Tag: machine learning

AWS re:Invent Recap: Amazon SageMaker Clarify

Posted on by Idexcel Technologies

Amazon SageMaker Clarify

What happened?

AWS released Amazon SageMaker Clarify, a new tool for mitigating bias in machine learning model that helps customers more accurately and rapidly detect bias to build better solutions. This provides critical data and insights that increase transparency to help support analysis and explanation of model behavior to stakeholders and customers.

Why is it important?

  • Easily Detect Bias: SageMaker Clarify will help data scientists detect bias in data sets before training and their models after training.
  • Valuable Metrics & Statistics: It explains how feature values contribute to the predicted outcome, both for the model overall and for individual predictions.
  • Build Better Solutions: With the capability for developers to specify important model attributes, such as location, occupation, age, teams are better able to focus the set of algorithms in a sophisticated way to detect any presence of bias in those attributes. This enables teams to build the most accurate and effective solutions that drive client success.

Why are we excited?

With Amazon SageMaker Clarify, we can now better understand each feature in our ML models and give more detailed explanations to stakeholders. It provides transparency in model understanding that gives leadership more valuable information to inform critical business decision-making. SageMaker Clarify also includes feature importance graphs that explain model predictions and produce reports for presentations to better highlight any significant business impacts.

Availability

SageMaker Clarify is available in all regions where Amazon SageMaker is available. The tool will come free for all current users of Amazon SageMaker.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

AWS re:Invent Recap: Machine Learning Keynote

Posted on by Idexcel Technologies

Here are the key announcements from the re:Invent 2020 Machine Learning Keynote:

  1. Faster Distributed Training on Amazon SageMaker is the quickest and most efficient approach for training large deep learning models and datasets. Through model parallelism and data parallelism, SageMaker distributed training automatically splits deep learning models and datasets for training in significantly less time across AWS GPU instances.
  2. Amazon SageMaker Clarify detects potential bias during all phases of the data preparation, model training, and model deployment, giving development teams greater visibility into their training data and models to resolve potential bias and explain predictions in greater detail.
  3. Deep Profiling for Amazon SageMaker Debugger gives developers the capability to train models at a quicker pace by monitoring system resource utilization automatically and providing notifications of training bottlenecks.
  4. Amazon SageMaker Edge Manager: provides developers the tools to optimize, secure, monitor, and maintain ML model management on edge devices like smart cameras, robots, personal computers, and mobile devices.
  5. Amazon Redshift ML empowers data analyst, development, and scientist teams to create, train, and deploy machine learning (ML) models using SQL commands. Teams can now build and train machine learning models from Amazon Redshift datasets and apply them to use cases.
  6. Amazon Neptune ML leverages Graph Neural Networks (GNNs) to make easy, fast, and more accurate predictions using graph data. The accuracy of most graph predictions increases to 50% with Neptune ML when compared to non-graph prediction methods. The selection and training of the best ML model for graph data are automated and lets users run ML on their graph directly using Neptune APIs and queries. ML teams can now create, train, and apply ML on Neptune data, reducing the development time from weeks down to a matter of hours.
  7. Amazon Lookout for Metrics applies ML to detect metrics anomalies in your metrics to perform proactive monitoring of the health of your business, issue diagnosis, and opportunity identification quickly that can save costs, increase margins, and improve customer experience.
  8. Amazon HealthLake leverages ML models to empower healthcare and life sciences organizations to aggregate various health information from different silos and formats into a centralized AWS data lake to standardize health data.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

AWS 2020 re:Invent Recap: AWS Trainium

Posted on by Idexcel Technologies

What Happened: 

The newest AWS custom-designed chip, AWS Trainium, was announced during Andy Jassy’s 2020 re:Invent keynote, with the projected best price performance for training Machine Learning (ML) models in the cloud. Meant for deep learning training workloads for applications, it includes capabilities of image classification, semantic search, translation, voice recognition, NLP (Natural Language Processing), and recommendation engines.

Why It’s Important: 

  • Lower Costs: AWS Trainium instances are specifically targeted for reducing the training costs, in addition to the existing savings through using AWS Inferentia, which focuses on the inference factors of ML applications.
  • Easy Integration: Since it shares the same AWS Neuron SDK as AWS Inferentia, it’s easier for developers with Inferentia experience to start working with AWS Trainium. SDK integrates with popular ML frameworks like Pytorch, MXNet, and Tensorflow, making it easier for developers to move GPU instances to AWS with minimal code changes.
  • Greater Capabilities: This chip is optimized for training deep learning models for applications using images, text, and audio, which means more opportunities to build solutions that solve operational business challenges across industries.

