Calsoft’s Products and Platforms are based on artificial intelligence (AI), machine learning (ML), and continual deep learning (DL), which are the next wave of technology transforming how consumers and enterprises work, learn, and play.  AI has grabbed the center stage of business intelligence, despite having been around for decades, due to the growing pervasiveness of data, the scalability of cloud computing, the availability of AI accelerators, and the sophistication of the ML and DL algorithms.

The Internet of Things (IoT), mobile devices, big data, artificial intelligence (AI), machine learning (ML), and continual deep learning (DL) all combine to continually sense and collectively learn from an environment. Calsoft leverages Digital Transformations to deliver meaningful, value-added predictions and actions for improving industrial processes, healthcare, experiential engagement, or any other kind of enterprise decision making.

Calsoft’s business objectives are balanced between tactical and strategic, and they can range from improvement in operational efficiencies to increasing competitive differentiation, from maximizing product revenue to launching new digital revenue streams.

Presently about 40% of DX initiatives will use AI services; by 2021, 75% of commercial enterprise apps will use AI, over 90% of consumers will interact with customer support bots, and over 50% of new industrial robots will leverage AI.

By 2020, 85% of new operation-based technical hires will be screened for analytical and AI skills, enabling the development of data-driven DX projects. At the same time, we continuously enhance an integrated enterprise digital platform that will enable a new operating and monetization model. The IT organization will need to be among the enterprise's best and first use-case environments for AI – in development, data management, and cybersecurity.

Calsoft Model and Workload Deployments:

Challenges and Needs AI is changing the way business processes are carried out in the digital era. While the power and promise of AI is exciting, deploying AI models and workloads is not easy. At issue is the fact that ML and DL algorithms need huge quantities of training data (typically ranging from 8-10 times the volume used for traditional analytics), and AI effectiveness depends heavily on high quality, diverse, and dynamic data inputs. Historically, data analytics centered around large files, sequential access, and batched data. Modern data sources and characteristics are different. Today, data consists of small to large files and structured, semi structured, and unstructured content. Data access varies from random to sequential.

By 2025, more than a quarter of the global data set will be real time in nature, and real-time IoT data will make up more than 95% of it. In addition, data is increasingly distributed across on-premises, colocation, and public cloud environments.

Poor data quality has a direct correlation to biased and inaccurate model buildout. Ensuring data quality with large volumes of dynamic, diverse, and distributed data sets is a difficult task as it is hard for the developers to know, predict, and code for all the appropriate checks and validations. To address these challenges, enterprises desire an autonomous data quality and validation solution. Such a solution would automatically learn data's expected behavior, create thousands of data validation checks without the need for coding, update and maintain the checks over time, and eliminate both anticipated and unanticipated data quality errors to make the data more trustable and useable.

Both AI engineers and data scientists – will be needed to support the growing segment of AI-dependent DX initiatives.

Data scientists are big data wranglers and model builders. They take a mass of data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage, and organize the data. They then apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.

A data scientist may be required to:

  • Extract, clean, and prune huge volumes of data from multiple internal and external sources

  • Employ sophisticated analytics programs, ML, and statistical methods to prepare data for use in predictive and prescriptive modeling

  • Explore and examine data to determine hidden weaknesses, trends, or opportunities

  • Invent or build new algorithms and models to solve problems and build new tools to automate work

  • Train, optimize, and deploy data-driven AI models at scale to the most pressing challenges

  • Maintain the accuracy of AI models

  • Communicate predictions and findings to management and IT through effective data visualizations and reports


Data engineers/administrators build massive reservoirs for data. They develop, construct, test, and maintain architectures such as databases and large-scale data processing systems. Once continuous pipelines are installed to and from these huge "pools" of filtered information, data scientists can pull relevant data sets for their analyses.

Data/IT architects integrate AI frameworks into infrastructure deployment strategy and are responsible for supporting the scale, agility, and flexibility of the environment.

While choosing an AI solution, AI team must identify security, cost effectiveness, and operationalization (building, tuning, optimizing, training, deployment, and inferencing) of data models/intelligence.

Data Scientists' Productivity - Building, testing, optimizing, training, inferencing, and maintaining the accuracy of models is integral to AI workflow. These neural-network models are hard to build. To build, test, and deploy large-scale ML/DL models, data scientists normally use a variety of tools like RStudio and Spark with open source frameworks like Tensorflow and Caffe, as well as programming software like R or Python. Building and optimizing models can require manually testing thousands of combinations of hyperparameters.

Training models may take weeks or months to complete in some use cases.


Data scientists look for efficiency and automation in cleaning data from multiple sources and reducing the noise. They also need help in building, tuning, and choosing the right features for the model, including simplification in determining hyperparameter settings.

Data scientists look for speed and agility in iterative and cyclic processes. They also look for superior performance and flexible scaling of infrastructure resources for the training phase to cut down long training times and for tools to easily run jobs in a distributed cluster environment. There is a need for agile workload management, especially the ability to run jobs more efficiently to maximize resource utilization for performance and cost efficiency, and to visualize and stop jobs if they are not converging.

