphere with Tanzu, a cloud computing platform that already exists in most data centers.

This enterprise suite offers the AI tooling of TensorFlow, PyTorch, and RAPIDS, while vSphere’s Tanzu Kubernetes Grid can run containerized apps orchestrated with Kubernetes alongside virtualized apps, creating consistent infrastructure across all apps and AI-enabled workloads.

That consistency pulls data science out of silos and brings deployment into the mainstream data center, joining the IT team with data scientists and developers. Expensive devices like GPUs and accelerators can be shared using NVIDIA virtual GPUs, driving down the total cost of ownership while providing the speed-up needed to train ML models quickly. Meanwhile, data scientists are freed from IT management to focus on their specialized work.

In other words, IT leads get simplicity, manageability, easy scaling, and deployment; data scientists are supported with infrastructure and tooling that facilitates their core work; and business executives see quicker and greater ROI.

Summary

These applications merely scratch the surface of what is possible with AI in financial services, especially as the technology matures. To scale AI for financial services, companies can increase resource capacity, hire domain-specific expertise, and find innovative ways to govern their data.

The full-stack solution from NVIDIA and VMware provides a platform to leverage the competitive advantages of AI across entire firms while overcoming common implementation challenges.

For more information about how NVIDIA AI Enterprise can activate those uses, see the NVIDIA AI-Ready Enterprise Platform for Financial Services solution brief and the AI and HPC solutions for financial services page.

See what the platform can do for you with immediate trial access to the NVIDIA AI Enterprise software suite LaunchPad, which runs on private accelerated computing infrastructure in VMware vSphere environments and includes a curated set of hands-on labs for AI practitioners and IT staff.