Why We’re Excited

AWS Trainium will be the most sophisticated and advanced training technology leveraged to deliver elegant solutions to address customer challenges and project requirements. Since it integrates with and complements AWS Inferentia, ML training capabilities will significantly increase with optimized speed, skill, and efficiency. As the most cost-effective option with the broadest set of capabilities and robust AWS toolset to support it, end-to-end workflows can be created to scale AI/ML training workloads faster and bring products or services to market at an accelerated rate.

Availability:

AWS Trainium is available as an EC2 instance and will be available in Amazon Sagemaker in the 2nd half of 2021.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

AWS re:Invent Recap: Habana Gaudi Based EC2 System

Posted on by Idexcel Technologies

What Happened: 

With machine learning workloads growing rapidly and faster machines needed for training models in the cloud, Andy Jassy announced Habana Gaudi-based Amazon EC2 Instances will be available in the first half of 2021. Powered by new Habana Gaudi pre-processors from Intel, users can expect a 40% better price/performance over the current GPU based EC2 instances. Built specifically for ML training, these instances work seamlessly with TensorFlow and Pytorch for training deep learning models.

Why It’s Important: 

  • Competitive Edge: This has solidified AWS as the top choice for cloud-based systems suitable for large Machine Learning workloads. 
  • Cost: With 40% better price/performance, organizations, and partners will benefit from significant cost savings.
  • Easily Integrated: using Habana Gaudi instances integrates with and natively supports common frameworks already used such as TensorFlow and PyTorch.
  • Seamless Transition: These instances are used with familiar tools and technology like AWS Deep Learning AMIs, Amazon EKS and ECS for containerized applications, and Amazon SageMaker.

Why We’re Excited

Developers now have the power to build new training models or port existing models from graphics processing units to Gaudi accelerators using Habana’s SynapseAI Software Suite. This reduces the cost of training AI models at scale, allowing for a rapid and sophisticated training process that results in a faster, more optimized delivery of solutions.  

Availability 

Habana Gaudi EC2 instances will be available in the first half of 2021. Sign up here for early access.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

AWS re:Invent Recap: SageMaker Data Wrangler

Posted on by Idexcel Technologies

What happened?

The new service, SageMaker Data Wrangler, was announced during Andy Jessy’s 2020 re:Invent Keynote. Incorporated into AWS SageMaker, this tool simplifies the data preparation workflow so the entire process can be done from one central interface.

Why is it important?

  • SageMaker Data Wrangler contains over 300 built-in data transformations to normalize, transform, and combine features without having to write any code.
  • With SageMaker Data Wrangler’s visualization templates, transformations can be previewed and inspected in Amazon SageMaker Studio.
  • Data can be collected from multiple data sources and imported in one single go for data transformations.
  • Data can be in various file formats, such as CSV files, Parquet files, and database tables.
  • Data preparation workflow can be exported to a notebook or a code script for Amazon SageMaker pipeline or future use.

Why We’re Excited

SageMaker Data wrangler makes it easier for data scientists to prepare data for machine learning training using existing pre-loaded data preparation options. With preparation completed more quickly, our data science teams can accelerate the delivery of solutions to clients at a much faster pace.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

6 Business Continuity Strategies to Implement Post COVID-19

Posted on by Idexcel Technologies

The health crisis of COVID-19 impacted businesses, people, and communities in numerous ways, causing us to change our strategies and the way we live going forward. This means that businesses are adapting to an incredibly new business landscape that’s changing the way we will work for the foreseeable future. Organizations are challenged with reinventing strategies, enabling virtual teams with remote workspaces, and exploring what’s possible for creating new innovations. Here are some key strategies to implement to accelerate business continuity and transition to a new working world:

Establish Your Team Leaders

The greatest asset any organization has are its people. Choose the team members with proven reliability, organization skills, and strong leadership qualities, especially under pressure. Situations like COVID-19 can prove to be stressful, so it is wise to choose to your Business Continuity team with these things in mind. Some roles you might consider designating specifically for Business Continuity purposes are Executive Business Continuity Manager (overall Team Lead), Communication Lead, IT Lead, Human Resources, Facilities/Maintenance, and Operations/Logistics Lead. These roles can depend on your specific business needs and internal departmental breakdown. Once you’ve decided your key players, it’s time to evaluate the primary business processes that need to continue in case of business disruption.

Document & Identify Critical Processes

From internal human resources processes like payroll processing, retirement plan administration, healthcare benefits to business operations such as supply chain management, customer support, operational processes, each of these requires certain access to various technology and secure applications. It is important to know if these processes will still be able to be performed with the current systems architectures and IT tools in place. That leads us into the next strategy, where we connect each process with existing resources in place to determine if the business continuity plan being developed will need specific changes, updates, or additions.