AI applications push the limits on thousands of GPU cores or thousands of CPU servers. AI and DL require a new class of accelerated infrastructure primarily based on GPUs. For the linear math computations needed for training neural network models, a single system configured with GPUs is significantly more powerful than a cluster of nonaccelerated systems. However, not all AI deployments are the same. Organizations should explore heterogenous processing architectures (e.g., GPUs, FPGAs, ASICs, or Manycore processors) based on the performance, operating environment, required skill sets, costs, and energy demand for their AI deployments.


We also know that parallel compute demands parallel storage. While the training phase requires large data stores, inferencing has less need for it. The inference models are often stored in a repository where they benefit from ultra-low-latency access. While the training phase is set once the execution model has been developed based on the data and the workload has moved to the inferencing stage, re-training of the model is often needed as new or modified data comes to light.

Accelerate and Operationalize AI Deployments Using AI-Optimized Infrastructure real-time nature of the application may require near constant re-training and updating of the model. Organizations also may benefit from re-training the model over time due to additional data sources and insights in play. If data doesn't flow smoothly through the pipeline, productivity will be compromised and organizations will need to commit increasing effort and resources to manage the pipeline.

Data architects and engineers are challenged to ensure the agility, flexibility, scale, performance, security, and compliance requirements of AI workloads while keeping the costs in check. Enterprises clearly cannot support a cutting-edge tool like AI on legacy infrastructure that is challenged to meet the required needs for scale, elasticity, compute power, performance, and data management. Organizations are now using different infrastructure solutions and approaches to support the data pipeline for AI, a process that generally leads to data silos. Some create duplicate copies of the data for the pipeline so to not disturb the stable applications. Instead, organizations need to adopt infrastructure that is dynamically adaptable, scalable, and intelligent (self-configurable, self-optimizing, and self-healing). Such an infrastructure is tuned for varied data formats and access, and it can process and analyze large volumes of data. It also possesses the speed to support faster compute calculations and decision making, manage risks, and reduce the overall costs of AI deployments.

Enterprise Readiness Businesses are always concerned about the impact of bringing emerging technologies and frameworks into an enterprise setting.

Most of the AI/ML/DL frameworks, tool kits, and applications available do not implement security, transferring their use to disconnected experiments and lab implementations. Also, most companies choose to invest in separate clusters to run AI, which is costly and inefficient.

The company also manages all software patches and upgrades to the systems. With this solution, organizations can simplify the development experience and reduce the time required for training the AI model.

Data transformation and preparation is a highly manual set of steps:

  • identifying and connecting to data sources, extracting to a staging server, using tools and scripts to manipulate the data (e.g., removing extraneous elements, breaking large images down to "tile" size so they will fit in GPU memory).


Security is implemented around:

  • Authentication: Support for Kerberos, SiteMinder, AD/LDAP, and OS auth is provided, as is Kerberos authentication for HDFS.  

  • Authorization: This feature allows for fine-grained access control, ACL/role-based control (RBAC), Spark binary life cycle, notebook updates, deployments, resource planning, reporting, monitoring, log retrieval, and execution.

  • Impersonation: Different tenants can define production execution users.

  • Encryption: SSL and authentication between all daemons is enabled.


Deep Learning Impact builds a DL environment with an end-to-end workflow that allows data scientists to focus on training, tuning, and deploying models into production. The build/training stage of the AI/ML/DL process is computing heavy and very iterative, and expertise in model tuning and optimization is scarce. This is the stage where an organization's gaps in DL and data science skills hurt the most.

Deep Learning Impact assist in the selection and creation of models using cognitive algorithms to suggest and optimize the hyperparameters. They also support elastic training to allow for flexible allocation of resources at and during runtime, which provides the ability to dynamically share resources, prioritize one job over another, and enable resiliency to failure. Runtime training visualization allows the data scientist to see the model's progress and provides the opportunity to stop training if it is not providing the right results, which helps in delivering more accurate neural models faster.


Infrastructure for the solution consists of the following elements:

  • Compute: Power Servers feature CPU:GPU NVLink connection, which delivers higher I/O bandwidth than x86 servers. They are also capable of supporting large amounts of systems memory.


The decision to run the AI pipeline on public cloud vs. on-premises is typically driven by data gravity, where the data currently exists or is likely to be stored. Easy access to compute resources and applications as well as the speed by which the capabilities need to be explored and deployed are also important factors.

Public cloud services provide data scientists and IT professionals with the infrastructure and tools to train AI models, experiment with new algorithms, or to learn new skills and techniques in an easy and agile way.

Look for dynamically adaptable, simple, flexible, secure, cost-efficient, and elastic infrastructure that can support high capacity along with high throughput and low latency for high performance training and inferencing experience.

Embrace intelligent infrastructure, leverage it for predictive analytics and valuable insights, then slowly phase in task automation once the trustworthiness and quality of data is established.



Image by Anders Jildén