Identify Key Technology and Tools

Performing a proper assessment of current tools and technologies to validate capability will reveal where there might be gaps that need to be filled. One key question to consider is “Will these tools and technologies we currently have in place work in the case of a future change in working environment?” The answer to this will help identify what potential technologies or tools that might be needed in order to continue seamlessly operating with minimal disruption. Need help strategizing? Learn more about how to leverage cloud technology to improve business operations and increase performance efficiencies here.

Consider Contingency Technology and Tools

Is your system architecture set up for a new working structure for virtual teams? Is your cloud strategy crystal clear and strong enough to handle changing needs in terms of scalability and operations? Is it ready in case of another change in the working environment or future disaster? For example, it might be necessary to set up virtual workspace situations for employees. As a preferred AWS partner, Idexcel can help implement AWS Workspaces solutions in your organization – enabling business continuity by providing users and partners with a highly secure, virtual Microsoft Windows or Linux desktop. This setup grants your team access to the documents, applications, and resources they need, anywhere, anytime, from any supported device. Learn more about how we can help do that here.

Build A Customer Communication Plan

Communication with your staff, clients, and partners is perhaps the most important element of these strategies. The more they hear from you, the better off you will be with establishing trust and reliability. When communicating, be sure to follow these 3 guidelines:

1. Timing is everything. Responding quickly is key to establishing trust, visibility, and proactivity. It’s critical to be timely with messaging and depending on that communication sent, to give proper response and planning time to the recipient.

2. Be clear, concise, authentic, and provide value. Keep your communications simple and to the point. Create messaging that provides value, help, and support during any business changes or possible disruptions. Another key tip: keep it positive and avoid the use of negative words to evoke a more positive feeling and reaction to the communication. The more authentic and personable the messaging, the more likely you are to receive a positive response and invoke a sense of comfortability.

3. Leverage all communication channels. Social Media is a great way to keep in touch with your audience. Employees, clients, and partners alike are all very active, especially on LinkedIn, given it’s a key point of communication and connection digitally among professionals. Keep up with email communication with your teams internally as well, checking in often and also checking in how it may have impacted them.

Set Your Organization Up for Innovation

With a Business Continuity plan in place and the team assembled, now might be the time to consider strategically planning for innovative solutions. Specific technologies can be implemented to ensure accelerated business continuity measures are in place to better set your business and teams up for success.

For example, many organizations are adopting Machine Learning solutions with RPA (Robotic Process Automation). Many websites are using chatbots for answering general FAQs asked by the customers, eliminating the need for personnel to respond, and enabling them to focus on other tasks. They can positively impact the customer’s experience and are an ideal tool for short-staffed employers, saving thousands of hours of productivity and cost.

If you need help strategizing and creating your business continuity plan, get in touch with us to get connected with an expert.

Is Machine Learning the Solution to Your Business Problem?

Posted on by Idexcel Technologies

The term Machine Learning (ML) is defined as ‘giving computers the ability to learn without being explicitly programmed’ (this definition is attributed to Arthur Samuel). Another way to think of this is that the computer gains intelligence by identifying patterns and data sets on its own, improving output accuracy over time as more data sets are examined. Since ML can be a challenging solution to implement, we’ve put together some foundational steps to assess the feasibility of building an ML solution for your organization: 

1. Identify the problem TYPE 

Start by distinguishing between automation problems and learning problems. Machine learning can help automate your processes, but not all automation problems require learning.

Automation: Implementing automation without learning is appropriate when the problem is relatively straightforward. These are the kinds of tasks where you have a clear, predefined sequence of steps currently being executed by a human, but that could conceivably be transitioned to a machine.

Machine Learning: For the second type of problem, standard automation is not enough – it requires learning from data. Machine learning, at its core, is a set of statistical methods meant to find patterns of predictability in datasets. These methods are great at determining how certain features of the data are related to the outcomes you are interested in.

2. Determine if you have the right data

The data might come from you, or an external provider. In the latter case, make sure to ask enough questions to get a good feel for the data’s scope and whether it is likely to be a good fit for your problem. consider your ability to collect it, its source, the required format, where it is stored, but also the human factor. Both executives and employees involved in the process need to understand its value and why taking care of its quality is important. 

3. Evalute Data Quality and Current State

Is the data you have usable as-is, or does it require manual human manipulation before introducing into the learning environment? A solid dataset is one of the most important requirements for building a successful machine learning model. Machine learning models that make predictions to answer their questions usually need labeled training data. For example, a model built to learn how to determine borrower due dates to improve accurate reporting needs a starting point from which to build an accurate ML solution. Labeled training datasets can be tricky to obtain and often require creativity and human labor to create them manually before any ML can happen.

4. Assess Your Resources

Do you have the right resources to maintain your ML solution? Once you have an appropriate question and a rich training dataset in hand, you’ll need people with experience in data science to create your models. Lots of work goes into figuring out the best combination of features, algorithms, and success metrics needed to make an accurate model. This can be time-consuming and requires consistent maintenance over time.

5. Confirm Feasibility of ML Project

With the four previous steps for assessing whether or not ML is right for your organization, consider the responses. Is the question appropriate for building an ML business solution? Is the data available, or at least attainable? Does the data need hours of human labor? Do you have the right skilled team members to carry out the project? And finally, is it worth it – meaning, will the solution have a large impact, financially and socially? 

It’s important to consider these key questions when assessing whether or not Machine Learning is the right solution for your organization’s needs. Connect with our ML experts today to schedule your free assessment. 

The Future of Machine Learning

Posted on by Idexcel Technologies

Technology is innovating and revolutionizing the world at a rapid pace with the application of Machine Learning. Machine learning (ML) and Artificial Intelligence (AI) might appear to be the same, but the reality is that ML is an application of AI that enables a system to automatically learn from data input. The functional capabilities of ML drive operational efficiency and capacity automation in various industries.

Technological Innovation for Convenience
Workforce handling is tedious and less productive; this is where Artificial Intelligence has lucratively overcome the age-old system of manual labor. With the world moving at such a fast pace, monitoring has become a constraint for most organizations; for this very reason, Artificial Intelligence and Machine Learning are used more as tools of convenience rather than just pieces of technology.

We have seen how accounting systems have replaced ledger books. At the same time, processes have been set up to align machines with organizational requirements effectively to balance everyone’s demands.

However, with the way Artificial Intelligence is advancing, it seems this technology is quickly going to change the way processes are functioning. Not only trends on social media will be affected, but even marketing will see a complete makeover through the use of Artificial Intelligence.

The Effect on Various Fields
When it comes to Artificial Intelligence, everybody wants a taste of it. From marketing experts and tech innovators to education sector decision-makers, Artificial Intelligence holds the capability to pave the path for a healthy future. Artificial Intelligence has been designed to provide utmost customer satisfaction. To derive maximum results from the nuances of AI customer-centric processes will need to align their business metrics to the logic of this latest technology.

As Big Data evolves, machine learning will continue to grow with it. Digital Marketers are wrapping their heads around Artificial Intelligence to produce the most efficient results by putting in minimal efforts. The entire algorithm and the build of Artificial Intelligence will be used to predict trends and analyze customers. These insights are aimed at helping marketers build patterns to drive organizational results. In the future, it seems like every basic customer need would be taken care of through fancy automation and robotic algorithms.

Healthcare Sector
The healthcare industry is one of the widely reckoned industries in the world today. Simply put, it has the maximum effect on today’s society. Through the use of Artificial Intelligence and Machine Learning, doctors are hoping to be able to prevent the deadliest of diseases, which even includes the likes of cancer and other life-shortening diseases.

Robots Assistants, intelligent prostheses, and other technological advancements are pushing the health care sector into a new frenzy, which will be earmarked towards progressing into a constantly evolving future.

Financial Sector
In the financial sector, it’s vital to ensure that companies can secure their operations by reducing risk and increasing their profits. Through the use of extensive Artificial Intelligence, companies can build elaborate predictive models, which can successfully mitigate the potential of on-boarding risky clients and processes; this can include signing on dangerous clients, taking on risky payments, or even signing up on hazardous loans.

No matter what might be the company’s requirement, Artificial Intelligence is a one-stop shop when it comes to preventing fraudulent activities in day to day operations – this, in turn, will lead to money savings possibilities, profit enhancement and risk reduction within every organizational vertical.

Robotics
We are steadily heading towards a future that will be marked complete with the rise of robotics and automation; this is not going to be restricted to the medical sector only; intelligent drones, manufacturing facilities, and other industries are also going to be benefited by the rise of robotics. Artificial Intelligence methodologies like Siri and Cortana have already seen the light of day – this is just the beginning. More and more companies are going to take these capabilities to a new level.

As more and more military operations begin to seek advantages from the likes of mechanized drones, it won’t be long before e-commerce companies like Amazon start to deliver their products through the use of drones. The potential is endless, and so are the possibilities. In the end, it is all about using technology in the right manner to ensure the appropriate benefits are driven in the right direction.

Also Read

How Artificial Intelligence Transforming Finance Industry
Artificial Intelligence to Make DevOps More Effective
How Big Data Is Changing the Financial Industry